The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.
Measurement of soil carbon is the focus of attention of present and future international conventions and agreements, related to global climate change. Past inventories and current carbon stock inventories involve different analytical methods, and methodological biases and uncertainties should be reduced to develop reliable estimates of the effects of land uses changes on total organic carbon. Furthermore, the carbon-equivalent is highly variable, and there is the need of using a specific correction factor for each location, resulting from the combination of land use, textural gradients, and sampling depth. In this context, the aims of this study were creating correction equations for the determinations through wet combustion (Walkley-Black- WB) for a Rhodic Hapludox based on the determinations made through dry combustion (CS) at different depths and management systems. The experimental design was 4 x 5 factorial with 3 replications. Treatments were: Conventional Tillage (CT); Minimum Tillage (MT); No-till with chisel plowing (NTC) and No-Till (NT). The collection depths were: 0-2.5; 2.5-5; 5-10; 10-20 and 20-40 cm. The measured carbon equivalent values ranged from 1.06 to 1.18 and were dependent on land use and soil depth. Rhodic Hapludox under different management presented the following order of carbon equivalent values: NTC < CT < NT < MT. The carbon equivalent values increased with depth. The high ratio between C-WB and C-CS (R2= 0.75, p= 0.0001) justifies the use of correction factors.
Experimental Study of CO2 Gasification of Biomethanation WasteIJERA Editor
Gasification is one of prominent thermochemical processes generally used to convert organic feedstock to combustible syngas (CO and H2). An experimental study of biomass gasification using carbon dioxide as an gasifying medium was carried out in a fixed bed gasifier. The main aim of this study was to determine the effect of temperature on the output syngas. The present study reported the results for producing syngas with CO2 as gasification agent and biomass (rice husk and bio-methanation waste) as raw material. The gasification was performed at 700-900°C respectively and CO2 flow rate was maintained at 0.5 lpm. Maximum syngas production found at high temperature (900°C). The syngas analysis showed higher hydrogen yield at higher temperatures.
Measurement of soil carbon is the focus of attention of present and future international conventions and agreements, related to global climate change. Past inventories and current carbon stock inventories involve different analytical methods, and methodological biases and uncertainties should be reduced to develop reliable estimates of the effects of land uses changes on total organic carbon. Furthermore, the carbon-equivalent is highly variable, and there is the need of using a specific correction factor for each location, resulting from the combination of land use, textural gradients, and sampling depth. In this context, the aims of this study were creating correction equations for the determinations through wet combustion (Walkley-Black- WB) for a Rhodic Hapludox based on the determinations made through dry combustion (CS) at different depths and management systems. The experimental design was 4 x 5 factorial with 3 replications. Treatments were: Conventional Tillage (CT); Minimum Tillage (MT); No-till with chisel plowing (NTC) and No-Till (NT). The collection depths were: 0-2.5; 2.5-5; 5-10; 10-20 and 20-40 cm. The measured carbon equivalent values ranged from 1.06 to 1.18 and were dependent on land use and soil depth. Rhodic Hapludox under different management presented the following order of carbon equivalent values: NTC < CT < NT < MT. The carbon equivalent values increased with depth. The high ratio between C-WB and C-CS (R2= 0.75, p= 0.0001) justifies the use of correction factors.
Experimental Study of CO2 Gasification of Biomethanation WasteIJERA Editor
Gasification is one of prominent thermochemical processes generally used to convert organic feedstock to combustible syngas (CO and H2). An experimental study of biomass gasification using carbon dioxide as an gasifying medium was carried out in a fixed bed gasifier. The main aim of this study was to determine the effect of temperature on the output syngas. The present study reported the results for producing syngas with CO2 as gasification agent and biomass (rice husk and bio-methanation waste) as raw material. The gasification was performed at 700-900°C respectively and CO2 flow rate was maintained at 0.5 lpm. Maximum syngas production found at high temperature (900°C). The syngas analysis showed higher hydrogen yield at higher temperatures.
Phytogenic or Petrogenic Hydrocarbons - Using Biomarkers for DelineationChemistry Matters Inc.
Presentation on the use of petroleum biomarkers for delineation of petroleum impacts in a high organic soil area (muskeg). Phytogenic hydrocarbons are natural compounds that are misidentified by standard analytical methodologies of high organic content soils as petroleum hydrocrabons. This artificially biases the measurements high for organic rich soils. Petroleum hydrocarbon products have distinct petrogenic biomarkers that can be used to identify if a sample contains petroleum or not. These biomarkers were used in this presentation to determine where petroleum impacts in the soil end and limit the unnecessary excavation of a muskeg chasing samples that were above guidelines due to the presence of natural hydrocarbons. Presentation shows how environmental forensics and petroleum forensics investigations can be used in an environmental site assessment.
Study on Quality of Soil: Part-II. Simultaneous Determination of Cu, Pb, Cd, ...BRNSS Publication Hub
The analytical procedure has been developed for simultaneous determination of the toxic trace metals Cu, Pb, Cd, and Znin soil samples from Bhusawal, employing electrochemical techniques, namely square wave voltammetry, differential pulse polarographic, and anodic stripping voltammetry (DP-ASV) techniques at hanging mercury drop electrode. The soil samples were collected from Bhusawal area, at five points, in March 2016. The metals were made free from any interference, and the applicability of the method has been proved by the analysis of soil samples from polluted and non-polluted area. Accuracy is verified by employing atomic absorption spectrometry. Simultaneous determination by the polarographic and voltammetric method for studied four metals and calculation of concentration level of each metal in the collected samples from the selected area was studied. The results and conclusions were discussed.
Co gasification of coal and biomass – thermodynamic and experimental studyeSAT Journals
Abstract Cogasification of coal and biomass is a new area of research. Cogasification offers several advantages than individual feed gasification. A thermodynamic analysis of lignite coal and rice husk cogasification using only steam was studied by using HSC chemistry software in this paper involving the effect of temperature 500-1200°C and GaCR ratio(1-3) on the product gas composition. The study also focused on calculation of thermoneutral conditions and hundred percent carbon conversion temperature in cogasification of lignite coal and rice husk. Experimental study of co gasification of rice husk and coal was also done at fixed steam to carbon ratio. The experimental study was found to be more kinetically controlled.
Keyword: cogasification, rice husk, lignite coal, HSC chemistry software, fixed bed.
Plenary talk at ISPAC conference on the use of polycyclic aromatic hydrocarbons (PAHs) in environmental forensics. Covers basics of what enviromental forensics investigations (EFIs) are and how PAHs can be used to help determine sources of releases (creosote, railway ties), oil sands development and oil spill releases (Macondo oil spill, gulf oil spill).
Study of abiotic factors across the brahmaputra belt in relation to its suita...eSAT Journals
Abstract
A healthy ecosystem is a result of balanced interaction between biotic and abiotic factors. Water temperature, pH, DO, FCO2, TA, TH etc are the most important abiotic factors influencing the physico-chemical and biological events of water body (Rahman et al., 2008). All species have their own optimal range for these abiotic parameters. In relation to aquatic life, there maturation time is also dependent on these parameters. These factors have great influence on aquatic life (DuttaMunshi and DuttaMunshi, 1995). This paper deals with the observation of fluctuation of these abiotic factors across the Brahmaputra Belt and its relation with aquatic life, mostly fishes.
Keywords: Water temperature, pH, DO, FCO2, TA, TH etc…
Greenhouse Gas Emissions From Land Applied Swine Manure: Development of Metho...LPE Learning Center
For more: http://www.extension.org/67579 A new method was used at the Ag 450 Farm Iowa State University (41.98N, 93.65W) from October 24, 2012 through December 14, 2012 to assess GHG emission from land-applied swine manure on crop land. Gas samples were collected daily from four static flux chambers. Gas method detection limits were 1.99 ppm, 170 ppb, and 20.7 ppb for CO2, CH4 and N2O, respectively. Measured gas concentrations were used to estimate flux using four different models, i.e., (1) linear regression, (2) non-linear regression, (3) non-equilibrium, and (4) revised Hutchinson & Mosier (HMR). Sixteen days of baseline measurements (before manure application) were followed by manure application with deep injection (at 41.2 m3/ha), and thirty seven days of measurements after manure application.
This presentation summarizes the findings of an air emissions and odour sampling program conducted on the Baytex Reno Field. The data was collected in response to local resident complaints of odours in the area. The study collected samples using industry standard procedures and analyzed by state of the art analytical equipment. The results showed that no human health effects were exceeded and that no odour thresholds were exceeded. This study exemplifies how odours may be detected even though the standard analytical practices are not able to measure the odiferous compounds. PAHs were measured in the study and show a petrogenic ligher signature present the ambient air in the region as well as diesel markers from the trucking activity. This summary report was presented on January 22, 2014 to the Peace River AER Public Proceeding (1769924).
Effects of anion on the corrosion behaviors of carbon steel under artificial ...eSAT Journals
Abstract
Rain is one of the main importance issues for atmospheric corrosion problem. Effects of rainfall on corrosion behaviors of carbon steels were investigated using artificial rainfall equipment. Three types of Atmospheric Corrosion Monitoring (ACM) sensors, which consist of Fe-Ag, Zn-Ag, and Al-Ag galvanic couples, were used to illustrate the correlation between the sensors output, Corrosion Rate (CR), and chemical concentration in the rain. The effects of ionic species on the corrosion behaviors were observed by using NaCl, KCl, Na2SO4, NaNO3, and KNO3 as rainfall solutions. The result revealed that the rainfall rate was insensitive to ACM sensors outputs and CRs. In contrast, the chemical species and their concentrations in the rainfall solution significantly affected the ACM outputs and CRs. The corrosivity of the cations (Na+ and K+) is negligible compared to the anions (Cl-, SO42-, NO3-).For a given number of molar concentration, the CRs resulted from the corrosivity of SO4-2anions were higher than that of Cl- and NO3- anions, respectively. According to the empirical data, the CRs is increased and then reach a steady state as the molar concentration is continuously increased. This research also indicates that the ACM sensors outputs of Fe-Ag and Zn-Ag couples are capable of estimating corrosivity of the atmosphere, while the ACM sensor of Al-Agcouple can be used to determine not only the time of wetness but also the typeofchemical species in the environment. The research methods discussed in this paper proves that the CRs are dependent on the atmospheric composition and can be forecasted through ACM sensors.
Gasification of solid refuse fuel in a fixed bed reactorMd Tanvir Alam
The global energy demand is increasing rapidly with increasing human population, urbanization and modernization [1]. The world heavily relies on fossil fuel to meet its energy demand; in order to reduce the dependency on fossil fuels, diversify the use of new and alternative fuels and to secure energy production routes, energy production from waste is inevitable [2]. Solid refuse fuel (SRF) is a well-known alternative fuel produced from the combustibles in MSW [3]. In this study an effort was endeavored to gasify SRF in a fixed bed reactor at various equivalence ratio (ER) to find out the optimum condition.
Temporal trends of spatial correlation within the PM10 time series of the Air...Florencia Parravicini
We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM10) over a ten years time frame.
Presentation given by Enzo Mangano of the University of Edinburgh on "Adsorption Materials and Processes for Carbon Capture from Gas-Fired Power Plants – AMPGas" at the UKCCSRC Gas CCS Meeting, University of Sussex, 25 June 2014
Presentation given by Dr Hao Liu from University of Nottingham on "CO2 capture from NGCC Flue Gas and Ambient Air Using PEI-Silica Adsorbent" in the Capture Technical Session on Solid Adsorption at the UKCCSRC Biannual Meeting - CCS in the Bigger Picture - held in Cambridge on 2-3 April 2014
An ecological assessment of food waste composting using a hybrid life cycle a...Ramy Salemdeeb
A conference paper published at the 8th Conference of the International Society for Industrial Ecology, At University of Surrey, Guildford, UK, At Surrey
Phytogenic or Petrogenic Hydrocarbons - Using Biomarkers for DelineationChemistry Matters Inc.
Presentation on the use of petroleum biomarkers for delineation of petroleum impacts in a high organic soil area (muskeg). Phytogenic hydrocarbons are natural compounds that are misidentified by standard analytical methodologies of high organic content soils as petroleum hydrocrabons. This artificially biases the measurements high for organic rich soils. Petroleum hydrocarbon products have distinct petrogenic biomarkers that can be used to identify if a sample contains petroleum or not. These biomarkers were used in this presentation to determine where petroleum impacts in the soil end and limit the unnecessary excavation of a muskeg chasing samples that were above guidelines due to the presence of natural hydrocarbons. Presentation shows how environmental forensics and petroleum forensics investigations can be used in an environmental site assessment.
Study on Quality of Soil: Part-II. Simultaneous Determination of Cu, Pb, Cd, ...BRNSS Publication Hub
The analytical procedure has been developed for simultaneous determination of the toxic trace metals Cu, Pb, Cd, and Znin soil samples from Bhusawal, employing electrochemical techniques, namely square wave voltammetry, differential pulse polarographic, and anodic stripping voltammetry (DP-ASV) techniques at hanging mercury drop electrode. The soil samples were collected from Bhusawal area, at five points, in March 2016. The metals were made free from any interference, and the applicability of the method has been proved by the analysis of soil samples from polluted and non-polluted area. Accuracy is verified by employing atomic absorption spectrometry. Simultaneous determination by the polarographic and voltammetric method for studied four metals and calculation of concentration level of each metal in the collected samples from the selected area was studied. The results and conclusions were discussed.
Co gasification of coal and biomass – thermodynamic and experimental studyeSAT Journals
Abstract Cogasification of coal and biomass is a new area of research. Cogasification offers several advantages than individual feed gasification. A thermodynamic analysis of lignite coal and rice husk cogasification using only steam was studied by using HSC chemistry software in this paper involving the effect of temperature 500-1200°C and GaCR ratio(1-3) on the product gas composition. The study also focused on calculation of thermoneutral conditions and hundred percent carbon conversion temperature in cogasification of lignite coal and rice husk. Experimental study of co gasification of rice husk and coal was also done at fixed steam to carbon ratio. The experimental study was found to be more kinetically controlled.
Keyword: cogasification, rice husk, lignite coal, HSC chemistry software, fixed bed.
Plenary talk at ISPAC conference on the use of polycyclic aromatic hydrocarbons (PAHs) in environmental forensics. Covers basics of what enviromental forensics investigations (EFIs) are and how PAHs can be used to help determine sources of releases (creosote, railway ties), oil sands development and oil spill releases (Macondo oil spill, gulf oil spill).
Study of abiotic factors across the brahmaputra belt in relation to its suita...eSAT Journals
Abstract
A healthy ecosystem is a result of balanced interaction between biotic and abiotic factors. Water temperature, pH, DO, FCO2, TA, TH etc are the most important abiotic factors influencing the physico-chemical and biological events of water body (Rahman et al., 2008). All species have their own optimal range for these abiotic parameters. In relation to aquatic life, there maturation time is also dependent on these parameters. These factors have great influence on aquatic life (DuttaMunshi and DuttaMunshi, 1995). This paper deals with the observation of fluctuation of these abiotic factors across the Brahmaputra Belt and its relation with aquatic life, mostly fishes.
Keywords: Water temperature, pH, DO, FCO2, TA, TH etc…
Greenhouse Gas Emissions From Land Applied Swine Manure: Development of Metho...LPE Learning Center
For more: http://www.extension.org/67579 A new method was used at the Ag 450 Farm Iowa State University (41.98N, 93.65W) from October 24, 2012 through December 14, 2012 to assess GHG emission from land-applied swine manure on crop land. Gas samples were collected daily from four static flux chambers. Gas method detection limits were 1.99 ppm, 170 ppb, and 20.7 ppb for CO2, CH4 and N2O, respectively. Measured gas concentrations were used to estimate flux using four different models, i.e., (1) linear regression, (2) non-linear regression, (3) non-equilibrium, and (4) revised Hutchinson & Mosier (HMR). Sixteen days of baseline measurements (before manure application) were followed by manure application with deep injection (at 41.2 m3/ha), and thirty seven days of measurements after manure application.
This presentation summarizes the findings of an air emissions and odour sampling program conducted on the Baytex Reno Field. The data was collected in response to local resident complaints of odours in the area. The study collected samples using industry standard procedures and analyzed by state of the art analytical equipment. The results showed that no human health effects were exceeded and that no odour thresholds were exceeded. This study exemplifies how odours may be detected even though the standard analytical practices are not able to measure the odiferous compounds. PAHs were measured in the study and show a petrogenic ligher signature present the ambient air in the region as well as diesel markers from the trucking activity. This summary report was presented on January 22, 2014 to the Peace River AER Public Proceeding (1769924).
Effects of anion on the corrosion behaviors of carbon steel under artificial ...eSAT Journals
Abstract
Rain is one of the main importance issues for atmospheric corrosion problem. Effects of rainfall on corrosion behaviors of carbon steels were investigated using artificial rainfall equipment. Three types of Atmospheric Corrosion Monitoring (ACM) sensors, which consist of Fe-Ag, Zn-Ag, and Al-Ag galvanic couples, were used to illustrate the correlation between the sensors output, Corrosion Rate (CR), and chemical concentration in the rain. The effects of ionic species on the corrosion behaviors were observed by using NaCl, KCl, Na2SO4, NaNO3, and KNO3 as rainfall solutions. The result revealed that the rainfall rate was insensitive to ACM sensors outputs and CRs. In contrast, the chemical species and their concentrations in the rainfall solution significantly affected the ACM outputs and CRs. The corrosivity of the cations (Na+ and K+) is negligible compared to the anions (Cl-, SO42-, NO3-).For a given number of molar concentration, the CRs resulted from the corrosivity of SO4-2anions were higher than that of Cl- and NO3- anions, respectively. According to the empirical data, the CRs is increased and then reach a steady state as the molar concentration is continuously increased. This research also indicates that the ACM sensors outputs of Fe-Ag and Zn-Ag couples are capable of estimating corrosivity of the atmosphere, while the ACM sensor of Al-Agcouple can be used to determine not only the time of wetness but also the typeofchemical species in the environment. The research methods discussed in this paper proves that the CRs are dependent on the atmospheric composition and can be forecasted through ACM sensors.
Gasification of solid refuse fuel in a fixed bed reactorMd Tanvir Alam
The global energy demand is increasing rapidly with increasing human population, urbanization and modernization [1]. The world heavily relies on fossil fuel to meet its energy demand; in order to reduce the dependency on fossil fuels, diversify the use of new and alternative fuels and to secure energy production routes, energy production from waste is inevitable [2]. Solid refuse fuel (SRF) is a well-known alternative fuel produced from the combustibles in MSW [3]. In this study an effort was endeavored to gasify SRF in a fixed bed reactor at various equivalence ratio (ER) to find out the optimum condition.
Temporal trends of spatial correlation within the PM10 time series of the Air...Florencia Parravicini
We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM10) over a ten years time frame.
Presentation given by Enzo Mangano of the University of Edinburgh on "Adsorption Materials and Processes for Carbon Capture from Gas-Fired Power Plants – AMPGas" at the UKCCSRC Gas CCS Meeting, University of Sussex, 25 June 2014
Presentation given by Dr Hao Liu from University of Nottingham on "CO2 capture from NGCC Flue Gas and Ambient Air Using PEI-Silica Adsorbent" in the Capture Technical Session on Solid Adsorption at the UKCCSRC Biannual Meeting - CCS in the Bigger Picture - held in Cambridge on 2-3 April 2014
An ecological assessment of food waste composting using a hybrid life cycle a...Ramy Salemdeeb
A conference paper published at the 8th Conference of the International Society for Industrial Ecology, At University of Surrey, Guildford, UK, At Surrey
Life Cycle Assessment of Wastewater Treatment Plant.pptxAbdulSameeu3
Life cycle assessment (LCA) has been proved to act as a desirable tool to evaluate the environmental
impacts of wastewater treatment plants (WWTPs). However, the application of LCA methodology in the
field of wastewater treatment is still in progress. This ppt has made a review of the LCA studies dealing
with biological (activated sludge) WWTPs, with the aim to provide qualitative interpretation of the
associated environmental impact categories: eutrophication potential, global warming potential,
toxicity-related impacts, energy balance, water use, land use and other impact categories.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Evaluation of a Continuously-Mixed Farm-Based Anaerobic Co-Digestion SystemLPE Learning Center
Full proceedings at: http://www.extension.org/72776 New York State’s largest manure-based anaerobic co-digestion facility was evaluated continuously for a 2-year period following the U.S. EPA Protocol for quantifying and reporting on the performance of anaerobic digestion systems for livestock manures. Overall, we assessed and determined the system’s performance with respect to the: 1) conversion of biomass to biogas, 2) conversion of biogas to useful energy, and 3) system’s economics. The information developed by this project can be used to compare performance information developed from other manure-based anaerobic digestion systems.
A short description of thermal technologies for the recovery of ammonia from N-rich wastewaters and expirementing with membrane distillation for getting better results.
Optimizing the Reverse Osmosis Process Parameters by Maximizing Recovery by T...QUESTJOURNAL
ABSTRACT: In this study, the effects of Operating Pressure, Potential Hydrogen, Oxidation Reduction Potential and Anti Scaling Agent on multi responses like Permeate, COD, Total Solids, Conductivity and Hardness in the Reverse Osmosis Process were experimentally investigated on RO 8100 ST8 PT44 400Wl machine. The settings of RO parameters were determined by using Taguchi’s experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the RO parameters. Results show that potential of hydrogen, operating pressure, oxidation reduction potential and anti scaling agent are the four Parameters that influence the Permit more effectively and COD, Total Solids, Conductivity and Hardness respectively. Improvement in recovery of RO process is achieved with optimize setting. Finally, the ranges for best RO conditions are proposed for ZLD process.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
How Can CCU Provide a Net Benefit? - presentation by Peter Styring in the Emissions through the CCS Lifecycle session at the UKCCSRC Cardiff Biannual Meeting, 10-11 September 2014
The Role of Carbon Capture Storage (CCS) and Carbon Capture Utilization (CCU)...Ofori Kwabena
The role of Carbon Capture and Storage & Carbon Capture and Utilization-
Capturing carbon dioxide and storing (CCS) is a climate change mitigation technology which is aimed at reducing CO2 emissions. The utilization of CO2 (CCU) in the manufacture of commercial products is also a technology used to complement CCS technology.
This paper presents a literature review on the mechanisms, developments, cost analysis, life cycle environmental impacts, challenges and policy options that are associated with these technologies.
Similar to Fuzzy logic for plant-wide control of biological wastewater treatment process including greenhouse gas emissions (20)
An optimal general type-2 fuzzy controller for Urban Traffic NetworkISA Interchange
Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the traffic information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the modified backtracking search algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
State of charge estimation of lithium-ion batteries using fractional order sl...ISA Interchange
This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov’s stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estima- tion method is effective, and the designed observers have good performance.
Fractional order PID for tracking control of a parallel robotic manipulator t...ISA Interchange
This paper presents the tracking control for a robotic manipulator type delta employing fractional order PID controllers with computed torque control strategy. It is contrasted with an integer order PID controller with computed torque control strategy. The mechanical structure, kinematics and dynamic models of the delta robot are descripted. A SOLIDWORKS/MSC-ADAMS/MATLAB co-simulation model of the delta robot is built and employed for the stages of identification, design, and validation of control strategies. Identification of the dynamic model of the robot is performed using the least squares algorithm. A linearized model of the robotic system is obtained employing the computed torque control strategy resulting in a decoupled double integrating system. From the linearized model of the delta robot, fractional order PID and integer order PID controllers are designed, analyzing the dynamical behavior for many evaluation trajectories. Controllers robustness is evaluated against external disturbances employing performance indexes for the joint and spatial error, applied torque in the joints and trajectory tracking. Results show that fractional order PID with the computed torque control strategy has a robust performance and active disturbance rejection when it is applied to parallel robotic manipulators on tracking tasks.
Design and implementation of a control structure for quality products in a cr...ISA Interchange
In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen HYSYS dynamic environment under real operating conditions. The simulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns.
Model based PI power system stabilizer design for damping low frequency oscil...ISA Interchange
This paper explores a two-level control strategy by blending a local controller with a centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favorably compared with some controllers prevalent in the literature.
A comparison of a novel robust decentralized control strategy and MPC for ind...ISA Interchange
Abstract: In this work we have developed a novel, robust practical control structure to regulate an industrial methanol distillation column. This proposed control scheme is based on a override control framework and can manage a non-key trace ethanol product impurity specification while maintaining high product recovery. For comparison purposes, an MPC with a discrete process model (based on step tests) was also developed and tested. The results from process disturbance testing shows that, both the MPC and the proposed controller were capable of maintaining both the trace level ethanol specification in the distillate (XD) and high product recovery (β). Closer analysis revealed that the MPC controller has a tighter XD control, while the proposed controller was tighter in β control. The tight XD control allowed the MPC to operate at a higher XD set point (closer to the 10 ppm AA grade methanol standard), allowing for savings in energy usage. Despite the energy savings of the MPC, the proposed control scheme has lower installation and running costs. An economic analysis revealed a multitude of other external economic and plant design factors, that should be considered when making a decision between the two controllers. In general, we found relatively high energy costs favor MPC.
Fault detection of feed water treatment process using PCA-WD with parameter o...ISA Interchange
Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA- WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000 MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an auto- matic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results.
Model-based adaptive sliding mode control of the subcritical boiler-turbine s...ISA Interchange
As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.
A Proportional Integral Estimator-Based Clock Synchronization Protocol for Wi...ISA Interchange
Clock synchronization is an issue of vital importance in applications of wireless sensor networks (WSNs). This paper proposes a proportional integral estimator-based protocol (EBP) to achieve clock synchronization for wireless sensor networks. As each local clock skew gradually drifts, synchronization accuracy will decline over time. Compared with existing consensus-based approaches, the proposed synchronization protocol improves synchronization accuracy under time-varying clock skews. Moreover, by restricting synchronization error of clock skew into a relative small quantity, it could reduce periodic re-synchronization frequencies. At last, a pseudo-synchronous implementation for skew compensation is introduced as synchronous protocol is unrealistic in practice. Numerical simulations are shown to illustrate the performance of the proposed protocol.
An artificial intelligence based improved classification of two-phase flow patte...ISA Interchange
Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are re- corded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows.
New Method for Tuning PID Controllers Using a Symmetric Send-On-Delta Samplin...ISA Interchange
In this paper we present a new method for tuning PI controllers with symmetric send-on-delta (SSOD) sampling strategy. First we analyze the conditions that produce oscillations in event based systems considering SSOD sampling strategy. The Describing Function is the tool used to address the problem. Once the conditions for oscillations are established, a new robustness to oscillation performance measure is introduced which entails with the concept of phase margin, one of the most traditional measures of relative stability in closed-loop control systems. Therefore, the application of the proposed robustness measure is easy and intuitive. The method is tested by both simulations and experiments. Additionally, a Java application has been developed to aid in the design according to the results presented in the paper.
Load estimator-based hybrid controller design for two-interleaved boost conve...ISA Interchange
This paper is devoted to the development of a hybrid controller for a two-interleaved boost converter dedicated to renewable energy and automotive applications. The control requirements, resumed in fast transient and low input current ripple, are formulated as a problem of fast stabilization of a predefined optimal limit cycle, and solved using hybrid automaton formalism. In addition, a real time estimation of the load is developed using an algebraic approach for online adjustment of the hybrid controller. Mathematical proofs are provided with simulations to illustrate the effectiveness and the robustness of the proposed controller despite different disturbances. Furthermore, a fuel cell system supplying a resistive load through a two-interleaved boost converter is also highlighted.
Effects of Wireless Packet Loss in Industrial Process Control SystemsISA Interchange
Timely and reliable sensing and actuation control are essential in networked control. This depends on not only the precision/quality of the sensors and actuators used but also on how well the communications links between the field instruments and the controller have been designed. Wireless networking offers simple deployment, reconfigurability, scalability, and reduced operational expenditure, and is easier to upgrade than wired solutions. However, the adoption of wireless networking has been slow in industrial process control due to the stochastic and less than 100% reliable nature of wireless communications and lack of a model to evaluate the effects of such communications imperfections on the overall control performance. In this paper, we study how control performance is affected by wireless link quality, which in turn is adversely affected by severe propagation loss in harsh industrial environments, co-channel interference, and unintended interference from other devices. We select the Tennessee Eastman Challenge Model (TE) for our study. A decentralized process control system, first proposed by N. Ricker, is adopted that employs 41 sensors and 12 actuators to manage the production process in the TE plant. We consider the scenario where wireless links are used to periodically transmit essential sensor measurement data, such as pressure, temperature and chemical composition to the controller as well as control commands to manipulate the actuators according to predetermined setpoints. We consider two models for packet loss in the wireless links, namely, an independent and identically distributed (IID) packet loss model and the two-state Gilbert-Elliot (GE) channel model. While the former is a random loss model, the latter can model bursty losses. With each channel model, the performance of the simulated decentralized controller using wireless links is compared with the one using wired links providing instant and 100% reliable communications. The sensitivity of the controller to the burstiness of packet loss is also characterized in different process stages. The performance results indicate that wireless links with redundant bandwidth reservation can meet the requirements of the TE process model under normal operational conditions. When disturbances are introduced in the TE plant model, wireless packet loss during transitions between process stages need further protection in severely impaired links. Techniques such as re-transmission scheduling, multi-path routing and enhanced physical layer design are discussed and the latest industrial wireless protocols are compared.
Fault Detection in the Distillation Column ProcessISA Interchange
Chemical plants are complex large-scale systems which need designing robust fault detection schemes to ensure high product quality, reliability and safety under different operating conditions. The present paper is concerned with a feasibility study of the application of the black-box modeling method and Kullback Leibler divergence (KLD) to the fault detection in a distillation column process. A Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) polynomial model is firstly developed to estimate the nonlinear behavior of the plant. Furthermore, the KLD is applied to detect abnormal modes. The proposed FD method is implemented and validated experimentally using realistic faults of a distillation plant of laboratory scale. The experimental results clearly demonstrate the fact that proposed method is effective and gives early alarm to operators.
Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank SystemISA Interchange
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H1 framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
A KPI-based process monitoring and fault detection framework for large-scale ...ISA Interchange
Large-scale processes, consisting of multiple interconnected sub-processes, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel re- presentation of each sub-process, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods.
An adaptive PID like controller using mix locally recurrent neural network fo...ISA Interchange
Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional integral derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initi- alized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on- line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.
A method to remove chattering alarms using median filtersISA Interchange
Chattering alarms are the most found nuisance alarms that will probably reduce the usability and result in a confidence crisis of alarm systems for industrial plants. This paper addresses the chattering alarm reduction using median filters. Two rules are formulated to design the window size of median filters. If the alarm probability is estimated using process data, one rule is based on the probability of alarms to satisfy some requirements on the false alarm rate, or missed alarm rate. If there are only historical alarm data available, the other rule is based on percentage reduction of chattering alarms using alarm duration distribution. Experimental results for industrial cases testify that the proposed method is effective.
Design of a new PID controller using predictive functional control optimizati...ISA Interchange
An improved proportional integral derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. I. Santín et al. / ISA Transactions 77 (2018)146–166 147
List of abbreviations
AE Aeration Energy (kWh/d)
AOB Ammonia Oxidizing Bacteria
ASM1 Activated Sludge Model no. 1
BOD5 5-day Biological Oxygen Demand (mg/l)
Qa Internal recycle flow rate (m3
/d)
qEC External carbon flow rate (m3
/d)
Qin Influent flow rate (m3
/d)
qEC,1 External carbon flow rate in the first tank (m3
/d)
Qw Wastage flow rate (m3
/d)
Qst Flow rate from the storage tank (m3
/d)
BSM1 Benchmark Simulation Model no 1 SNtot Total nitrogen concentration (mg/l)
BSM2 Benchmark Simulation Model no 2
BSM2G Benchmark Simulation Model no 2 Gas
CO2 Carbon dioxide (kg/d)
COD Chemical Oxygen Demand (mg/l)
CODt total Chemical Oxygen Demand (mg/l)
DCS Default COntrol Strategy
EC External Carbon (kg/d)
EQI Effl uent Quality Index (kg of pollutants/d)
GHG Greenhouse gases
HEnet Net Heating Energy (kWh/d)
SNtot,e
Total nitrogen concentration in the effl uent (mg/l)
SNH Ammonium and ammonia nitrogen concentration
(mg/l)
SNH,in Ammonium and ammonia nitrogen concentration at
the input of the primary clarifier (mg/l)
SNH,i Ammonium and ammonia nitrogen concentration in
tank i (mg/l)
SNH,e Ammonium and ammonia nitrogen concentration in
the effl uent (mg/l)
SNO Nitric Oxide concentration (mg/l)
HRT Hydraulic Retention Time (s)
KL a Oxygen transfer coeffi cient (d−1
)
KLai Oxygen transfer coeffi cient in tank i (d−1
)
ME Mixing Energy (kWh/d)
METprod Methane production in the anaerobic digester (kg/d)
N2O Nitrous oxide (kg equivalent CO2/d)
SNO2
SNO3
SNO3 i
SNKj
SN2O
Nitrite concentration (mg/l)
Nitrate concentration (mg/l)
Nitrate concentration in tank i (mg/l)
Kjeldahl nitrogen (mg/l)
Dissolved nitrous oxide concentration (mg/l)
SO Dissolved oxygen concentration (mg/l)
N2 dinitrogen CO2/d)
OCI Overall Cost Index
SO,i Dissolved oxygen concentration in tank i (mg/l)
SP Sludge Production (kg/d)
PE Pumping Energy (kWh/d)
PI Proportional-Integral
Tas Temperature (◦C)
Q Flow rate (m3
/d)
TSS Total Suspended Solids (mg/l)
WWTP Wastewater Treatment Plants
bacteria (AOB) denitrification pathway for N2O emissions based on
Guo and Vanrolleghem [12]. In addition, BSM2G is the result of the
evolution of previous benchmarks. First, the Benchmark Simulation
Model no 1 (BSM1) was developed in Copp [13], which includes
the biological treatment and a secondary clarifier, using one-week
period to evaluate results. Next, the Benchmark Simulation Model no
2 (BSM2) (Gernaey et al. [14]) included the whole cycle of a WWTP,
adding the sludge treatment and a primary clarifier, applying a more
complete influent with a one-year period for evaluation. BSM2G dif-
fers from BSM2 mainly in the inclusion of GHG emissions assess-
ment. It should be noted that the use of models for the evaluation of
GHG emissions is currently restricted to the research domain, due to
the incomplete knowledge regarding the SNO2
production pathways
(Mannina et al. [15], Ni and Yuan [16]).
Although the present work uses Proportional-Integral (PI) con-
trollers, the main contribution is based on a fuzzy controller to cope
with the mentioned problems in WWTPs. There are already many
works in the literature that have applied fuzzy control strategies
in WWTPs. For example, the fuzzy controller was applied for the
basic control loop of the dissolved oxygen concentration (SO) in the
fifth reactor (SO,5) by using BSM1 in Belchior et al. [17] and Nasr
et al. [18] or in a pilot plant in Traore et al. [19]. In the case of
Santín et al. [20] and Meyer and Pöpel [21], the fuzzy controller is
used for ammonium and ammonia nitrogen concentration (SNH) in
the fifth tank (SNH,5) cascade control by manipulating the SO,5 set-
point, also by using BSM1 as testing plant. The fuzzy inference sys-
tem is employed in Pai et al. [22] to improve artificial neural net-
work to predict the total suspended solids (TSS) and the chemi-
cal oxygen demand (COD) in the effl uent from a hospital WWTP.
By using BSM2 as a working scenario, Santín et al. [23] Santín et
al. [24] apply fuzzy control to deal with pollutants limits viola-
tions. Fuzzy logic has also been applied for evaluation (Kalavrouzi-
otis et al. [25]) or management (Hirsch et al. [26]) of real WWTPs.
However, none of the referred papers have taken into account GHG
emissions.
Although there is a large number of works that apply control
strategies in WWTPs, the evaluation of GHG emissions has emerged
in recent years. Some works that analyze GHG emissions in WWTPs
by applying control strategies are Flores-Alsina et al. [7,8] and Barbu
et al. [27]. They use BSM2G, but with different model versions.
Flores-Alsina et al. [7] tests the effect of traditional control strate-
gies in GHG emissions, but without considering those produced by
nitrification. Flores-Alsina et al. [8] shows the effect on GHG emis-
sions of the different areas of a WWTP. Barbu et al. [27] presents the
effects of other traditional control strategies on water quality, opera-
tional costs and, especially, on GHG emissions, by an integral indica-
tor for performance evaluation. However, it was not the goal of these
works to implement specific control strategies in order to reduce N2O
emission in the nitrification process. On the other hand, Santín et
al. [28] reduce N2O emissions combining cascade SNO2
control and
cascade SNH,5 control. Boiocchi et al. [29] reduce N2O emissions with
a fuzzy controller that manipulates the oxygen transfer coeffi cient
(KLa) of the aerobic reactors based on SNH and the nitrate concen-
tration (SNO3
) in the input and in the output of the nitrification pro-
cess. In addition, Boiocchi et al. [29] take into account the effect that
the oxygen aeration can produce on effl uent costs and quality. Santín
et al. [28] combine two control strategies with PI controllers to also
reduce costs and improve the effl uent quality, but without attempt-
ing to eliminate nutrient violations. Both articles only manipulate
KLa of the aerobic reactors and only reduce SNO2
as GHG emissions.
The present article reduces the SNO2
emissions using a different
control strategy than the two referred articles, by means of only
3. 148 I. Santín et al. / ISA Transactions 77 (2018)146–166
Fig. 1. BSM2 plant with notation used for flow rates.
SNH sensors, which are commonly used in real plants. The proposed
controller not only manipulates SO in the third reactor (SO,3), SO in
the fourth reactor (SO,4) and the SO,5 set-points, but also the inter-
nal recirculation flow rate (Qa), the external carbon flow rate (qEC)
in the first reactor (qEC,1) and the flow rate from the storage tank
(Qst). This fact also allows the proposed paper to differentiate itself
from the mentioned referenced articles, by reducing the CO2 emis-
sions due to the endogenous respiration of biomass, CO2 generated
from the external carbon source production and CO2 due to the elec-
tric consumption, and by removing the limit violations of the nutri-
ents. The proposed control strategies also differ from the literature,
achieving these objectives (together with the reduction of costs) by
the implementation of a single fuzzy controller, which to the best
of the authors knowledge has not been previously proposed. Also, it
has to be emphasized the addition of the derivatives of some vari-
ables with respect to time as fuzzy controller inputs, in order to
act in advance. In addition, the temperature is also considered as a
fuzzy controller input, since higher temperature means higher GHG
emissions (Boiocchi et al. [29]) and lower temperature means more
total nitrogen concentration (SNtot
) and SNH are generated. The article
explains the contribution of each manipulated variable in the pro-
posed objectives, as well as the effects of each of the fuzzy controller
al. [14]) that includes GHG emissions. The model was presented in
Flores-Alsina et al. [7] and an updated version provided by the same
authors has been used for the present article. Within these modifica-
tions, it is included the incorporation of the AOB denitrification path-
way for N2O emissions based on Guo and Vanrolleghem [12]. There-
fore, the present BSM2G includes two pathways for N2O emissions
(heterotrophic denitrification and AOB denitrification).
2.1. Layout
The BSM2G layout (Fig. 1) is designed for an influent with an aver-
age flow rate of 20,648.36 m3
/d and an average biodegradable COD of
592.53 mg/l. In the same way as in the case of BSM2, BSM2G is made
up of a primary clarifier, a secondary treatment and a sludge treat-
ment. The secondary treatment includes the biological reactors and
a secondary settler.
For the biological treatment, BSM2G includes, as in BSM1 (Copp
[13]) and BSM2, five biological reactors, two of which are anoxic
and three are aerobic. The biological reactions inside the reactors are
modeled by the Activated Sludge Model no 1 (ASM1) (Henze et al.
[30]). In BSM2G, ASM1 is extended on the basis of Hiatt and Grady
[31] and Mampaey et al. [32] in order to include, besides SNO , the
input. It should be noted that the proposed fuzzy control looks for a 3
other compounds that are present in the nitrification and deni tri-
trade-off between the aforementioned objectives, achieving satisfac- fication processes: SNO , nitric oxide (SNO), N2O and dinitrogen (N2).
tory results and without meaningfully worsening any of them. 2There is an internal r tion from the last aerobic reactor to feed
The paper is organized as follows. First, BSM2G working scenario
is presented. Next, the default and the proposed control strategies
are explained. Afterwards, simulations results are shown, together
with the discussion about them. Finally, the most important conclu-
sions are drawn.
2. Materials and methods
A benchmark is used for the evaluation of the proposed con-
trol strategy, as it is a common practice in wastewater treatment
research. This is BSM2G, which is an extension of BSM2 (Gernaey et
ecircula
the first anoxic reactor with SNO3
. The secondary clarifier is mod-
eled as a 10 layers non-reactive unit. At lower levels the sludge is
deposited by gravity. Some of this sludge is recirculated to the first
anoxic reactor (external recirculation) and the other part is led to be
treated. The hydraulic retention time of the primary clarifier and the
secondary treatment is 22 h. It is based on the average dry weather
flow rate and the volume of the primary clarifier (900 m3
), the bio-
logical reactors (12,000 m3
) and the secondary settler (6000 m3
). The
volume of each of the two anoxic tanks is 1500 m3
and the volume
of each of the three aerobic tanks is 3000 m3
.
4. I. Santín et al. / ISA Transactions 77 (2018)146–166 149
Table1
Limits for the effl uent pollutants.
Variable Value
SNtot
<18 mg/l
CODt <100 mg/l
SNH <4 mg/l
TSS <30 mg/l
BOD5 <10 mg/l
For the sludge treatment, BSM2G makes use of a thickener, an
anaerobic digester and a dewatering unit. The water extracted from
the sludge by the dewatering process is recirculated to the primary
clarifier through the storage tank to regulate its flow rate.
The influent data in BSM2G includes rainfall and storm events, as
well as variations in temperature. It is defined for 609 days, but only
the results of the period from day 245 to day 609 are considered for
evaluation. With the aim to stabilize the plant, a constant influent is
applied to the plant for 200 days, before any simulation.
2.2. Evaluation criteria
The performance of the control strategies is evaluated by consid-
ering the effl uent quality, the operational costs and the GHG emis-
sions.
The evaluation criteria for the effl uent quality are the percentage
of time for which the values of the effl uent pollutants are over the
established limits and the Effl uent Quality Index (EQI). Table 1 shows
the limits established for the effl uent concentrations of SNtot
, total
COD (CODt), SNH, TSS and 5-day Biological Oxygen Demand (BOD5) In
BSM2G, SNtot
is the sum of SNO3
, SNO2
, SNO, SN2O
and Kjeldahl nitrogen
(SNKj), which includes the organic nitrogen and SNH. Within these
concentrations, this article only evaluates the violations of SNtot
in
the effl uent (SNtot,e
) and SNH in the effl uent (SNH,e). This is due to the
fact that the rest of the concentrations are commonly kept under the
established limits and they only exceed the limits in exceptional days
when the high increase of the influent flow makes the wastewater be
bypassed to the effl uent without being treated.
EQI is expressed in Kg of pollutants per day and is calculated
weighting the effl uent concentration of the different pollutants,
according to the following expression:
t=609days
treatment, the sludge treatment, the difference between electric con-
sumption and electric generation, the EC production and the sludge
to be disposed. Within these sources, with the control strategy pro-
posed in this article, those produced in the biological treatment, due
to electricity and due to EC production are attempted to be reduced
and then evaluated.
3. Control approach
The control approach proposed in this article is mainly based on
a single fuzzy controller that manipulates the variables of the water
line in a WWTP. However, although the work is focused on this fuzzy
controller, three PI controllers are also applied in order to control
SO,3, SO,4 and SO,5 by manipulating KLa in the third tank (KLa3), in
the fourth tank (KLa4) and in the fifth tank (KLa5). Each one of them
controls the SO of one reactor by manipulating the KLa of the same
reactor.
Due to the large number of inputs and outputs of the pro-
posed fuzzy controller, it has been elaborated incrementally in order
to observe the effect produced by the different manipulated and
measured variables. The fuzzy controller finally designed is called
fuzzy_plantwide and the intermediate fuzzy controllers are num-
bered from 1 to 4 (fuzzy_1, fuzzy_2, fuzzy_3 and fuzzy_4).
In order to compare the performance of the new proposals, the
default control strategy (DCS) used in BSM2G is considered the start-
ing point. The second part includes the explanations of the PI con-
trollers used at the basic level and the fuzzy controller. Fig. 2 shows
the configuration of DCS and the proposed control strategy.
For all the control strategies, ideal sensors have been considered,
as it is a common practice in the research made on benchmark sim-
ulation models.
3.1. Default control strategy (DCS)
As it is shown in Fig. 2a, the DCS closed-loop control configuration
consists of a PI controller that controls SO,4 at a set-point of 2 mg/l by
manipulating KL a3, KLa4 and KL a5 with KLa5 set to the half value of
KLa3 and KLa4. KL a values are constrained from 0 to 360 d−1
.
For other possible manipulated variables, a fixed value is applied.
qEC,1 is added at a constant flow rate of 2 m3
/d. For the rest of the
reactors there is no external carbon addition. Two different wastage
flow rate (Qw ) values are imposed depending on the time of the year:
from 0 to 180 days and from 364 to 454 days Qw is set to 300 m3
/d;
and for the remaining time periods Qw is set to 450 m3/d. Qa is fixed
EQI =
1
1000 · T ∫
t=245days
(2 · TSS(t) + COD(t) + 30 · SNKj(t)) + at 61,944 m3/d.
The tuning parameters of the PI controller implemented in DCS
are kp = 25 and Ti = 0.002. The tuning employed here is taken from
+ 10 · (SNO3
+ SNO2
+ SNO + SN2O
)(t) + 2 · BOD5(t)) · Q(t) · dt
(1)
Nopens et al. [33] in order to consider the same basic control loops
configuration as in the BSM2 default control strategy.
where T is the evaluation period and Q is the flow rate.
The operational costs are evaluated by the Operational Cost Index
(OCI). It is calculated weighting the different costs of a WWTP as fol-
lows:
OCI = AE + PE + 3 · SP + 3 · EC + ME − 6 · METprod + HEnet (2)
where AE is the aeration energy (kWh/d), PE is the pumping energy
(kWh/d), SP is the sludge production (kg/d), EC refers to the carbon
that could be added to improve denitrification (kg/d), ME is the mix-
ing energy (kWh/d), METprod is the produced methane (kWh/d) and
HE is the heating energy (kWh/d).
The GHG emissions are a new evaluation criterion of BSM2G with
respect to BSM2. They are calculated according to the principles pro-
posed by Hiatt and Grady [31] and Mampaey et al. [32]. The follow-
ing sources of GHG emissions are taken into account: the biological
3.2. Proposed control strategy
The proposed control strategy in this paper is mainly focused on
the implementation of fuzzy logic. The own authors’ experience in
WWTP control engineering and the opinions received from opera-
tors in real plants make the authors conclude that the experience
and knowledge of the plant behavior is of great importance in the
control strategies application in WWTPs. For this reason, the main
control proposed is based on a single fuzzy controller designed to
manipulate six variables of the water line based on different mea-
sured variables, as well as their time derivatives in some cases, to
know their trend over time. The fuzzy controller does not try to keep
5. 150 I. Santín et al. / ISA Transactions 77 (2018)146–166
the measured variables at a given set-point, as in Santín et al. [20].
The configuration of the full proposed control strategy is shown in
Fig. 2b.
Within the manipulated variables of the fuzzy controller, Qa, qEC,1
and Qst are directly related to the actuator. However, SO in the
aerobic reactors are controlled by PI controllers, whose set-points
are manipulated by the fuzzy controller. These PI controllers are
explained in the next section.
3.2.1. PI controllers for SO control in the aerobic reactors
PI controllers aim to maintain a variable at a given set-point
(unlike the proposed fuzzy controller) and they are mostly used in
real plants. SO in the reactors are some of the few variables that can
be maintained at a set-point in a WWTP, without a large error. There-
fore, PI controllers are proposed for this objective. For the fuzzy logic,
it is easier and more coherent to find the relationship of SNH and SNO3
with SO in the aerobic reactors, than directly with KL a of the aerobic
reactors.
The tuning of these PI controllers as well as the controlled and
manipulated variables of each one are the same with those the
default PI controller (kp = 25 and Ti = 0.002). This is because their
tracking is satisfactory enough and the implementation objectives of
this article are focused on the fuzzy controller. KLa values are also
constrained from 0 to 360 d−1
as in DCS.
3.2.2. Fuzzy logic
Fuzzy logic can be defined as a control based on human exper-
tise, determined by words instead of numbers and sentences instead
of equations. However, process variables are measured in numbers
instead of words. For this reason, the fuzzy controller adapts the
input variables into suitable linguistic values by membership func-
tions (mf). For further information about the fuzzy control, the reader
is referred to standard references such as Klir and Yuan [34].
The proposed fuzzy controller has been initially tuned based on
the knowledge of the biological processes described by the extended
ASM1 and on a specific analysis of the evolution over time of the
fuzzy controller inputs. After that, the membership functions values
range have been adjusted by trial and error in order to optimize the
results. The proposed fuzzy controller has been designed and tested
progressively. It has been always implemented with a sampling time
of 15 min. The complete fuzzy controller is called fuzzy_plantwide,
which consists of 14 inputs, 6 outputs, and 80 fuzzy rules. For the
controller inputs, 8 sensors are required. As shown in Figs. 2b and
3, the fuzzy_plantwide inputs are SNH at the input of the biological
reactors (SNH,0), at the output of the second reactor (SNH,2), of the
third reactor (SNH,3), of the fourth reactor (SNH,4) and SNH,5, the sum
of SNO3
at the output of the fifth reactor (SNO3 5) and SNH,5, the tem-
perature (Tas), the input flow rate (Qin ), the product of Qin and SNH
at the input of the primary clarifier (SNH,in), as well as the deriva-
tive with respect to time of SNH,2 (dSNH,2/dt), SNH,3 (dSNH,3/dt), SNH,4
(dSNH,4/dt), the sum of SNH,5 and SNO3 5 (d(SNH,5+SNO3 5)/dt) and the
product of Qin and SNH,in (d(Qin·SNH,in )/dt). The fuzzy_plantwide out-
puts are the SO,3 set-point, the SO,4 set-point, the SO,5 set-point, Qa,
qEC,1 and Qst . Mamdani (Mamdani [35]) is the method of inference.
Fig. 4 shows the most relevant input-output relationships of the
fuzzy controller through surface graphs, which allows the observa-
tion of the non-linearity of the fuzzy controller. The regulation of the
fuzzy controller output variables is aimed to reduce GHG emissions,
to reduce costs and to improve the effl uent quality by reducing SNH,e
and SNtot,e
limit violations. However, the manipulation of each vari-
able has different objectives and there is no variable that tries to ful-
fill all the objectives only by itself. The value of the output variables
is obtained based on the input variables, by means of the so-called
fuzzy rules.
Fig. 2. Layouts ofDCS and fuzzy_plantwide.
6. I. Santín et al. / ISA Transactions 77 (2018)146–166 151
Fig. 3. Inputs and outputs offuzzy_1, fuzzy_2, fuzzy_3, fuzzy_4 and fuzzy_plantwide.
The 80 fuzzy rules relate the manipulated variables to the val-
ues of the measured variables. These input-output relationships are
based on the biological processes that take place during the wastew-
ater treatment, as well as on the plant operation experience. The rea-
sons for the choice of input-output relationships are explained in the
following paragraphs, for each one of the fuzzy controllers. The fuzzy
rules code is presented in appendix A and explained by a scheme in
appendix B. The FIS1
Editor from Matlab, used for the implementa-
tion of the fuzzy controllers, has some constrains in applying differ-
ent conditions. This fact requires the definition of a big number of
fuzzy rules that could be significantly reduced with a more flexible
tool.
In order to know the effect produced in the plant by the differ-
ent inputs and outputs, the controller has been tested and explained
incrementally by different steps until the fuzzy_plantwide has been
implemented. To this end, the fuzzy controllers have been numbered
from 1 to 4, as inputs and/or outputs have been added. Fig. 3 show
the inputs and outputs of each of these fuzzy controllers. The code
of fuzzy_plantwide is shown in Appendix A. The objectives sought in
each of the fuzzy controllers, as well as the reasons for their applica-
tion, are explained below.
Fuzzy_1. The main objective of fuzzy_1 is to reduce N2O emis-
sions, which are an important factor of GHG emissions. Higher N2O
emissions are generated during nitrification. As shown in several
articles such as Kimochi et al. [3], Kampschreur et al. [4], Foley et
al. [5], Law et al. [6], Flores-Alsina et al. [7,8], Aboobakar et al. [9] or
Wang et al. [10], N2O emissions during nitrification are the result of
partial nitrification. It happens when the SNH oxidation is not com-
pletely converted to SNO3
. Therefore, N2O emissions are related to SO
in the aerobic reactors (Boiocchi et al. [29]).
Therefore, the first application of the fuzzy controller is created
with the intention to avoid partial nitrification. Fuzzy_1 manipulates
the SO set-points of the aerobic reactors based on the SNH input of
each reactor. SO set-points are constrained from 0 to 5 mg/l. The SO
values are finally obtained by the PI controllers, whose set-points
are given by the fuzzy controller. Then, knowing the SNH input of a
reactor and based on the experience of the plant, the required SO is
added by the fuzzy controller for complete nitrification. In addition,
not only the values of SNH, but also their slopes are taken into account
by their derivatives with respect to time. This allows the controller to
be able to act in advance. In the case of fuzzy_1, when SO is low and
SNH begins to increase, the increase of SO has to be fast. Otherwise,
a very large increase of N2O can be produced. For this reason, the
derivative of SNH with respect to time (dSNH/dt) is taken into account,
mainly when the values of SNH are low in order to detect their immi-
nent increase (Fig. 4a, c and e). Finally, the resulting SO values are also
influenced by temperature, because N2O emissions are much higher
at high temperatures (Fig. 4b, d and f). It is important to note the dif-
ference between fuzzy_1 and the cascade SNH control widely used in
the literature (Vrecko et al. [36], Stare et al. [37], Flores-Alsina et al.
[7], Barbu et al. [27], etc.), because although this can achieve better
effl uent quality results, N2O emissions are not considered and par-
tial nitrification can occur, as high GHG emission values are shown
in Barbu et al. [27] by SNH,5 cascade control.
Although the main objective of fuzzy_1 is the reduction of the N2O
emissions, the levels of SNH and SNO3
are also taken into considera-
tion to some extent, since SO is regulated based on the SNH values.
In fuzzy_1 the outputs SO,3, SO,4 and SO,5 have five member-
ship functions, since “very_high” is added in fuzzy_2. Regarding the
1
FIS: Fuzzy Inference System.
7. 152 I. Santín et al. / ISA Transactions 77 (2018)146–166
Fig. 4. Graphic surfaces of the fuzzy control outputs related to the inputs.
inputs, SNH,2 has five mf, whereas SNH,3 and SNH,4 have six mf. This
is due to the fact that SNH,2 is similar throughout the year, but SNH,3
SNH,e and SNtot,e
are above of the established limits. For this purpose,
the inputs SNH,5 and the sum of SNH,5 and SNO3 5 (giving a value close
and SNH,4 vary depending on the temperature because more SNH is to that of SNtot
) are added.
oxidized at high temperatures than at low temperatures. In the case
of the time derivatives, dSNH,2/dt has three mf, dSNH,3/dt has four mf
and dSNH,4/dt has one mf (which is “high”). Each one has been ana-
lyzed separately, but all dSNH/dt have the same objective, which is to
give a “medium” value of SO when SNH is “low” and dSNH/dt is “high”
or “very_high”. Finally, the SO values also depend on Tas, which con-
sists of two mf (“medium-low” and “high”).
SNH,5 has three mf and SNH,5+SN O3 5 has five mf. However, in
both cases only one is used in fuzzy_2 (“high”). In addition, the mf
“very_high” is added to all SO outputs. Then, for all the fuzzy rules
of fuzzy_1, the constraint if SNH,5 is not “high” is added. When SNH,5
is “high”, all the SO outputs will be “very_high”, in order to oxidize
more SNH and to avoid its increase. Also, if SNH,5 is not “high” and
SNH,5+SNO3 5 is “high”, the “low” value is given to all SO (Fig. 4g–i). In
Fuzzy_2. The next step of the fuzzy controller aims to improve the this way, less SNO3
is generated and therefore SN tot and the aeration
effl uent quality in terms of reducing the percentage of time when costs are reduced.
8. I. Santín et al. / ISA Transactions 77 (2018)146–166
The input SNH,5+SNO3 5 is not added as a constraint in the fuzzy
rules of fuzzy_1, because that could imply an increase of N2O by
reducing SO.
Fuzzy_3. In the following fuzzy controller application, Qa is added
as output, while Qin and SNH,0 are added as inputs. The manipulation
of Qa aims to reduce the SNH peaks and it is not only based on Qin and
SNH,0, but also on SNH,5. Qa is constrained from 0 to 309,720 m3
/d
and, in addition, its variations are also constrained to 26,000 m3
/d
between two samples (15 min) in order not to have abrupt changes.
Qin has one mf (“high”), which is above the usual ranges of dry
weather and, thus, the values of this mf happen when there is rain-
fall event. SNH,0 also has only one mf called “high” and Qa three mf
(“low”, “medium” and “high”). Then, by fuzzy rules, when SNH,5 is
increased close to the limit, Qa is reduced in order to increase the
hydraulic retention time (HRT) and thus improve the nitrification
process. On the other hand, when there is a Qin increase due to a
rainfall and, at that time SNH,0 is “high” while SNH,5 is “low”, Qa is
increased to dilute the SNH concentration (Fig. 4j). When the SNH
peak reaches the aerobic reactors, detected as a result of SNH,5 being
“high”, Qa is reduced.
Fuzzy_4. In fuzzy_4, qEC,1 is added as an output. This is intended to
regulate the addition of qEC,1 instead of keeping it fixed at 2 m3
/d. The
addition of qEC improves the denitrification, significantly reducing
SNO3
values, but on the contrary, it increases the operational costs.
In addition, although a slight decrease of N2O in denitrification can
be produced when external carbon is added, the total GHG emis-
sions are higher due to an increase in the endogenous respiration
of biomass, in the sludge processing and in the chemical and energy
use. Due to these reasons, fuzzy_4 aims to add carbon only in the
cases where a reduction of SNtot,e
is necessary. In this way, the value
of qEC,1 added in fuzzy_4 is based on SNH,5+SN O3 5 and its time deriva-
tive (Fig. 4k). In addition, Qin is also taken into account, since its value
is increased when there is a rainfall, qEC,1 is also increased (Fig. 4l).
The value of qEC,1 is constrained from 0 to 5 m3
/d.
Five mf of the input SNH,5+SNO35 are related to five mf of the
output qEC,1. In the case of d(SNH,5+SNO3 5)/dt, it has three mf and
it is considered both to increase the values of qEC,1 and to reduce
them. So, when the mf of Qin is not active because there is no
rainfall, if d(SNH,5+SNO3 5)/dt is “medium”, the relationship between
SNH,5+SNO3 5 and qEC,1 is as follows: if SNH,5+SNO35 is “low” then qEC,1
is “low”, if SNH,5+SNO3 5 is “medium” then qEC,1 is “medium” and so
on. In the event that d(SNH,5+SNO3 5 )/dt is “high”, qEC,1 is previ-
ously increased to act in advance against SNtot,e
limit violations. If
d(SNH,5+SNO3 5)/dt is “low”, the value of qEC,1 is lower to save carbon
costs. If Qin is “high”, the values of qEC,1 in relation to SNH,5+SNO3 5 and
d(SNH,5+SNO3 5)/dt are also increased.
Fuzzy_plantwide. Finally, the fuzzy controller is fully imple-
mented, which is called fuzzy_plantwide. The fuzzy_plantwide code
is shown in Appendix A and a scheme of its fuzzy rules is in
Appendix B. The last application of the fuzzy controller adds Qst as
a manipulated variable. This is based on the product of Qin and SNH,in
and its derivative with respect to time.
The storage tank is responsible for regulating the amount of
water that is recirculated from the dewatering to the primary settler.
Although the amount of recirculated water is very low in comparison
to the influent, its SNH is very high.
First of all, the default operation of the storage tank has been par-
tially modified. As explained in the previous section, in the default
operation, when the water volume of the tank is below or equal to
the minimum established value, all the flow leads into the tank while
Qst is equal to 0. This has been modified in order to fill the tank if it
is necessary. In such a way that all the flow is led by bypass if Qst
is higher than the input flow. On the other hand, if the given Qst is
lower than the input flow, Qst will be equal to the given value and the
tank will be filled by the difference between the inlet and the out-
153
Table2
Simulationresultsofthedefaultcontrolstrategy,literatureandtheproposedfuzzycontrollersaswellaspercentagesofimprovementwithrespecttothedefaultcontrolstrategy.
EvaluationCriteriaDCSSantínetal.[28]fuzzy_1fuzzy_2fuzzy_3fuzzy_4Fuzzy_plantwide
valuevalue%of
improvement
85.09
value%of
improvement
87.55
value%of
improvement
88.77
value%of
improvement
89.24
value%of
improvement
94.81
value%of
improvement
98,96EffluentqualitySNtot,eviolations
(%ofoperatingtime)
SNH,eviolations
(%ofoperatingtime)
EQI(kgofpollutants/d)
10.61.721.321.191.140.550.11
1.140.05438.570.1289.470.06694.21010001000100
5665.985469.223.475490.453.105489.193.125486.983.165595.431.245567.771.73
OperationalcostsAE(kWh/d)
PE(kWh/d)
EC(kg/d)
OCI
4306.25
261.48
800
9272.78
–
–
–
8635.33
–
–
–
6.94
3788.46
261.48
800
8737.47
12.02
0
0
5.78
3786.08
261.48
800
8735.08
12.08
0
0
5.80
3782.05
263.71
800
8733.22
12.17
−0.85
0
5.82
3701.54
263.74
546.12
7839.63
14.04
−0.86
31.73
15.45
3639.85
263.73
5015.01
7677.35
15.47
−0.86
35.62
17.20
GHGemissionsN2Obiotreatment(kgCO2equiv-
alent/d)
Endogenousrespirationof
biomass(kgCO2/d)
Totalbiotreatment(kgCO2/d)
PowerCredit(kgCO2/d)
EC_GHG(kgCO2/d)
TotalCO2(KgCO2/d)
1596.21197.7922.17773.3751.55782.9250.95786.6650.72801.9249.76858.2846.23
3563.83––3541.010.643540.980.643540.890.643455.733,.033443.693.37
9086.11
−505.85
821.33
17,851.1
–
–
–
17,134.19
–
–
–
4.02
8243.59
−738.91
821.33
16,753.28
9.27
46.07
0
6.15
8253.15
−740.01
821.33
16,761.64
9.18
46.29
0
6.10
8256.82
−740.83
821.33
16,764.53
9.13
46.45
0
6.09
8187.67
−755.23
560.68
16,339.12
9.89
49.30
31.73
8.47
8231.31
−781.23
528.34
16,315.06
9.41
54.44
35.67
8.60
9. 154 I. Santín et al. / ISA Transactions 77 (2018)146–166
let flow while the volume does not reach its maximum established
value.
Once the operation of the storage tank has been modified,
fuzzy_plantwide controller aims to compensate the Qin ·SNH,in peaks
by reducing Qst . Conversely, when the values of Qin ·SNH,in are lower,
Qst is increased to empty the storage tank. Qst is constrained from 0
to 1500 m3
/d.
Both Qin ·SNH,in and Qst have five mf, whereas d(Qin·SNH,in)/dt
has three mf. When d(Qin·SNH,in )/dt is “medium” the relationship
between Qin ·SNH,in and Qst is completely reversed (if Qin ·SNH,in is
“low” then Qst is “high”, if Qin ·SNH,in is “medium-low” then Qst is
“medium-high”, etc). In the case of d(Qin·SNH,in)/dt is “low”, Qst is
higher. Conversely, if d(Qin·SNH,in)/dt is “high”, the values of Qst are
lower (Fig. 4m).
4. Simulation results and discussion
This section presents the simulation results and the discussion
regarding the fuzzy controller. As well as in the previous section, the
results have been analyzed for each one of the fuzzy controllers that
have been implemented incrementally in order to observe the effects
of the different inputs, outputs and fuzzy rules.
Table 2 shows the results obtained with fuzzy_1, fuzzy_2, fuzzy_3,
fuzzy_4 and fuzzy_plantwide, as well as the results of Santín et al.
[28] and DCS. The latter has been used as reference for the percentage
of improvement. The articles Flores-Alsina et al. [7,8] and Boiocchi et
al. [38] also include the GHG emissions assessment, but they have
not been considered for comparison because the first two articles
use the original BSM2G and the last one uses BSM2 for Nitrous oxide
(BSM2N). In the case of Barbu et al. [27] and Santín et al. [28], they
are the only papers that use the same updated BSM2G as the present
article. Although Barbu et al. [27] evaluates GHG emissions, it does
not implement a specific control strategy to reduce them, resulting
in higher GHG emissions than by applying DCS. Therefore, Barbu et
al. [27] has neither been considered for comparison, since the main
objective of the present article is the reduction and consequently, the
first step before considering other criteria.
Effl uent quality has been evaluated through the percentage of
time of SNtot,e
and SNH,e limit violations. Although the main objective
in terms of quality is to keep contaminants below the established
limits, EQI is also shown as a criterion to be compared. COD, TSS and
BOD5 limit violations are not shown because they only occur on cer-
tain days when there is a bypass and this is not modified with the
proposed fuzzy controller.
Within the operational costs, there are shown those that have
significant variations. These are especially AE and EC, but PE is also
shown because Qa is regulated from fuzzy_3.
Regarding the GHG emissions, in addition to the total CO2, the
emissions of the sources that have significant variations with the
proposed fuzzy controller are also shown. These are those produced
Fig. 5. SNH,2 , SO,3 , N2O,3 and KL a3 oftwo days simulation in summer (a) and in winter (b) for the default and the proposed fuzzy controllers.
10. I. Santín et al. / ISA Transactions 77 (2018)146–166 155
Fig. 6. SNH,3 , SO,4 , N2O,4 and KL a4 oftwo days simulation in summer (a) and in winter (b) for the default and the proposed fuzzy controllers.
in the biological treatment (total biotreatment), the CO2 due to
electric consumption minus the electric generation (Power credit)
and the CO2 generated from external carbon source production
(EC_GHG). Among the GHG emissions produced in the biological
treatment, they are shown those that have significant variations due
to the application of the proposed fuzzy controller, which are the N2O
the reductions of the SNtot,e
and SNH,e violations. Regarding the oper-
ational costs, the reduction of OCI is mostly obtained due to an AE
reduction.
Figs. 5–8 allow the analysis of these numerical results shown in
Table 2. The first three figures show SNH at the input, SO at the out-
put, N2O at the output and KLa4 at the output for the third, fourth
emissions and the CO2 produced by the endogenous respiration of and fifth tanks. Fig. 8 shows the SN tot,e and SNH,e time evolution. For
the biomass.
Figs. 5–11 show the evolution over time of the different input and
output variables, as well as the evolution of some variables that give
all figures, two summer days and two winter days are shown. The
summer days selected are those with higher N2O emissions. And for
the winter days a rainfall event has been selected that results in SNH,e
information about the objectives of the plant performance. They are
shown only for two days in order to better observe the comparison and SN tot,e
increases.
between the different controllers and for both winter and summer
days. This is because the behavior of the plant is different depending
on temperature. There have been selected specific days to observe
The N2O reduction obtained is shown in Figs. 5–7 and it is mainly
achieved in summer, because the emissions are much higher at high
temperatures. Within the three reactors, the most problematic N2O
emissions occur in tank 3. Fig. 5a show an example of a large differ-
the effect of some concrete variables. Both the numerical results and
the evolution of the variables over time are discussed below. ence of N2O emissions between DCS and the fuzzy controllers. This
Fuzzy_1. Table 2 shows how the N2O emissions in the biological is due to the fact that SO,3 with DCS is much lower, whereas SNH,2 is
very similar, which produces a partial nitrification.
treatment are reduced by 51.55% by applying fuzzy_1 compared to
DCS. This is the most important factor in the reduction of GHG, but In the case of the fourth tank (Fig. 6a), although the N2O emissions
are lower than in the third tank, larger peaks of N2O are also observed
it is not the only one, since there is also a reduction of power credit,
mainly due to a decrease in AE. In addition, among the results shown in DCS compared to fuzzy_1. However, in this case S O,4
is maintained
in the same table, in terms of effl uent quality, it is worth highlighting
at 2 mg/l with DCS and its value is higher than that obtained with
fuzzy_1 during all the time. One possible reason is the high genera-
11. 156 I. Santín et al. / ISA Transactions 77 (2018)146–166
Fig. 7. SNH,4 , SO,5 , N2O,5 and KL a5 oftwo days simulation in summer (a) and in winter (b) for the default and the proposed fuzzy controllers.
tion of N2O in the third tank (N2O,3).
In the last tank, during summer time (Fig. 7a), first it is observed
that the values of SNH,4 are lower with DCS than with fuzzy_1. This
is due to the higher values of SO,4 mentioned before, which result in
a higher SNH oxidation. In addition, the values of SO,5 are also higher
in DCS and consequently the N2O emissions are lower, although dur-
ing the peaks of SNH,4 the differences are reduced and there is even an
interval when SNH,4 is higher in DCS. In any case, the N2O emissions in
the last tank are much lower compared to the fourth tank and above
all compared to the third tank. During the summer periods the SNH,e
values are low (Fig. 8a), this fact translates that the high SO,4 and SO,5
values obtained with DCS are unnecessary. In addition, high SO,5 val-
ues produce an increase in AE, resulting in increased costs and GHG
emissions due to the electric consumption. Also, by the internal recir-
culation, high SO,5 values result in an excess of SO in the anoxic tanks
and consequently in a deterioration of the denitrification process.
values of SO,3 obtained with fuzzy_1 are similar to those obtained
with DCS (Fig. 5b), but with fuzzy_1 they are slightly lower when
SNH,2 is low and slightly higher when there is a peak of SNH,2. The SO,4
and SO,5 values (Figs. 6b and 7b) are almost all the time lower with
fuzzy_1 than with DCS, except when the SNH peak is increased more
than usual, as happens on the day 422.
As it can be observed in Fig. 8b, the discussed SO reduction by
fuzzy_1 results in a SNtot,e
reduction, as shown in the SNtot,e
violations
results in Table 2. Referring to SNH,e, the values are higher most of
the time in the case of fuzzy_1 than in the case of DCS because with
lower SO values, less SNH is oxidized. However, during this time, SNH,e
values are below the established limits. When there is a SNH peak, SO
is increased when fuzzy_1 is applied, which decreases the time of the
SNH,e violations, as shown in the results available in Table 2.
In short, fuzzy_1 offers, with respect to DCS, a N2O reduction
mainly due to the SO regulation in tank 3. In terms of effl uent quality,
Other important factors for the N2O emissions reduction are the S tot,e and SNH,e violations have been greatly reduced. This is achieved
inputs of dSNH/dt in the three aerobic tanks. In Figs. 5a, 6a and 7a it
can be observed the fast SO increase at the beginning of the SNH peaks
by regulating SO of the aerobic tanks, increasing them when there is
an SNH increase, and keeping them at low levels for the rest of the
when an increase in its slope is detected. This fact is very important time, thus nitrifying less and therefore generating less SNO and con-
because if SO is not increased rapidly when SNH begins to increase, sequently less SNtot,e . Although the main objective is to
3
keep pollu-
the result can be a significant rise of N2O. tants below limits, EQI is also reduced. Finally, by applying fuzzy_1,
In cold periods, the N2O emissions are lower and the main dif-
ficulty is keeping SNH,e and SNtot,e
below the established limits. The
the SO values are lower most of the time in comparison with DCS and
N
12. I. Santín et al. / ISA Transactions 77 (2018)146–166 157
Fig. 8. SNH,e and SNtot,e
of two days simulation in summer (a) and in winter (b) for the default and the proposed fuzzy controllers.
Fig. 9. Qin , SNH,0 , SNH,5 and Qa of days 422 simulation for the default and the proposed fuzzy controllers.
this results in an AE decrease.
Fuzzy_2. Table 2 shows that the main differences of the fuzzy_2
results compared to fuzzy_1 are the SNH,e and SNtot,e
limits violations.
Specifically, the largest reduction is obtained in the SNH,e limit viola-
tions, which are reduced by more than 14% compared to fuzzy_1.
As previously discussed, the SNH,e and SNtot,e
limits violations
are more likely to occur at low temperatures. The performance of
fuzzy_2 is shown in Fig. 8b and the variations in the SO manipulation
13. 158 I. Santín et al. / ISA Transactions 77 (2018)146–166
Fig. 10. Qin , SNH,5 +SNO35 and qEC,1 of two days simulation in summer (a) and in winter (b) for the default and the proposed fuzzy controllers.
are shown in Figs. 5b, 6b and 7b. When SNH,5 is “high” the fuzzy rules
implemented in fuzzy_1 are overridden, and a “very_high” value of
SO of the aerobic reactors is added. This is observed in the above
referred figures, where the differences between fuzzy_1 and fuzzy_2
are only observed on day 422, where SO,3, SO,4 and SO,5 have higher
values with fuzzy_2, which coincides with a high SNH,5 peak. Conse-
quently, the SNH,e peak is reduced (Fig. 8b).
Another fuzzy rule added in fuzzy_2 allows a decrease in the val-
ues of SO when SNH,5 + SNO3 5 is “high” as long as SNH,5 is not “high”.
This effect is more diffi cult to be observed in the figures. However, by
using fuzzy_2 it is possible to see that SO,3, SO,4 and SO,5 are reduced
faster after the SNH peak and, for a certain time interval, they are
lower than in the case of fuzzy_1. This does not affect SNtot,e
of day
422, since SO is increased prior to this moment, but there are other
peaks during the year that are slightly reduced. These reductions are
low, as it can be seen in the SNtot,e
violations results in Table 2.
Finally, in terms of operational costs, the SO increase when SNH,5
is “high” does not result in an AE increase, because these SO increases
occur only rarely throughout the year and as a result of the slight SO
reduction when SNH,5+SNO3 5 is “high”. In fact, Table 2 shows that AE
is even slightly lower with fuzzy_2 when compared to fuzzy_1.
Fuzzy_3. The results of Table 2 show how the SNH,e violations are
completely removed by means of fuzzy_3, while the other results
are the same or have non-significant variations when compared to
fuzzy_2.
Fig. 9 allows its analysis with the example of day 422. Qa is mostly
maintained at its default value. However, day 422 is an example
where Qa is incremented and reduced from its default value. When
there is an increase of Qin due to a rainfall and there is also a SNH,0
peak, fuzzy_3 increases Qa in order to dilute SNH. This dilution is
achieved because SNH,5, which is recirculated, is lower than SNH,0.
Due to this reason, Qa is only increased in the case when SNH,5 is
not “high”. On the other hand, increasing Qa results in a reduction
of the hydraulic retention time and consequently the denitrification
and nitrification processes worsen. Therefore, it has to be taken into
account that the Qa manipulation aims to reduce the SNH,e violations
and the nitrification process is the key factor to achieve that. Accord-
ingly, when the SNH increase reaches the aerobic reactors, detected
by a SNH,5 increase, fuzzy_3 decreases Qa in order to increase the
hydraulic retention time and thus the aerobic reactors oxidize more
SNH. The result is observable in the reduction of the SNH,5 peak by
the application of fuzzy_3 when compared to fuzzy_2. Consequently
SNH,e is also reduced as it is shown in Fig. 8.
The only important difference in fuzzy_3 is the elimination of the
SNH,e limit violations. However, it can be observed that there is a
slight reduction in the SNtot,e
violations. This is because sometimes
the conditions Qin is “high” and SNH,0 is “high” are met, but the SNH,5
peak fails to get the “high” value. Hence, Qa is increased, but subse-
14. I. Santín et al. / ISA Transactions 77 (2018)146–166 159
Fig. 11. Qin ·SNH,in, Qst and Vst of two days simulation in summer (a) and in winter (b) for fuzzy_plantwide.
quently is not reduced below the default value. This fact causes more
SNO3
to be denitrified and the aerobic reactors to generate less SNO3
as
reduce it more than by using fuzzy_3. This is the reason for reducing
SNtot,e
limit violations. Therefore, fuzzy_4 increases qEC,1 only when
a result of the hydraulic retention time being lower. Due to this rea- it is necessary with the objective that SNtot,e
si not exceed the estab-
son, the PE mean is also slightly higher. However, these variations
are practically negligible.
Fuzzy_4. Table 2 shows the results obtained with fuzzy_4. In
terms of effl uent quality, a decrease of more than 5.57% in the
SNtot,e
limit violations is achieved (being near of the total removal),
lished limit. Thus, fuzzy_4 gives more importance to the fact that the
pollutants concentration is below the established limits than to their
means.
It is worth to note that the value of qEC,1 depends not only
on SNH,5+SN O 5 but also on the input d(SNH,5+SN O 5)/dt. Thus, if
3 3
while EQI is slightly worsened. In reference to operating costs, EC is
reduced with 31.73% that results in a OCI reduction of 9.63%. This
reduction also results in a total GHG emissions reduction of 2.38%.
Figs. 10 and 8 allow to analyze the mentioned results. Fig. 10
shows that the values of SNH,5+SN O3 5 are lower in summer than in
winter and, therefore, the values of qEC,1 obtained by fuzzy_4 are
SNH,5+SN O3 5 increases with a high slope, qEC,1 is more rapidly
increased in order to further reduce the SNH,5+SNO3 5 peak. While
SNH,5+SNO3 5 decreases, qEC,1 is reduced more quickly to reduce costs.
In addition, if there is a Qin increase due to a rainfall and there is
a high SNH,5+SNO3 5 slope, qEC,1 is rapidly increased to its maximum
value without taking into account the SNH,5+SNO3 5 value, as it can be
also lower. However, for both summer and winter, the values of qEC,1
are below the default value (2 mg/l) most of the time. This leads
to higher SNH,5+SNO3 5 and thus, higher SNtot,e
values for most of the
time by applying fuzzy_4 when compared to fuzzy_3. This is the rea-
son for the slight EQI worsening. On the other hand, when there is
a SNH,5+SNO3 5 peak, qEC,1 is increased above 2 mg/l. Only in cases
where the SNH,5+SNO3 5 peak is much higher, as in the case of day
422, the qEC,1 increase by applying fuzzy_4 is high enough high to
observed on day 422 in Fig. 8.
Finally, the fact that most of the time qEC,1 is below its default
value explains the cost reduction obtained. Also, it results in a GHG
emissions reduction due to a CO2 generated from the EC production
and from the endogenous respiration of biomass.
Fuzzy_plantwide. The results in Table 2 show a decrease in the
SNtot,e
limit violations due to the reduction of their peaks, with
the almost complete removal. They are only 4 times of violations
15. 160 I. Santín et al. / ISA Transactions 77 (2018)146–166
throughout the year and they are due to a high Qin increase that
results in the bypass from the influent to the effl uent without being
treated. The reduction of the SNH,e peaks is not reflected in the results
since the SNH,e limit violations are completely removed in fuzzy_3.
This SNtot,e
and SNH,e peak reduction also leads to slight AE and EC
reductions, which have repercussions on both cost and GHG emis-
sion reductions.
The operation of Qst manipulation by fuzzy_plantwide controller
is shown in Fig. 11. First, it is worth explaining that the liquid
extracted from the sludge treatment is recirculated to the primary
treatment and regulated by the storage tank. The flow rate of this
liquid is very low compared to the influent. However, the reason for
Qst manipulation is the high value of SNH in the recirculated liquid,
which can produce SNH increases in the biological treatment. This
SNH increase has also results in the need for more SNH to be oxidized
and thus more SNO3
is generated, which results in a SNtot
increase.
Then, as it can be seen in Fig. 11, both in summer and winter, when
improvement of EQI, OCI and CO2 emissions compared to DCS are
of 1.97%, 14.4% and 8.24% respectively. Although there are small
changes in the percentages of improvement, they are still satisfac-
tory. It is important to note that filters are usually applied, which
attenuate the noise signal. In addition, the controller parameters
should be adjusted after an analysis of the sensor signals.
5. Conclusions
This paper has presented the implementation of a fuzzy controller
for the plant-wide control of biological wastewater treatment pro-
cesses with the objectives of reducing GHG emissions, SNtot,e
and the
SNH,e limits violations and operational costs (AE and EC). The imple-
mentation of three PI controllers have also been required in order
to track the SO set-points given by the fuzzy controller. The follow-
ing points summarize the results obtained with the proposed control
strategy:
there is a Qin ·SNH,in peak, Qst is reduced in order not to recircu-
late SNH. When the values of Qin ·SNH,in are lower, Qst is increased • It is possible to reduce GHG emissions by manipulating the SO set-
in order to reduce the volume of the tank and thus to have it ready
for the next Qin·SNH,in peak. This is seen in the evolution of the stor-
age tank volume (Vst). Fig. 11 only shows the values obtained with
fuzzy_plantwide controller because by using the previous fuzzy con-
trollers and DCS, Qst is always kept at 0 and all the flow rate from the
dewatering is led by bypass once the storage tank is full.
points of the aerobic reactors, based on the SNH at the entrance of
each reactor, avoiding partial nitrification. In addition, with the
same regulation it is also possible to improve the effl uent quality
and to reduce operational costs.
• The manipulation of the SO set-points taking also into account
SNH,5 as an input allows the fuzzy controller a slight reduction of
the SNH,e peaks when these are higher than usually. In addition,
The input d(Qin ·SNH,in)/dt is similarly used as fuzzy_4 operates
with d(SNH,5+SN O3 5 )/dt. Therefore, it is used to increase or decrease
the SNO3 5
+SNH,5 measurement allows to slightly reduce the S Ntot,e
Qst more quickly based on the slope of Qin ·SNH,in in order to act in
advance.
Focusing first on the results obtained in winter, Fig. 8b shows that
the SNH,e and SNtot,e
peak reductions are clearly observable. When Qst
peaks when there is no risk of SNH,e violation or GHG increase.
• The manipulation of Qa allows the SNH,e reduction when it is nec-
essary. For this purpose, Qa is manipulated, increasing it to dilute
SNH at the input of the biological treatment or decreasing it to
increase the hydraulic retention time.
is increased during lower Qin ·SNH,in values, SNH,e and SNtot,e
are higher • Increasing q EC,1 when SNO35
+SNH,5 increases allows to avoid the
with fuzzy_plantwide compared to fuzzy_4. Thus, variations of SNH,e
SNtot,e limit violations. Decreasing q EC,1 for the remaining time
and SNtot,e
with fuzzy_plantwide are smoother. Fig. 10b shows that results in a reduction of GHG emissions and costs.
less qEC,1 is required due to the SNtot,e
peak reduction. In addition, • The regulation of Qst based on the influent, specifically based on
Figs. 5b, 6b and 7b show that less SO is required to complete nitrifi- Qin ·SNH,in, allows to significantly reduce the SN tot,e and SNH,e peaks.
cation due to the reduction of the SNH peaks in each of the reactors.
The qEC,1 and SO reductions result in reductions of operating costs
• The derivatives of some fuzzy controller inputs with respect to
time allow better regulation when acting in advance. Especially
and GHG emissions as shown in the results in Table 2.
In the case of the summer period, due to the fact that the val- in the case of the SO set-points, to avoid large GHG increases.
ues of SNtot,e
and SNH,e are lower, there is no risk of limit violations,
at least during dry weather, and this increases their values with
fuzzy_plantwide than with fuzzy_4 (Fig. 8a). The only drawback is
the consequent qEC,1 increase during summer (Fig. 10a). Although
there is a cost reduction with fuzzy_plantwide in the annual results,
the Qst and/or qEC,1 manipulations could be improved during ele-
vated temperatures in order to further reduce operating costs. On the
other hand, the SNH peaks of the aerobic tanks are also reduced dur-
ing the summer period and thus lower SO is also required (Figs. 5a, 6a
and 7a).
Regarding the stability of the fuzzy controller, although this is
complex to be guaranteed from a mathematical point of view, it can
be obtained through knowledge implementation. It has to be noticed
that during the entire simulation period of 609 days (1 year of eval-
uation) no instability has been detected.
Finally, although the use of ideal sensors is a valid option to com-
pare control strategies, a simulation with fuzzy_plantwide adding
noise and delay to the sensors has been performed. The complete
removal of the SNH,e limit violations has been maintained. The per-
centage of time of the SNtot,e
limit violations increases slightly to
0.45%, but it is still a low time percentage. The percentages of
• To carry out a sensitivity analysis is an objective as a future work,
to analyze the influence of the plant parameter variations in the
results obtained with the proposed control strategies.
Finally, satisfactory results have been obtained by applying the pro-
posed fuzzy controller, which allowed achieving the objectives to
reduce GHG emissions, to improve the effl uent quality and to reduce
operational cost.
Acknowledgment
This work was supported by the the Spanish MINECO/FEDER
grant DPI2016-77271-R. Lund University and Technical University of
Denmark are gratefully acknowledged for providing the BSM2G Mat-
lab/Simulink code, with a special mention for Dr. Flores Alsina and
Dr. Ulf Jeppsson.
18. -if SNll.3 is "low" then
50,4 is "low"
-if SN!j3 is "medium-
low' then 50,4 is
"medium-lo w"
-if SNH,3 is "medium"
then S0.4 is "medium"
-if SN83 is "low"
-if SNH,3 is "medium-
low" then 50,4 is
"low"
-if SNH,3 is
"medium" then S0.4
is "medium-low"
so ·ts "medium"
-if SNH,3 is "medium-
low" then 50,4 is
"medium"
-if SNH,3 is "medium"
then 50.4 is "medium"
I. Santin et al. / ISA Transactions 77 (2018) 146-166 163
Appendix B. Scheme of the fuzzy_plantwide rules
S0,3 manipulation
S0,4 manipulation
if Tas is "medium-
low"
if dSN11,3/dt is not
"very high"
ifSNH,5 isnot "high"
-if SNfl. is "medium-
hign then S0,4 is
"medium-high"
-if SN11,3 is "high"
then s0,. is "high"
-if SNH,3 is "very
0
if Tu is "high"
-if sN8fsi; Jd [ "en
50,4
-if S3 is "mediu m-
"high"
low' then 504 is
"medium"
-if SNH,3 is "medium"
then 50,4 is "medium"
-if SN is "medium"
then 3 is "medium"
-if SNH.Z is"mediurn-
high" then s0,, is
"medium-high"
-if SNu,z is"hig h then 50.3
is "high"
-if SNH,z is";;..then 50.J is
-ifSNH.Z is "medium-low"
then 50,3 is "medium-low"
-if SN1zsi JY:.n 50•3
-if SNH.Z is "mediu m- lo w "
then 50,3 is "medium"
-if SNH,z i · .en 50,
3
-if SNH.Z is"medium-
low" then 50,3 is
"medium-low"
-if sN"Izsi IJr:.nso,3
-if SNH.2 is "medium-low"
then 50,3 is "medium"
if SNH,sisnot "high"
if S..5 is "high"then
S0,3'is"veryhigh"
ifdSN11,2/dt is "high"
if TEi. is "medium-low"
anadSNH,2/dtis not
"high"
if ds...2/dt is "low"
-if SNfl. is "medium-
hign then S04 is
"mediumw
-if SN11,3 is "high"
then 50,4 is
"medium-high"
-if SN is "very
high""hi .o.< is
19. 164 I. Santin et al. / ISA Transactions 77 (2018) 146-166
S0,s manipulation
I
if SNu,5is not "high"
if Qinis "high" and s••0 is "high"
then Q.is "hign"
if °t;1is not "high"or SNois not
" igh" then Q. is "me ium"
Q manipulation3
I
-if SNu,' is
"medium-high"
then S05 is
"medium"
-if SNH.• is"high"
then S0,5 is
"medium-high"
-ifSwu.• is "very
high then S0,5 is
"high"
-if S"".t is "medium-
low ' then S0,5 is
"medium"
-if SN8,4is "medium"
then S0,5 is "medium-
high"
-if SNu,4 is "medium-
high" then S0,5 is "high"
-if S is "high" then
"s s is "high"
-ifSNH.4 is "very high"
then S0,5 is "high"
-if S"""is "low"
then S0,5 is
"medium"
-if SN!l.4 is"low"
then S0,5 is"low"
-if SNH,4 is
"medium-low"
then S0,5 is"low"
-if S••.• is
"medium" then 50,
5 is"medium-low"
-if SNu,4 is "low"
then S0,5 is
"medium"
-ifSNH,f is
"medium-low"
then S0,5 is
"medium"
"medl ·ti:n S0,
5 is "medium"
ifSNH.5 isnot
"high"
ifSNH,Sis "high" then
S0,5 is "very high
if Tas is "medium -
low"
if dSNll.4/dt is not
"high"
if dSa.Jdt is
"high"
if dSJ<a'l/dtis
"high"
-if SNa4 is "low"
then S0,5 is "low"
I
if SNu,5 is "high" then Q. is "low"
-
-
20. I. Santín et al. / ISA Transactions 77 (2018)146–166 165
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