Use of Control theory within process control industries has changed rapidly due to the increase complexity
of instrumentation, real time requirements, minimization of operating costs and highly nonlinear
characteristics of chemical process. Previously developed process control technologies which are mostly
based on a single controller are not efficient in terms of signal transmission delays, processing power for
computational needs and signal to noise ratio. Hybrid controller with efficient system modelling is essential
to cope with the current challenges of process control in terms of control performance. This paper presents
an optimized mathematical modelling and advance hybrid controller (Fuzzy Logic and PID) design along
with practical implementation and validation of pH neutralization pilot plant. This procedure is
particularly important for control design and automation of Physico-chemical systems for process control
industry.
An Adaptive Liquid Level Controller Using Multi Sensor Data FusionTELKOMNIKA JOURNAL
This paper describes a design of adaptive liquid level control system using the concept of Multi
Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the
level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i)
implementing a MSDF framework using Pau’s framework for measuring liquid level and temperature, ii)
analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable
adaptive controller which will produce desired control action for controlling liquid level accurately using
neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation
of level transmitter output for change in temperature. This information is used by controller to train the
neural network so as to tune the controller parameters (proportional gain, integral constant, and differential
constant), to drive the actuator. Results obtained show that the system is able to control liquid level within
range of 1.915% of set point even with variations in liquid temperature.
DESIGN OF PID CONTROLLERS INTEGRATOR SYSTEM WITH TIME DELAY AND DOUBLE INTEGR...ijics
In this paper first we investigate optimal PID control of a double integrating plus delay process and compare with the SIMC rules. What makes the double integrating process special is that derivative action is actually necessary for stabilization. In control, there is generally a trade-off between performance and
robustness, so there does not exist a single optimal controller. However, for a given robustness level (here defined in terms of the Ms-value) we can find the optimal controller which minimizes the performance J (here defined as the integrated absolute error (IAE)-value for disturbances). Interestingly, the SIMC PID controller is almost identical to the optimal pid controller. This can be seen by comparing the paretooptimal
curve for J as a function of Ms, with the curve found by varying the SIMC tuning parameter Tc.
Second, design of Proportional Integral and Derivative (PID) controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. The performances of the proposed controllers are compared with the
controllers designed by recently reported methods. The robustness of the proposed controllers for the uncertainty in model parameters is evaluated considering one parameter at a time using Kharitonov’s theorem. The proposed controllers are applied to various transfer function models and to non linear model of isothermal continuous copolymerization of styrene-acrylonitrile in CSTR. An experimental set up of tank
with the outlet connected to a pump is considered for implementation of the PID controllers designed by
the three proposed methods to show the effectiveness of the methods.
Decentralized proportional-integral control with carbon addition for wastewat...journalBEEI
Two main challenges in activated sludge wastewater treatment plant (WWTP) are cost and effluent quality, which has forced the wastewater treatment operator to find an alternative to improve the existing control strategy. The Benchmark Simulation Model No. 1 (BSM1) is applied as operational settings for this study. In BSM1, the standard control variables are the internal recirculation flow rate and the oxygen transfer rate. To improve the existing control strategy of BSM1, three alternative control handles are proposed, which are the individual aeration intensity control, carbon source addition and combination of both. The effect of each control handles in terms of the effluent violation, effluent quality, aeration cost, and total operational cost index are examined. The simulation result has shown that the individual control of aeration intensity improved the effluent quality index, and reduced the aeration, pumping, and total operational cost index when compared to the standard BSM1 control handle. Nonetheless, the addition of a fixed external carbon source has shown a significantly improved effluent quality with a lower number of total nitrogen violations as compared to the standard BSM1 control handles. Thus, the proposed control handles may be beneficial if applied in a real WWTP.
An Adaptive Liquid Level Controller Using Multi Sensor Data FusionTELKOMNIKA JOURNAL
This paper describes a design of adaptive liquid level control system using the concept of Multi
Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the
level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i)
implementing a MSDF framework using Pau’s framework for measuring liquid level and temperature, ii)
analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable
adaptive controller which will produce desired control action for controlling liquid level accurately using
neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation
of level transmitter output for change in temperature. This information is used by controller to train the
neural network so as to tune the controller parameters (proportional gain, integral constant, and differential
constant), to drive the actuator. Results obtained show that the system is able to control liquid level within
range of 1.915% of set point even with variations in liquid temperature.
DESIGN OF PID CONTROLLERS INTEGRATOR SYSTEM WITH TIME DELAY AND DOUBLE INTEGR...ijics
In this paper first we investigate optimal PID control of a double integrating plus delay process and compare with the SIMC rules. What makes the double integrating process special is that derivative action is actually necessary for stabilization. In control, there is generally a trade-off between performance and
robustness, so there does not exist a single optimal controller. However, for a given robustness level (here defined in terms of the Ms-value) we can find the optimal controller which minimizes the performance J (here defined as the integrated absolute error (IAE)-value for disturbances). Interestingly, the SIMC PID controller is almost identical to the optimal pid controller. This can be seen by comparing the paretooptimal
curve for J as a function of Ms, with the curve found by varying the SIMC tuning parameter Tc.
Second, design of Proportional Integral and Derivative (PID) controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. The performances of the proposed controllers are compared with the
controllers designed by recently reported methods. The robustness of the proposed controllers for the uncertainty in model parameters is evaluated considering one parameter at a time using Kharitonov’s theorem. The proposed controllers are applied to various transfer function models and to non linear model of isothermal continuous copolymerization of styrene-acrylonitrile in CSTR. An experimental set up of tank
with the outlet connected to a pump is considered for implementation of the PID controllers designed by
the three proposed methods to show the effectiveness of the methods.
Decentralized proportional-integral control with carbon addition for wastewat...journalBEEI
Two main challenges in activated sludge wastewater treatment plant (WWTP) are cost and effluent quality, which has forced the wastewater treatment operator to find an alternative to improve the existing control strategy. The Benchmark Simulation Model No. 1 (BSM1) is applied as operational settings for this study. In BSM1, the standard control variables are the internal recirculation flow rate and the oxygen transfer rate. To improve the existing control strategy of BSM1, three alternative control handles are proposed, which are the individual aeration intensity control, carbon source addition and combination of both. The effect of each control handles in terms of the effluent violation, effluent quality, aeration cost, and total operational cost index are examined. The simulation result has shown that the individual control of aeration intensity improved the effluent quality index, and reduced the aeration, pumping, and total operational cost index when compared to the standard BSM1 control handle. Nonetheless, the addition of a fixed external carbon source has shown a significantly improved effluent quality with a lower number of total nitrogen violations as compared to the standard BSM1 control handles. Thus, the proposed control handles may be beneficial if applied in a real WWTP.
Combined ILC and PI regulator for wastewater treatment plantsTELKOMNIKA JOURNAL
Due to high nonlinearity with features of large time constants, delays, and interaction among variables, control of the wastewater treatment plants (WWTPs) is a very challenging task. Modern control strategies such as model predictive controllers or artificial neural networks can be used to deal with the non-linearity. Another characteristic of this system should be considered is that it works repetitively. Iterative learning control (ILC) is a potential candidate for such a demanding task. This paper proposes a method using ILC for WWTPs to achieve new results. By exploiting data from the previous iterations, the learning control algorithm can improve gradually tracking control performance for the next runs, and hence outperforms conventional control approaches such as feedback controller and model predictive control (MPC). The benchmark simulation model No.1-BSM1 has been used as a standard for performance assessment and evaluation of the control strategy. Control of the Dissolved Oxygen in the aerated reactors has been performed using the PD-type ILC algorithms. The obtained results show the advantages of ILC over a classical PI control concerning the control quality indexes, IEA and ISE, of the system. Besides, the conventional feedback regulator is designed in a combination with the iterative learning control to deal with uncertainty. Simulation results demonstrate the potential benefits of the proposed method.
Modified smith predictor based cascade control of unstable time delay processesISA Interchange
An improved cascade control structure with a modified Smith predictor is proposed for controlling open-loop unstable time delay processes. The proposed structure has three controllers of which one is meant for servo response and the other two are for regulatory responses. An analytical design method is derived for the two disturbance rejection controllers by proposing the desired closed-loop complementary sensitivity functions. These two closed-loop controllers are considered in the form of proportional–integral-derivative (PID) controller cascaded with a second order lead/lag filter. The direct synthesis method is used to design the setpoint tracking controller. By virtue of the enhanced structure, the proposed control scheme decouples the servo response from the regulatory response in case of nominal systems i.e., the setpoint tracking controller and the disturbance rejection controller can be tuned independently. Internal stability of the proposed cascade structure is analyzed. Kharitonov’s theorem is used for the robustness analysis. The disturbance rejection capability of the proposed scheme is superior as compared to existing methods. Examples are also included to illustrate the simplicity and usefulness of the proposed method.
Evaluation of Anaerobic Fluidized Bed Reactor for treating Sugar mill effluen...IJERA Editor
Anaerobic treatment processes are credible options for providing sustainable treatment to biodegradable waste streams. The Anaerobic Fluidized Bed Reactor (AFBR) is an evolving process that requires waste specific design methodologies based on kinetics of the specific process. The research was precisely an experimental study on AFBR having23.56 litres of effective volume to evaluate its treatment performance and gas recovery in terms of Chemical Oxygen Demand (COD), Hydraulic Retention Time(HRT)and Organic Loading Rate (OLR). The synthetic sugar influent COD was variedfrom 1500 to 4000 mg/lit. The OLR for the operating flow rates were ranged from 1.36 to 28.8 Kg COD/m3.day for HRT varied from 3.2 to 24 hrs. The maximum COD removal efficiency is 90.06 at an operating OLR of 3.42 Kg COD/m3.day. The maximum biogas yield was observed at 0.28 m3/kg COD removed.
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).
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...Scientific Review SR
This research paper aims at investigating disturbance rejection associated with a highly oscillating
second-order process. The PD-PI controller having three parameters are tuned to provide efficient rejection of a
step input disturbance input. Controller tuning based on using MATLAB control and optimization toolboxes.
Using the suggested tuning technique, it is possible to reduce the maximum time response of the closed loop
control system to as low as 0.0095 and obtain time response to the disturbance input having zero settling time.
The effect of the proportional gain of the PD-PI controller on the control system dynamics is investigated for a
gain ≤ 100. The performance of the control system during disturbance rejection using the PD -PI controller is
compared with that using a second-order compensator. The PD-PI controller is superior in dealing with the
disturbance rejection associated with the highly oscillating second-order process
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller With ...Waqas Tariq
In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. Today, most of systems (e.g., robot manipulators) are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools (e.g., nonlinear sliding mode controller) are used in artificial intelligent control methodologies to design model free nonlinear robust controller with high performance (e.g., minimum error, good trajectory, disturbance rejection). Non linear classical theories have been applied successfully in many applications, but they also have some limitation. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on applied sliding mode controller in fuzzy logic theory to solve the limitation in fuzzy logic controller and sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the gain updating factor and sliding surface slope in PID like sliding mode fuzzy controller to have the best performance and reduce the limitation.
Combined ILC and PI regulator for wastewater treatment plantsTELKOMNIKA JOURNAL
Due to high nonlinearity with features of large time constants, delays, and interaction among variables, control of the wastewater treatment plants (WWTPs) is a very challenging task. Modern control strategies such as model predictive controllers or artificial neural networks can be used to deal with the non-linearity. Another characteristic of this system should be considered is that it works repetitively. Iterative learning control (ILC) is a potential candidate for such a demanding task. This paper proposes a method using ILC for WWTPs to achieve new results. By exploiting data from the previous iterations, the learning control algorithm can improve gradually tracking control performance for the next runs, and hence outperforms conventional control approaches such as feedback controller and model predictive control (MPC). The benchmark simulation model No.1-BSM1 has been used as a standard for performance assessment and evaluation of the control strategy. Control of the Dissolved Oxygen in the aerated reactors has been performed using the PD-type ILC algorithms. The obtained results show the advantages of ILC over a classical PI control concerning the control quality indexes, IEA and ISE, of the system. Besides, the conventional feedback regulator is designed in a combination with the iterative learning control to deal with uncertainty. Simulation results demonstrate the potential benefits of the proposed method.
Modified smith predictor based cascade control of unstable time delay processesISA Interchange
An improved cascade control structure with a modified Smith predictor is proposed for controlling open-loop unstable time delay processes. The proposed structure has three controllers of which one is meant for servo response and the other two are for regulatory responses. An analytical design method is derived for the two disturbance rejection controllers by proposing the desired closed-loop complementary sensitivity functions. These two closed-loop controllers are considered in the form of proportional–integral-derivative (PID) controller cascaded with a second order lead/lag filter. The direct synthesis method is used to design the setpoint tracking controller. By virtue of the enhanced structure, the proposed control scheme decouples the servo response from the regulatory response in case of nominal systems i.e., the setpoint tracking controller and the disturbance rejection controller can be tuned independently. Internal stability of the proposed cascade structure is analyzed. Kharitonov’s theorem is used for the robustness analysis. The disturbance rejection capability of the proposed scheme is superior as compared to existing methods. Examples are also included to illustrate the simplicity and usefulness of the proposed method.
Evaluation of Anaerobic Fluidized Bed Reactor for treating Sugar mill effluen...IJERA Editor
Anaerobic treatment processes are credible options for providing sustainable treatment to biodegradable waste streams. The Anaerobic Fluidized Bed Reactor (AFBR) is an evolving process that requires waste specific design methodologies based on kinetics of the specific process. The research was precisely an experimental study on AFBR having23.56 litres of effective volume to evaluate its treatment performance and gas recovery in terms of Chemical Oxygen Demand (COD), Hydraulic Retention Time(HRT)and Organic Loading Rate (OLR). The synthetic sugar influent COD was variedfrom 1500 to 4000 mg/lit. The OLR for the operating flow rates were ranged from 1.36 to 28.8 Kg COD/m3.day for HRT varied from 3.2 to 24 hrs. The maximum COD removal efficiency is 90.06 at an operating OLR of 3.42 Kg COD/m3.day. The maximum biogas yield was observed at 0.28 m3/kg COD removed.
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).
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...Scientific Review SR
This research paper aims at investigating disturbance rejection associated with a highly oscillating
second-order process. The PD-PI controller having three parameters are tuned to provide efficient rejection of a
step input disturbance input. Controller tuning based on using MATLAB control and optimization toolboxes.
Using the suggested tuning technique, it is possible to reduce the maximum time response of the closed loop
control system to as low as 0.0095 and obtain time response to the disturbance input having zero settling time.
The effect of the proportional gain of the PD-PI controller on the control system dynamics is investigated for a
gain ≤ 100. The performance of the control system during disturbance rejection using the PD -PI controller is
compared with that using a second-order compensator. The PD-PI controller is superior in dealing with the
disturbance rejection associated with the highly oscillating second-order process
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller With ...Waqas Tariq
In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. Today, most of systems (e.g., robot manipulators) are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools (e.g., nonlinear sliding mode controller) are used in artificial intelligent control methodologies to design model free nonlinear robust controller with high performance (e.g., minimum error, good trajectory, disturbance rejection). Non linear classical theories have been applied successfully in many applications, but they also have some limitation. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on applied sliding mode controller in fuzzy logic theory to solve the limitation in fuzzy logic controller and sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the gain updating factor and sliding surface slope in PID like sliding mode fuzzy controller to have the best performance and reduce the limitation.
Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC...Francisco Gonzalez-Longatt
DC microgrids are desired to provide the electricity for the remote areas which are far from the main grid. The microgrid creates the open horizontal environment to interconnect the distributed generation especially photovoltaic (PV). The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the microgrid and forced to introduce the controller for voltage stability. There are many control strategies to control the voltage of a DC microgrid in the literature. In this paper the proportional-integral-derivative (PID) and fuzzy logic PID (FL-PID) controller has been designed and compared in term of performance. Performance measures like maximum overshoot and settling time of FL-PID compared with the PID proved that the former is better controller. The controllers are designed and simulated in the MATLAB programming environment. The controllers has been tested for the real time data obtained from Pecan Street Project, University of Texas at Austin USA.
DESIGN OF PID CONTROLLERS INTEGRATOR SYSTEM WITH TIME DELAY AND DOUBLE INTEGR...ijcisjournal
In this paper first we investigate optimal PID control of a double integrating plus delay process and compare with the SIMC rules. What makes the double integrating process special is that derivative action is actually necessary for stabilization. In control, there is generally a trade-off between performance and robustness, so there does not exist a single optimal controller. However, for a given robustness level (here defined in terms of the Ms-value) we can find the optimal controller which minimizes the performance J (here defined as the integrated absolute error (IAE)-value for disturbances). Interestingly, the SIMC PID controller is almost identical to the optimal pid controller. This can be seen by comparing the paretooptimal curve for J as a function of Ms, with the curve found by varying the SIMC tuning parameter Tc. Second, design of Proportional Integral and Derivative (PID) controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. The performances of the proposed controllers are compared with the
controllers designed by recently reported methods. The robustness of the proposed controllers for the uncertainty in model parameters is evaluated considering one parameter at a time using Kharitonov’s theorem. The proposed controllers are applied to various transfer function models and to non linear model of isothermal continuous copolymerization of styrene-acrylonitrile in CSTR. An experimental set up of tank with the outlet connected to a pump is considered for implementation of the PID controllers designed by the three proposed methods to show the effectiveness of the methods.
Robustness enhancement study of augmented positive identification controller ...IAESIJAI
The dissolved oxygen concentration in the wastewater treatment process
(WWTP) must remain in a specific range while the factory operates. The
augmented positive identification (PID) controller with a nonlinear element
(sigmoid function) is proposed to assure stability and reduce uncertainties in
the wastewater direct reuse/recycling model. The nonlinear controller gains
(PID controller with sigmoid function) for uncertain wastewater treatment
processes are tuned using the particle swarm optimization (PSO) technique.
The proposed robust method for controlling wastewater treatment processes
has good robustness during model mismatching, reduces treatment time
compared to traditional positive identification (PID) controllers tuned by
PSO, is easy to apply, and has good performance, according to simulation
results.
Movie Review GuidelinesI. Introduction· Genre · Movie Titl.docxroushhsiu
Movie Review Guidelines
I. Introduction
· Genre
· Movie Title
· Director
· Principal location
· Mention your opinion –use a description
· Include the top actor
II. Brief Summary of the Plot
III. Your analysis of the movie’s component’s
· The theme
· The directing
· The acting
· Visual elements
IV. Conclusion
· return to your opinion of the movie
· do you recommend the movie or skip this movie
ChE 460 Literature Review Due: Dec 03, 2019, 11 AM
Paper Review (50 pts.)
Read the paper: “Dissolved oxygen control of the activated sludge wastewater treat-
ment process using model predictive control,” Computers and Chemical Engineering, vol
32, 1270-1278, 2008, and write a note about that paper. Please submit the printed hard
copy. Handwriting version is not accepted.
You need to show and discuss the following contents. Please do not copy and paste
any sentences from that paper.
� Motivations (10 pts.): why is this work important from the industrial or academic
perspective?
� Methodologies (10 pts.): including the modeling and controller design methods.
� Your questions about the method in this paper (10 pts.): Model predictive control
is the state-of-the-art technique for industrial automation. It is very normal that
students cannot easily understand its concept. List all your questions on this
method.
� Comments with critical thinking (10 pts.): List advantages & drawbacks of the
proposed method. Provide your suggestions or possible improvement.
Format Requirement (10 pts.): Print your review on A4 paper, at least two full
pages (not including references), single space, Times New Roman 12, margins 1 inch
on all sides, no figure. Please list references in the end of this review (You can follow
the reference format of Computers and Chemical Engineering). If your report does not
meet above requirements, then you can obtain at most 1 point in this part.
1
Ashraf Al Shekaili
Chemical Engineering 460
Dr. Yu Yang
Literature Review
Dissolved Oxygen Control of The Activated Sludge Wastewater Treatment Process Using Model Predictive Control
The process of waste water treatment is very complex and hard to control due to non-linear behavior system. This happens because of the variation in composition of the incoming wastewater along with disturbances in flow and load. Many control strategies were proposed to control the process; however, their evaluation is difficult due to shortage in the standard evaluation criteria.
The dissolved oxygen in the aerobic reactors play a role in the activity of microorganisms that live in activated sludge. High concentration of dissolved oxygen is required to feed enough oxygen to microorganisms in the sludge so the organic matters will be decomposed. However, excessive dissolved oxygen may lead to increase the operational cost because of high energy consumption.
Building a model to control a process is extremely important for any industry because industries have to meet the effluent requirements of ...
A Robust Fuzzy Logic Control of Two Tanks Liquid Level ProcessINFOGAIN PUBLICATION
An attempt has been made in this paper to analyze the efficiency of Fuzzy Logic, PID controllers on Non Interacting Two Tanks (Cylindrical) Liquid Level Process. The liquid level process exhibits Nonlinear square root law flow characteristics. The control problem formulated as level in second tank is controlled variable and the inlet flow to the first tank is manipulated variable. The PID Controller is designed based on Internal Model Control (IMC) Method. The Artificial Intelligent Fuzzy logic controller is designed based on six rules with Gaussian and triangular fuzzy sets. MATLAB - Simulink has been used to simulate and verified the mathematical model of the controller. Simulation Results show that the proposed Fuzzy Logic Controller show robust performance with faster response and no overshoot, where as the conventional PID Controller shows oscillations responses for set point changes. Thus, the Artificial Intelligent FLC is founded to give superior performance for a Non linear problem like two tanks. This paper will help the method suitable for research findings concerning on two tank liquid level system.
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
DESIGN, IMPLEMENTATION, AND REAL-TIME SIMULATION OF A CONTROLLER-BASED DECOUP...IAEME Publication
In this paper, dynamic decoupling control design strategies for the MIMO Continuous Stirred Tank Reactor (CSTR) process characterised by nonlinearities, loop interaction and the potentially unstable dynamics, are presented. Simulations of the behavior of the closed loop decoupled system are performed in Matlab/Simulink. Software transformation technique is proposed to build a real-time module of the developed in Matlab/Simulink environment software modules and to transfer it to the real-time environment of TwinCAT 3.1 software of the Beckhoff PLC. The simulation results from the investigations done in Simulink and TwinCAT 3.1 software platforms have shown the suitability and the potentials of the method for design of the decoupling controller and of merging the Matlab/Simulink control function blocks into the TwinCAT 3.1 function blocks in real-time. The merits derived from such integration imply that the existing software and its components can be re-used. The paper contributes to implementation of the industrial requirements for portability and interoperability of the PLC software.
Design of a model reference adaptive PID control algorithm for a tank system IJECEIAES
This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller.
In this paper, a Matlab based GUI and Propotinal Integral Dervative (PID) controller is designed to automatically regulate the flow-rate of the circulating fluid. When fluids are transported over long distances, the pressure and flow rate have to be monitored remotely in a control room. Using an HMI or Control Panels the flow rate can be increased or decreased to compensate for pressure drops or disturbances. This paper attempts to demonstrate such an Industrial Control Operation in a scaled-down environment. A Graphical User Interface or GUI is constructed which enables the Operator to monitor, as well as control an electronically actuated Control Valve which can efficiently regulate the flow-rate. Automatic operations have also been implemented using a PID controller algorithm, which tries to track the Set-point in Real-time.
IMC Based Fractional Order Controller for Three Interacting Tank ProcessTELKOMNIKA JOURNAL
In model based control, performance of the controlled plant considerably depends on the
accuracy of real plant being modelled. In present work, an attempt has been made to design Internal
Model Control (IMC), for three interacting tank process for liquid level control. To avoid complexities in
controller design, the third order three interacting tank process is modelled to First Order Plus Dead Time
(FOPDT) model. Exploiting the admirable features of Fractional Calculus, the higher order model is also
modelled to Fractional Order First Order Plus Dead Time (FO-FOPDT) model, which further reduces the
modelling error. Moving to control section, different IMC schemes have been proposed based on the order
of filter. Various simulations have been performed to show the greatness of Fractional order modelled
system & fractional order filters over conventional integer order modelled system & integer order filters
respectively. Both for parameters estimation of reduced order model and filter tuning, Genetic Algorithm
(GA) is being applied.
Controller Tuning for Integrator Plus Delay Processes.theijes
A design method for PID controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. Analytical expressions for PID controllers are derived for several common types of process models, including first order and second-order plus time delay models and an integrator plus time delay model. Here in this paper, a simple controller design rule and tuning procedure for unstable processes with delay time is discussed. Simulation examples are included to show the effectiveness of the proposed method
—Continuous Stirred Tank Reactor (CSTR) here is
considered as a nonlinear process. The CSTR is widely used in
many chemical plants. Due to changes in process parameters the
accuracy of final product can be reduced. In order to get accurate
final product the faults developed in CSTR during the chemical
reaction need to be diagnosed. If not, the faults may lead to
degrade the performance of the system. For this purpose there
are various fault diagnosis methods are to be considered. Among
the methods, the neural network predictive controller can be used
to detect faults in CSTR. Servo response is performed to
understand the behavior of CSTR. By detecting various faults
and with suitable control techniques, the accuracy of the
desirable products in CSTR can be improved
Decentralised PI controller design based on dynamic interaction decoupling in...IJECEIAES
An enhanced method for design of decenralised proportional integral (PI) controllers to control various variables of flotation columns is proposed. These columns are multivariable processes characterised by multiple interacting manipulated and controlled variables. The control of more than one variable is not an easy problem to solve as a change in a specific manipulated variable affects more than one controlled variable. Paper proposes an improved method for design of decentralized PI controllers through the introduction of decoupling of the interconnected model of the process. Decoupling the system model has proven to be an effective strategy to reduce the influence of the interactions in the closed-loop control and consistently to keep the system stable. The mathematical derivations and the algorithm of the design procedure are described in detail. The behaviour and performance of the closed-loop systems without and with the application of the decoupling method was investigated and compared through simulations in MATLAB/Simulink. The results show that the decouplers - based closedloop system has better performance than the closed-loop system without decouplers. The highest improvement (2 to 50 times) is in the steady-state error and 1.2 to 7 times in the settling and rising time. Controllers can easily be implemented.
Performance analysis of a liquid column in a chemical plant by using mpceSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Design of Controllers for Liquid Level ControlIJERA Editor
The liquid level control system is commonly used in many process control applications. The aim of the process
is to keep the liquid level in the tank at the desired value. The conventional proportional-integral-derivative
(PID) controller is simple, reliable and eliminates the error rate but it cannot handle complex problems. Fuzzy
logic controllers are rule based systems which simulates human behavior of the process. The fuzzy controller is
combined with the PID controller and then applied to the tank level control system. This paper proposes Inverse
fuzzy with fuzzy logic controller for controlling liquid level system for a plant. This paper also compares the
transient response as well as error indices of PID, Fuzzy logic controller, inverse fuzzy controllers. The
responses of the controllers are verified through simulation. From the simulation results, it is observed that
inverse fuzzy-PID controller gives the superior performance than the other controllers. The inverse fuzzy-PID
controller gives better performance than the PID and fuzzy controller in terms of overshoot and settling time.
Performance analysis is carried out with Liquid Flow Control System Design with Fuzzy logic controller.
Results are evaluated by comparing the response time of conventional PID, fuzzy logic and Inverse fuzzy
controller. Comparative analysis of the performance of different controllers is done in MATLAB and Simulink.
Similar to HYBRID FUZZY LOGIC AND PID CONTROLLER FOR PH NEUTRALIZATION PILOT PLANT (20)
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
Assure Contact Center Experiences for Your Customers With ThousandEyes
HYBRID FUZZY LOGIC AND PID CONTROLLER FOR PH NEUTRALIZATION PILOT PLANT
1. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
DOI : 10.5121/ijfls.2013.3201 1
HYBRID FUZZY LOGIC AND PID CONTROLLER FOR
PH NEUTRALIZATION PILOT PLANT
Oumair Naseer1
, Atif Ali Khan2
1,2
School of Engineering, University of Warwick, Coventry, UK,
o.naseer@warwick.ac.uk
atif.khan@warwick.ac.uk
ABSTRACT
Use of Control theory within process control industries has changed rapidly due to the increase complexity
of instrumentation, real time requirements, minimization of operating costs and highly nonlinear
characteristics of chemical process. Previously developed process control technologies which are mostly
based on a single controller are not efficient in terms of signal transmission delays, processing power for
computational needs and signal to noise ratio. Hybrid controller with efficient system modelling is essential
to cope with the current challenges of process control in terms of control performance. This paper presents
an optimized mathematical modelling and advance hybrid controller (Fuzzy Logic and PID) design along
with practical implementation and validation of pH neutralization pilot plant. This procedure is
particularly important for control design and automation of Physico-chemical systems for process control
industry.
KEYWORDS
Hybrid Control, PID Controller, Fuzzy Logic Controller, pH Neutralization Pilot Plant, Process Control
and Automation Technologies, Control Theory and Applications.
1. INTRODUCTION
Control system design for ܪ neutralization pilot plant has a long history, due to its non-linear
characteristics, uncertainty and large number of requirements from environment legislation which
revised constantly. Most of the classical control techniques are developed on the bases of linear
theory. When these techniques are applied to the chemical systems having kinetic reactions and
thermodynamic relationships, they do not provide the adequate system performance and hence
fails to capture the entire operating range. ܪ neutralization process mainly consists of ܪ
measurement of an acid-base chemical reaction in which hydrogen ions and hydroxide ions are
neutralized or combined with each other to form water, while the other ions involved remained
unchanged. Acid is a substance that ionises in water to produce hydrogen ions and base is a
substance which ionises in water to produce hydroxyl ions. The characteristics of acid-base
neutralization reaction are normally represented by a titration curve. Fig.1 below provides the
information about the equilibrium point [1, 2], the type of acid-based (strong/weak) involved and
the total volume or amount of substances involved at the end of the titration process. The S-
shaped curve shown in Fig. 1 depends on the concentration and composition of acid and based
used in the process reaction. It can be seen that at ܪ value of 7, a small change in input
produces a large change in output, which provides the significance of controlling ܪ Value [3,
33, and 35].
2. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
2
Figure 1: Titration curve for acid-base process reaction.
Scale of measuring acidity of the system ranges between ܪ values 1 to 14. At room
temperature 25°C, if the ܪ value is less than 7, the mixed solution has higher concentration of
hydrogen ions; hence it is acidic in nature. If ܪ value is greater than 7 then the mixed solution
has higher concentration of hydroxyl ions and is alkaline/base in nature. However, if ܪ value is
7 then the mixed solution is neutral. For industrial safety, all waste water should maintain a ܪ
level of 7±1. ܪ control loop mainly consists of (i) an open loop type of control scheme; in
which the control valve opening is kept at certain positions for specific time durations, (ii) a
feedback control scheme; which involves a direct relationship between the control valve opening
and the ܪ value in the process, and (iii) a feed-forward control scheme; in which controller will
compensate for any measured disturbance before it affects the process (i.e. the ܪ value in the
case of this application). In the past few years, several control strategies have been developed in
the process control to improve the performance of the system. This is achieved by efficiently
defining an optimal ܪ pilot plant model within the control structure and this trend keep on
increasing day by day. Classical control schemes such as Proportional-Integral-Derivative (PID)
control, depends on the optimal tuning of control parameters which are proportional gain, integral
gain and derivate gain. On the other hand, performance of fuzzy logic control depends on the
vigilant selection of the membership function for the input set and output set parameters.
Traditional PID or Fuzzy logic based controller cannot provide optimal performance as compared
to the combination of PID and Fuzzy logic or the combination of adaptive neural network control
and fuzzy logic control. In control system architecture, integration of such hybrid control
provides new trend towards the realization of intelligent systems. In this paper an optimal
mathematical modelling of ܪ neutralization pilot plant with Hybrid controller (Fuzzy logic and
PID) design, implementation and validation is presented.
2. RELATED WORK
A rigorous and generally applicable method of deriving dynamic equations for ܪ
neutralization in Continuous Stirred Tank Reactors (CSTRs) is presented in [5, 6]. In [4], author
uses the same model and provides a more optimal solution for adaptive control for ܪ
neutralization process control. A new concept concerning the averaging ܪ value of a mixture
3. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
3
of solutions is introduced in [7, 8]. It gives the idea to utilize reaction invariant variables in
calculating the ܪ value of mixtures of solutions, instead of using a direct calculation involving a
simple averaging of hydrogen ions. In [9], authors introduced a systematic method for the
modelling of dynamics of the ܪ neutralization process. They used a hypothetical species
estimation to obtain the inverse titration curve so that overall linearization of the control loop can
be utilized. Fundamental properties of continuous ܪ control are investigated in [10] using
Proportional-Integral-Derivative (PID) controller. Nonlinear adaptive control for ܪ
neutralization pilot plant is presented in [11]. In [12, 13] author evaluated the performance of the
system on a bench scale ܪ neutralization system in order to gain additional insight in terms of
the practical application. A feedback based nonlinear controller is developed in [14, 30] by
applying an input-output linearization approach to a reaction invariant model of the process by
using a Proportional Integral (PI) controller which utilizes an open-loop nonlinear state observer
and a recursive least squares parameter estimator. A mathematical modelling of the ܪ
neutralization process in a continuous stirred tank reactor which is based on a physico-chemical
approach is presented in [15]. In [16], an adaptive ܪ control for a chemical waste water
treatment plant is presented. The same approach is extended in [17]. In [18], author presented a
new approach for an adaptive combined feedback-feedforward control method for ܪ control
which was based on a quantitative physico-chemical analysis of the ܪ neutralization process.
However, this work has relatively high signal to noise ratio. Similar approach is further extended
by using an adaptive ܪ control algorithm in [19]. A comparison of linear and non-linear
control for ܪ neutralization plant is presented in [20]. In [21, 22], author presents the
investigation of the controller performance, such as tracking of the lime flow-rate set point,
investigation of different conditions of normal process operation and operation without ignition.
A new approach to ܪ control which utilizes an identification reactor to incorporate the
nonlinearities of the ܪ neutralization process is described in [23]. Indirect adaptive nonlinear
control of ܪ neutralization plant is presented in [24]. In [25, 30-32] practical control design
issues for a ܪ neutralization is investigated with the objective to design an online
identification method based on use of an extended Kalman filter. However, this work has low
processing power for high computational systems. In [26, 34] author outlines the framework of
the controller and develops a fuzzy relational model which is based on a fuzzy logic approach.
Similar approach is extended in [27]. In [28, 29], author outlines a technique in which the genetic
algorithm approach is employed to alter membership functions in response to changes in the
process. In [36] a fuzzy fed PID control of non-linear system is presented but this work doesn’t
capture the system’s characteristics in terms of signal transmission delays.
3. ܪ NEUTRALIZATION PILOT PLANT ARCHITECTURE
The architecture diagram of ܪ neutralization pilot plant is shown in Fig 2. This plant is
assembled by using the state of the art industrial instruments and measuring systems.
4. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
Figure 2
This pilot plant consists of three tanks; VE210 is the alkaline tank, VE200 is the acidic tank and
VE220 is the mixer tank. P210 and P200 are the pumps which are used to get the desired amount
of acid and base in the mixer tank. FT 210 and FT 200 are the
flow transmitters is to transmit the precisely measured amount of acid and base respectively to the
central computing system. Two control valves CV210 and CV200 are
and acid pipelines respectively to control the flow of the acid and base getting into the mixer tank.
These control valves are fully digital and are operated by using electrical signals. The mixer tank
contains a continuous steering motor, which keeps the solution in the mixer tank at t
state. Because whenever a corresponding amount of acid or base is added into the mixer tank, a
certain amount of time is required in order for the chemical reaction to take place. Continuous
steering ensures the stability of
architecture diagram is shown in Fig. 3.
Figure
Inputs to the system are the data coming from the
sensors, conductivity monitor sensors,
actuators that control the flow valves. Processor senses the
mixer tank and controls the valves of the acid and base tank accordingly to maintain the
level equals to 7.
International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
2: ܪ Neutralization pilot plant architecture.
This pilot plant consists of three tanks; VE210 is the alkaline tank, VE200 is the acidic tank and
VE220 is the mixer tank. P210 and P200 are the pumps which are used to get the desired amount
of acid and base in the mixer tank. FT 210 and FT 200 are the flow transmitters. The job of the
flow transmitters is to transmit the precisely measured amount of acid and base respectively to the
central computing system. Two control valves CV210 and CV200 are connected to the alkaline
to control the flow of the acid and base getting into the mixer tank.
These control valves are fully digital and are operated by using electrical signals. The mixer tank
contains a continuous steering motor, which keeps the solution in the mixer tank at t
state. Because whenever a corresponding amount of acid or base is added into the mixer tank, a
certain amount of time is required in order for the chemical reaction to take place. Continuous
steering ensures the stability of ܪ level at each point in the mixer tank. The overall system
architecture diagram is shown in Fig. 3.
Figure 3: Overall system architecture.
Inputs to the system are the data coming from the ܪ neutralization pilot plant i.e. the flow meter
sensors, conductivity monitor sensors, ܪ value meter. The corresponding outputs are the
actuators that control the flow valves. Processor senses the ܪ level of the mixed solution in
tank and controls the valves of the acid and base tank accordingly to maintain the
4
This pilot plant consists of three tanks; VE210 is the alkaline tank, VE200 is the acidic tank and
VE220 is the mixer tank. P210 and P200 are the pumps which are used to get the desired amount
flow transmitters. The job of the
flow transmitters is to transmit the precisely measured amount of acid and base respectively to the
connected to the alkaline
to control the flow of the acid and base getting into the mixer tank.
These control valves are fully digital and are operated by using electrical signals. The mixer tank
contains a continuous steering motor, which keeps the solution in the mixer tank at the uniform
state. Because whenever a corresponding amount of acid or base is added into the mixer tank, a
certain amount of time is required in order for the chemical reaction to take place. Continuous
level at each point in the mixer tank. The overall system
neutralization pilot plant i.e. the flow meter
value meter. The corresponding outputs are the
level of the mixed solution in
tank and controls the valves of the acid and base tank accordingly to maintain the ܪ
5. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
5
4. MATHEMATICAL MODELLING OF ܪ NEUTRALIZATION PILOT PLANT
Acid used in mixer tank is ܪଶܱܵସ and alkaline used is ܱܰܽܪ. Table.1 shows the various process
variables used in the neutralization plant.
No: Process Variables Instruments
1 ܪ value form mixer tank ܪ Meter
2 Concentration of alkaline tank Conductivity Meter
3 Concentration of acid tank Conductivity Meter
4 Flow rate of alkaline stream Flow meter
5 Flow rate of acid stream Flow meter
Table 1: Process variables for ܪ neutralization pilot plant.
Mixer Tank is the continuous steering tank reactor. F1 is the flow rate of the acid and F2 is the
flow rate of the alkaline. C1 is the concentration of acid and C2 is the concentration of alkaline.
The mathematical equation for mixer tank can be defined as the rate of accumulation of non-
reactant species (within element volume) is equal to the rate of flow of non-reactant species
(into element volume) minus the rate of flow of non-reactant species (out of element volume).
This can be written as:
ܸ
ௗఈ
ௗ௧
= ܨଵܥଵ − ሺܨଵ + ܨଶሻߙ (1)
ܸ
ௗఉ
ௗ௧
= ܨଶܥଶ − ሺܨଵ + ܨଶሻߚ (2)
Where, V is the volume of the tank. ߙ and ߚ are the non-reactant components of the system for
acid and alkaline respectively. They are defined as:
ߙ = ሾܪଶܱܵସሿ + ሾܱܵܪସ
ି
ሿ + ൣܱܵସ
ିଶ
൧ (3)
ߚ = ሾܰ
ା
ሿ (4)
Based on the electro-neutrality condition, sum of all the positive charges is equal to the sum of all
negative charges and can be written as:
ሾܰ
ା
ሿ + ሾܪାሿ = ሾܱܪିሿ + ሾܱܵܪସ
ି
ሿ + 2ሾܱܵସ
ିଶ
ሿ (5)
The equilibrium constant expression for water and HଶSOସ are as follows:
ܭଵ =
ሾுశሿሾுௌைర
షሿ
ுమௌைర
(6)
6. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
6
ܭଶ =
ሾுశሿൣௌைర
షమ
൧
ுௌைర
ష (7)
ܭௐ = ሾܪାሿሾܱܪିሿ (8)
ܭௐ is the constant ionic product of water and is equal to 1.0੨10ଵସ
. ܭଵ and ܭଶ are the two acid
dissociation constants for sulphuric acid with ܭଵ = 1.0੨10ଷ
and ܭଶ = 1.2੨10ିଶ
. ܪ value of
the solution can be calculated by using the following equation:
ܲܪ = − logଵሾܪାሿ (9)
Equation 5 can be solved for the value of Hydrogen ions H+ by using (6, 7, 8, and 9) and can be
written as:
ሾܪା
ሿସ
+ ܽଵሾܪା
ሿଷ
+ ܽଶሾܪା
ሿଶ
+ ܽଷሾܪା
ሿଵ
+ ܽସ (10)
ܽଵ = ܭଵ + ߚ
ܽଶ = ߚܭଵ + ܭଵܭଶ − ܭௐ − ܭଵߙ
ܽଷ = ߚܭଵܭଶ − ܭଵܭௐ − 2ܭଵܭଶߙ
ܽସ = −ܭଵܭଶܭௐ
Equation 10 is the Physico-chemical ܪ neutralization equation for the mixer tank. The block
diagram of the resulting plant is shown in the Fig. 4.
Figure 4: Mathematical model of ܪ neutralization pilot plant.
Fig. 5 shows the dynamic response of the system which is clearly a non-linear system. ܪ value
starts from 3. The variation of ܪ value from 3-4 is low and from 4-6 variation is very high.
However, from ܪ values 6-8 the slope is quite linear and the variation is moderate. From ܪ
values 8-10, variation is again higher and finally from ܪ values 10-12 variation is low.
7. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
Figure 5: Dynamic response of
5. PROPORTIONAL INTEGRAL DERIVATIVE
DESIGN
The characteristics curves for flow rates of acid and alkaline valves are shown in
evident that flow control for up scaling (opening of valve) and down scaling (closing of valve) is
not the same. The error varies from 2% to 6%. PID controller is designed and tuned to capture
these variations [3].
Figure
Firstly, the proportional gain is set to a minimum value and the other parameters integral and
derivative terms are set to give zero action. The proportional gain is then gradually increased
oscillations start to appear in the measured closed
so that the oscillations maintain constant amplitude. The value of gain that is used to achieve this
condition is termed as ultimate proportional gai
on Table 2 proportional gain Kp = 10.8, integral gain Ki = 0.65 and derivative gain Kd = 44.5.
International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
: Dynamic response of ܪ neutralization pilot plant.
NTEGRAL DERIVATIVE (PID) FLOW-RATE
The characteristics curves for flow rates of acid and alkaline valves are shown in
evident that flow control for up scaling (opening of valve) and down scaling (closing of valve) is
not the same. The error varies from 2% to 6%. PID controller is designed and tuned to capture
Figure 6: Flow-rates of acid and base streams.
Firstly, the proportional gain is set to a minimum value and the other parameters integral and
derivative terms are set to give zero action. The proportional gain is then gradually increased
oscillations start to appear in the measured closed-loop system response. The gain is then adjusted
so that the oscillations maintain constant amplitude. The value of gain that is used to achieve this
condition is termed as ultimate proportional gain with value (G=18) at the period (P=33). Based
on Table 2 proportional gain Kp = 10.8, integral gain Ki = 0.65 and derivative gain Kd = 44.5.
7
RATE CONTROL
The characteristics curves for flow rates of acid and alkaline valves are shown in Fig. 6. It is
evident that flow control for up scaling (opening of valve) and down scaling (closing of valve) is
not the same. The error varies from 2% to 6%. PID controller is designed and tuned to capture
Firstly, the proportional gain is set to a minimum value and the other parameters integral and
derivative terms are set to give zero action. The proportional gain is then gradually increased until
loop system response. The gain is then adjusted
so that the oscillations maintain constant amplitude. The value of gain that is used to achieve this
n with value (G=18) at the period (P=33). Based
on Table 2 proportional gain Kp = 10.8, integral gain Ki = 0.65 and derivative gain Kd = 44.5.
8. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
8
Type of Controller P PI PID
Proportional Kp 0.5 G 0.45G 0.6G
Integral Ki - 1.2Kp/P 2Kp/P
Differential Kd - - KpP/8
Table 2: Ziegler-Nichlos tuning formula for a closed loop system.
6. FUZZY LOGIC CONTROLLER DESIGN
Fuzzy logic controller mainly consists of three parts: (i) Fuzzification: process of converting
system inputs (process variables) into grades of membership for linguistic of fuzzy sets, (ii)
Fuzzy interference: mapping input space to output space using membership functions, logic
operations and if-then rule statements and (iii) Defuzzification: process of producing quantifiable
results (control valve inputs for PID controller) in the light of given fuzzy sets and membership
degree. Overall performance of the fuzzy logic controller depends on the selection of the
membership function of input and output sets.
The set point of the desired ܪ value is entered manually while other process control variables
are controlled automatically based on the information (feedback) coming from the plant output.
The job of the fuzzy logic controller is to maintain the corresponding ܪ value while
manipulating the process control variables. When the current ܪ value is less than the desired
value, Fuzzy logic controller sets a new point for the PID valve flow rate controller. The new
value of current set point depends upon the difference between the current ܪ value of the plant
reactor and the desired ܪ value. The overall diagram of the system is shown in the Fig. 7.
Figure 7: Logical diagram of fuzzy logic and PID controller.
Table 3 shows the membership function description and the parameters for fuzzy logic input set.
The mid condition is positioned between -1 and 1 to ensure the smoothness of the desired ܪ
value and to make certain that the zero offset for the steady state is achievable. The overall
performance of the system is determined by the input and output sets membership functions.
9. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
9
.
Symbols Descriptions Type Parameters
NXL Negative Extra Large Trapezoid -5.0 -5.0 -4.0 -2.0
NL Negative Large Triangle -3.0 -2.0 -1.0
NM Negative Medium Triangle -2.0 -1.25 -0.5
NS Negative Small Triangle -1.0 -0.5 0
Z Zero Triangle -0.5 0 0.5
PS Positive Small Triangle 0 0.5 1.0
PM Positive Medium Triangle 0.5 1.25 2.0
PL Positive Large Triangle 1.0 2.0 3.0
PXL Positive Extra Large Trapezoid 2.0 4.0 5.0 5.0
Table 3: Membership function description and parameters for input set
The entire range is divided into nine levels with the value ranging from -5.0 to 5.0. Fig. 8 further
demonstrates the functionality of the input sets of fuzzy logic controller.
Figure 8: Demonstration of membership function of input set.
Fig. 9 shows the membership functions for the output set of Fuzzy logic controller. Output set is
also divided into nine levels. The output set in this case determines the output for the PID
controller (whether to increase the ܪ value by increasing the flow-rate of acid or to decrease
the ܪ value by decreasing the flow-rate of base and vice versa) and to provide a reasonable
time response.
10. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
10
Table 4, shows the membership function description and parameters for output set. The middle
range is set from -20 to 20, so that the variation in mid-condition remains minute.
Symbols Descriptions Type Parameters
ONXL Negative Extra Large Trapezoid -100 -100 -60 -45
ONL Negative Large Triangle -50 -40 -30
ONM Negative Medium Triangle -35 -25 -15
ONS Negative Small Triangle -20 -10 0
OZ Zero Triangle -0.5 0 0.5
OPS Positive Small Triangle 0 10 20
OPM Positive Medium Triangle 15 25 35
OPL Positive Large Triangle 30 40 50
OPXL Positive Extra Large Trapezoid 45 60 100 100
Table 4: Membership function description and parameters for output set.
Table 5 defines the relationship between the input set and output set parameters of the fuzzy logic
controller. This is one-to-one function for every input set parameter there is a corresponding
output set variable.
No: Statement
Error in
ࡴ
Value
Statement
Manipulated
variables for
PID controller
1 IF NXL THEN ONXL
2 IF NL THEN ONL
3 IF NM THEN ONM
4 IF NS THEN ONS
5 IF Z THEN OZ
6 IF PS THEN OPS
7 IF PM THEN OPM
8 IF PL THEN OPL
9 IF PXL THEN OPXL
Table 5: If-then rules statement description of membership function and parameters for input and output
sets.
11. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
7. EXPERIMENTS
First experiment is carried out to investigate the overall performance of the hybrid fuzzy logic
and PID controller with the introduction of a static set point (
0.052M of H2SO4 is mixed with 0.052M of NaOH. These are the typical values for this kind of
system. Two step changes are made, first at the
This experiment is useful in determining the rise time (how long will it take for the output to
follow the desired input) and overall response time of the system. Second experiment is carried
out to investigate the robustness (to determine whether the output of the system is able to track
the input or not) of hybrid Fuzzy logic and PID controller. In this experiment, different set points
are introduced at different time steps in the form of a sq
set to 1.5 and period is configured to 600 sec. The random range of values changes from
to ܪ = 10. The initial ܪ vale is set to 7. The concentration v
0.051M and 0.0489M respectively. Third experiment is performed to compare the performance of
Hybrid Fuzzy Logic and PID controller against Fuzzy Logic controller. For this experiment,
value is varied from 6 to 10 at the regular intervals and the performance of both controllers is
observed.
8. RESULTS
Fig. 10, show the results of the first experiment. Initially
sec, the input of the system cha
sec, output reaches to ܪ = 10. However, the output is delayed because the flow rate of PID
controller depends on the mechanical flow valves of acid and base streams. At ti
the input drops to ܪ = 7 but output
to ܪ = 7 at 660 sec. Rise and fall time delays of the system are different because the control
valve have different rise time and fall time.
Figure 9: Experiment
Fig. 11 shows the result of the second experiment. Output of the system follows the input square
wave which shows the robustness of the controller. When input
output follows the input but with the minute delay. This delayed is because the
require certain amount of time to maintain the desired flow rate.
International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
First experiment is carried out to investigate the overall performance of the hybrid fuzzy logic
controller with the introduction of a static set point (ܪ value = 7). For this experiment,
0.052M of H2SO4 is mixed with 0.052M of NaOH. These are the typical values for this kind of
system. Two step changes are made, first at the ܪ value of 7 and second at the ܪ
This experiment is useful in determining the rise time (how long will it take for the output to
follow the desired input) and overall response time of the system. Second experiment is carried
ut to investigate the robustness (to determine whether the output of the system is able to track
the input or not) of hybrid Fuzzy logic and PID controller. In this experiment, different set points
are introduced at different time steps in the form of a square wave. The amplitude of the wave is
set to 1.5 and period is configured to 600 sec. The random range of values changes from
vale is set to 7. The concentration values for acid and base are set to
0.051M and 0.0489M respectively. Third experiment is performed to compare the performance of
Hybrid Fuzzy Logic and PID controller against Fuzzy Logic controller. For this experiment,
from 6 to 10 at the regular intervals and the performance of both controllers is
Fig. 10, show the results of the first experiment. Initially ܪ value is set to 7. At time step 300
sec, the input of the system changes to ܪ = 10. Output follows the input and at time step 380
= 10. However, the output is delayed because the flow rate of PID
controller depends on the mechanical flow valves of acid and base streams. At time step 600 sec,
= 7 but output ܪ value starts to drop down at 610 sec and settles down
= 7 at 660 sec. Rise and fall time delays of the system are different because the control
valve have different rise time and fall time.
: Experiment-1, performance of hybrid controller.
second experiment. Output of the system follows the input square
wave which shows the robustness of the controller. When input ܪ value varies from 7 to 10,
output follows the input but with the minute delay. This delayed is because the control valves
require certain amount of time to maintain the desired flow rate.
11
First experiment is carried out to investigate the overall performance of the hybrid fuzzy logic
value = 7). For this experiment,
0.052M of H2SO4 is mixed with 0.052M of NaOH. These are the typical values for this kind of
ܪ value of 10.
This experiment is useful in determining the rise time (how long will it take for the output to
follow the desired input) and overall response time of the system. Second experiment is carried
ut to investigate the robustness (to determine whether the output of the system is able to track
the input or not) of hybrid Fuzzy logic and PID controller. In this experiment, different set points
uare wave. The amplitude of the wave is
set to 1.5 and period is configured to 600 sec. The random range of values changes from ܪ = 6
alues for acid and base are set to
0.051M and 0.0489M respectively. Third experiment is performed to compare the performance of
Hybrid Fuzzy Logic and PID controller against Fuzzy Logic controller. For this experiment, ܪ
from 6 to 10 at the regular intervals and the performance of both controllers is
value is set to 7. At time step 300
10. Output follows the input and at time step 380
= 10. However, the output is delayed because the flow rate of PID
me step 600 sec,
value starts to drop down at 610 sec and settles down
= 7 at 660 sec. Rise and fall time delays of the system are different because the control
second experiment. Output of the system follows the input square
value varies from 7 to 10,
control valves
12. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
Figure 10: Experiment
Fig. 12, Shows the result of third experiment. It can be seen that from time intervals
seconds, hybrid controller is more efficient in tracking the input (set points) than Fuzzy Logic
controller. Also from time interval 500
logic controller.
Figure 11: Experiment-3, comparison of Fuzzy Logic controller against Hybrid Fuzzy Logic and PID
9. CONCLUSION AND FUTURE
This paper presents a hybrid control (PID and fuzzy logic controller) for
plant. It covers the entire operating range and is more robust against the uncertainty
variation). It is noticed that proposed hybrid controller is more stable as compared to the Fuzzy
Logic controller. Process modelling approach adopted in this paper is
chemical principles and fundamental laws. A conventional mathematical modelling process is
incorporated. Practical tests are carried out on actual system to estimate manipulating variables
which were not known before the experiments.
International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
: Experiment-2 robustness of hybrid controller.
Fig. 12, Shows the result of third experiment. It can be seen that from time intervals
seconds, hybrid controller is more efficient in tracking the input (set points) than Fuzzy Logic
controller. Also from time interval 500-1250 seconds, Hybrid controller is more stable than fuzzy
3, comparison of Fuzzy Logic controller against Hybrid Fuzzy Logic and PID
controller.
UTURE CONSIDERATIONS
This paper presents a hybrid control (PID and fuzzy logic controller) for ܪ neutralization pilot
covers the entire operating range and is more robust against the uncertainty
variation). It is noticed that proposed hybrid controller is more stable as compared to the Fuzzy
Logic controller. Process modelling approach adopted in this paper is based on the Physico
chemical principles and fundamental laws. A conventional mathematical modelling process is
incorporated. Practical tests are carried out on actual system to estimate manipulating variables
which were not known before the experiments. The design methodology (deriving dynamic non
12
Fig. 12, Shows the result of third experiment. It can be seen that from time intervals 1250-2000
seconds, hybrid controller is more efficient in tracking the input (set points) than Fuzzy Logic
1250 seconds, Hybrid controller is more stable than fuzzy
3, comparison of Fuzzy Logic controller against Hybrid Fuzzy Logic and PID
neutralization pilot
covers the entire operating range and is more robust against the uncertainty ܪ value
variation). It is noticed that proposed hybrid controller is more stable as compared to the Fuzzy
based on the Physico-
chemical principles and fundamental laws. A conventional mathematical modelling process is
incorporated. Practical tests are carried out on actual system to estimate manipulating variables
The design methodology (deriving dynamic non-
13. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
13
linear equation) presented in this paper is generally applicable to a ܪ neutralization plant, based
on continuous steering tank reactor. The robustness of the Hybrid controller depends upon the
process variables i.e. Acid/Base Flow rate control valve, ܪ value meter, flow transmitter and
concentration monitoring sensors as these instruments appear as manipulating variables during
controller design and implementation.
In this paper, PID controller is used to control the flow rate of both acid and alkaline streams and
a Fuzzy logic controller is used to control the ܪ value. However in future, an adaptive neural
network based controller can be used to achieve the same objective. Overall response of the
system in terms of rise and fall time heavily depends on the instruments used to assemble ܪ
neutralization plant. So a more predictable ܪ process model can be designed by using more
accurate instruments.
10. REFERENCES
[1] Butler, J. N., Ionic Equilibrium : A Mathematical Approach Addison-Wesley Publishing, Inc.,
London.1964.
[2] Christian, G. D. , "Acid-Base Equilibria," in Analytical Chemistry, Sixth edn, John Wiley & Sons,
Inc., United States of America, pp. 214-260. 2004.
[3] R. Ibrahim, “Practical modeling and control implementation studies on a ph neutalization plot
plant”, Department of Electronics and Electrical Engineering,mFaculty of Engineering,,
University of Glasgow, 2008.
[4] Alvarez, H., Londono, C., de Sciascio, F., & Carelli, R., "PH neutralization process as a
benchmark for testing nonlinear controllers", Industrial & Engineering Chemistry Research, vol.
40, no. 11, pp. 2467-2473, 2001.
[5] Bar-Eli, K. & Noyes, R. M., "A model for imperfect mixing in a CSTR", The Journal of Chemical
Physics, vol. 85, no. 6, pp. 3251-3257, 1986.
[6] McAvoy, T. J., Hsu, E., & Lowenthals, S., "Dynamics of pH in controlled stirred tank reactor",
Ind Eng Chem Process Des Develop, vol. 11, no. 1, pp. 68-78, 1972.
[7] Bates, R. G., Determination of pH: Theory and Practice, 2 edn, John Wiley & Sons, Inc, New
York, 1973.
[8] Gustafsson, T. K., "Calculation of the pH value of a mixture solutions—an illustration of the use
of chemical reaction invariants", Chemical Engineering Science, vol. 37, no. 9, pp. 1419-1421,
1982.
[9] Gustafsson, T. K. & Waller, K. V., "Dynamic modeling and reaction invariant control of pH",
Chemical Engineering Science, vol. 38, no. 3, pp. 389-398., 1983.
[10] Jutila, P., "An application of adaptive pH-control algorithms based on physicochemical in a
chemical waste-water treatment plant", International Journal of Control, vol. 38, no. 3, pp. 639-
655.1983.
[11] Gustafsson, T. K. & Waller, K. V., "Nonlinear and adaptive control of pH", Industrial &
Engineering Chemistry Research, vol. 31, no. 12, pp. 2681-2693, 1992.
[12] Henson, M. A. & Seborg, D. E., "Adaptive nonlinear control of a pH neutralization process",
Control Systems Technology, IEEE Transactions on, vol. 2, no. 3, pp. 169-182, 1994.
[13] Bohn, C. & Atherton, D. P., "SIMULINK package for comparative studies of PID anti- windup
strategies", Proceedings of the IEEE/IFAC Joint Symposium on Computer-Aided pp. 447-452,
1994.
[14] Gustafsson, T. K. & Waller, K. V., "Dynamic modelling and reaction invariant control of pH",
Chemical Engineering Science, vol. 38, no. 3, pp. 389-398, 1983.
[15] Gustafsson, T. K., Skrifvars, B. O., Sandstroem, K. V., & Waller, K. V. "Modeling of pH for
Control", Industrial & Engineering Chemistry Research, vol. 34, no. 3, pp. 820-827, 1995.
14. International Journal of Fuzzy Logic Systems (IJFLS) Vol.3, No2, April 2013
14
[16] Jutila, P. & Orava, J. P., "Control and Estimation Algorithms for Physico- Chemical Models of
pH-Processes in Stirred Tank Reactors", International Journal of Systems Science,vol.12, no.7,
pp.855-875, 1981.
[17] Jutila, P., "An application of adaptive pH-control algorithms based on physicochemical in a
chemical waste-water treatment plant", International Journal of Control, vol. 38, no. 3, pp. 639-
655, 1983.
[18] Jutila, P. & Visala, A., "Pilot plant testing of an adaptive pH-control algorithm based on physico-
chemical modelling", Mathematics and Computers in Simulation, vol. 26, no. 6, pp. 523-533,
1984.
[19] Gustafsson, T. K. & Waller, K. V., "Nonlinear and adaptive control of pH", Industrial &
Engineering Chemistry Research, vol. 31, no. 12, pp. 2681-2693, 1992.
[20] Bohn, C. & Atherton, D. P., "Analysis package comparing PID anti-windup strategies", IEEE
Control Systems Magazine, vol. 15, no. 2, pp. 34-40, 1995.
[21] Wright, R. A., Smith, B. E., & Kravaris, C., "On-Line identification and nonlinear control of pH
processes", Industrial and Engineering Chemistry Research, vol. 37, no. 6, pp. 2446-2461, 1998.
[22] Wright, R. A. & Kravaris, C., "On- line identification and nonlinear control of an industrial pH
process", Journal of Process Control, vol. 11, no. 4, pp. 361-374, 2001.
[23] Sung, S. W., Lee, I. B., & Yang, D. R., "pH control using an identification reactor", Industrial and
Engineering Chemistry Research, vol. 34, no. 7, pp. 2418- 2426, 1995.
[24] Yoon, S. S., Yoon, T. W., Yang, D. R., & Kang, T. S., "Indirect adaptive nonlinear control of a pH
process", Computers and Chemical Engineering, vol. 26, no. 9, pp. 1223-1230, 2002.
[25] Yoon, S. S., Yoon, T. W., Yang, D. R., & Kang, T. S., "Indirect adaptive nonlinear control of a pH
process", Computers and Chemical Engineering, vol. 26, no. 9, pp. 1223-1230, 2002.
[26] George, J. K. & Yuan B, Fuzzy Sets and Fuzzy Logic : Theory and Applications Prentice Hall,
PTR, New Jersey.1995.
[27] Kelkar, B. & Postlethwaite, B. 1994, "Fuzzy- model based pH control", IEEE International
Conference on Fuzzy Systems, vol. 1, pp. 661-666.1994.
[28] Karr, C. L. & Gentry, E. J, "Fuzzy control of pH using genetic algorithms", IEEE Transactions on
Fuzzy Systems, vol. 1, no. 1, pp. 46-53.1993.
[29] Karr, C. L., "Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm",
Proceeding of The International Society for Optical Engineering, vol. 1468, pp. 26-36.1991.
[30] Gong, M. & Murray-Smith, D. J., "A practical exercise in simulation model validation",
Mathematical and Computer Modelling of Dynamical Systems, vol. 4, no. 1, pp. 100-117.1998.
[31] Murray-Smith, D. J, "Issues of Model Accuracy and External Validation for Control System
Design", Acta Polytechnica, vol. 40, no. 3.2000.
[32] Murray-Smith, D. J., "Simulation Model Quality Issues In Engineering: A Review", Proceedings
5th Symposium on Mathematical Modelling, MATHMOD Vienna, Austria, 2006.
[33] Harvey, D., Morden Analytical Chemistry The McGraw-Hill Companies, Inc., Singapore.2000.
[34] Jamshidi, M., Ross, T. J., & Vadiee, N., Fuzzy Logic and Control: Software and Hardware
Applications Prentice Hall, Inc., New Jersey.1993.
[35] S. Vaishnav, Z. Khan. Design and Performance of PID and Fuzzy Logic Controller with Smaller
Rule Set for Higher Order System. International Conference on Modeling, Simulation and
Control, San Francisco, USA. Pages 24-26: 855–858, 2007.
[36] B. Hamed and A. El Khateb, "Hybrid Takagi-Sugeno fuzzy FED PID control of nonlinear
systems," Intelligent Systems and Automation, vol. 1019, pp. 99-102, 2008.