This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Application of Genetic Programming for Power System Planning and OperationIDES Editor
This work incorporates the identification of model
in functional form using curve fitting and genetic programming
technique which can forecast present and future load
requirement. Approximating an unknown function with
sample data is an important practical problem. In order to
forecast an unknown function using a finite set of sample
data, a function is constructed to fit sample data points. This
process is called curve fitting. There are several methods of
curve fitting. Interpolation is a special case of curve fitting
where an exact fit of the existing data points is expected.
Once a model is generated, acceptability of the model must be
tested. There are several measures to test the goodness of a
model. Sum of absolute difference, mean absolute error, mean
absolute percentage error, sum of squares due to error (SSE),
mean squared error and root mean squared errors can be used
to evaluate models. Minimizing the squares of vertical distance
of the points in a curve (SSE) is one of the most widely used
method .Two of the methods has been presented namely Curve
fitting technique & Genetic Programming and they have been
compared based on (SSE)sum of squares due to error.
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Application of Genetic Programming for Power System Planning and OperationIDES Editor
This work incorporates the identification of model
in functional form using curve fitting and genetic programming
technique which can forecast present and future load
requirement. Approximating an unknown function with
sample data is an important practical problem. In order to
forecast an unknown function using a finite set of sample
data, a function is constructed to fit sample data points. This
process is called curve fitting. There are several methods of
curve fitting. Interpolation is a special case of curve fitting
where an exact fit of the existing data points is expected.
Once a model is generated, acceptability of the model must be
tested. There are several measures to test the goodness of a
model. Sum of absolute difference, mean absolute error, mean
absolute percentage error, sum of squares due to error (SSE),
mean squared error and root mean squared errors can be used
to evaluate models. Minimizing the squares of vertical distance
of the points in a curve (SSE) is one of the most widely used
method .Two of the methods has been presented namely Curve
fitting technique & Genetic Programming and they have been
compared based on (SSE)sum of squares due to error.
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
FUZZY GENE OPTIMIZED REWEIGHT BOOSTING CLASSIFICATION FOR ENERGY EFFICIENT DA...IJCNCJournal
The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The
various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection
was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN,
a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced
with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is
computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes
and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked
Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is
selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian
mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data
packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is
carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation
of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL,
data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP.
The results observed that FGEORBC technique improves the data gathering and NL with minimum time as
well as EC than the state-of-the-art methods.
A major challenge in hydrological modelling is to identification of optimal
parameter set of different data, catchment characteristics and objectives. Although, the
identification of optimal parameter set is difficult because of conceptual hydrological
models contain more number of parameters and accuracy also depends upon all the
relevant number of parameters influencing in a model. This identification process
cannot estimate directly and therefore it measured based on calibrating the model
which minimizing an objective function. Here, the objective function can depend upon
the sensitivity of model parameters and calibration of model. In this paper, we proposed
the Emulator Based Optimization (EBO) for reducing number of runs and improving
conceptual model efficiency. Where, emulator models are used to represent the
response surface of the simulation models and it can play a valuable role for
optimization. In this study evaluates EBO for calibrating of SWAT hydrological model
with following steps like input design, simulation model, emulator modelling,
convergence criteria and validation. The results show that EBO calibrates the model
with high accuracy and it captured the observed model with consuming less time. This
study helps for decision making, planning and designing of water resources.
Unit commitment has been solved with many techniques viz., genetic algorithms evolution ary programming, simulated annealing, optimization and tab along with the combination of dynamic programming. This paper proposes Particle swarm Optimization combined with Lagrange Relaxation method (LR) for solving Unit Commitment (UC). The results from the test samples are compared with those obtained by Particle swarm optimization for solving unit commitment, Genetic algorithm and LR. The shortcoming of branch-and-bound is the exponential growth in the execution time with the size of UC problem. The integer and mixed integer methods adopt linear programming technique to solve and check for an integer solution. These methods have only been applied to small UC problems and have required major assumptions which limit the solution space. Lagrange relaxation for UC problem was superior to dynamic programming due to its faster computational time. However, it suffers from numerical convergence and solution quality problems. Furthermore, solution quality of LR depends on the method to update Lagrange multipliers. This paper proposes a new hybrid method for solving UC problem. The proposed method is developed in such way that a particle swarm optimization technique is applied to update Lagrange multipliers and improves the performance of LR method. To illustrate the effective of the proposed method, it is tested and compared to the conventional LR [69], GA [69], and HPSO [79] on 4 units test system and 10 units test system, respectively.
Capacitor Placement Using Bat Algorithm for Maximum Annual Savings in Radial ...IJERA Editor
This paper presents a two stage approach that determines the optimal location and size of capacitors on radial distribution systems to improve voltage profile and to reduce the active power loss. In first stage, the capacitor locations can be found by using loss sensitivity method. Bat algorithm is used for finding the optimal capacitor sizes in radial distribution systems. The sizes of the capacitors corresponding to maximum annual savings are determined by considering the cost of the capacitors. The proposed method is tested on 15-bus, 33 bus, 34-bus, 69-bus and 85-bus test systems and the results are presented.
Comparison of Emergency Medical Services Delivery Performance using Maximal C...IJECEIAES
Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered.
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
This paper presents a solution to solve the network reconfiguration, DG coordination (location and size) and capacitor coordination (location and size), simultaneously. The proposed solution will be determined by using Artificial Bee Colony (ABC). Various case studies are presented to see the impact on the test system, in term of power loss reduction and also voltage profiles. The proposed approach is applied to a 33-bus test system and simulate by using MATLAB programming. The simulation results show that combination of DG, capacitor and network reconfiguration gives a positive impact on total power losses minimization as well as voltage profile improvement compared to other case studies.
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
these filters in reaching a practical trade-off between the desired field estimation accuracy and the
required computational load.
ENERGY EFFICIENT ROUTING PROTOCOL BASED ON DSRijasuc
Energy consumption is a major concern in most of the present day devices in wireless networks. Especially
in Ad hoc networks, energy is a limited factor. Random movement in nodes add to the frequent failure of
routes which adds to the energy consumption in the network. In this paper, a routing protocol is proposed
which is based on a modification of the conventional DSR (Dynamic Source routing). A comparative
analysis is performed with respect to energy consumption, maximum throughput and delay. The routing
protocols used for reference in this analysis are DSDV, AODV and conventional DSR. Experimental results
show that the proposed modified DSR shows a reduced energy consumption, improved rate of maximum
throughput and a reduced delay compared to above mentioned routing protocols
Performance comparison of SVM and ANN for aerobic granular sludgejournalBEEI
To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
Enhancement of student performance prediction using modified K-nearest neighborTELKOMNIKA JOURNAL
The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered as the main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance, which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
FUZZY GENE OPTIMIZED REWEIGHT BOOSTING CLASSIFICATION FOR ENERGY EFFICIENT DA...IJCNCJournal
The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The
various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection
was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN,
a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced
with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is
computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes
and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked
Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is
selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian
mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data
packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is
carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation
of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL,
data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP.
The results observed that FGEORBC technique improves the data gathering and NL with minimum time as
well as EC than the state-of-the-art methods.
A major challenge in hydrological modelling is to identification of optimal
parameter set of different data, catchment characteristics and objectives. Although, the
identification of optimal parameter set is difficult because of conceptual hydrological
models contain more number of parameters and accuracy also depends upon all the
relevant number of parameters influencing in a model. This identification process
cannot estimate directly and therefore it measured based on calibrating the model
which minimizing an objective function. Here, the objective function can depend upon
the sensitivity of model parameters and calibration of model. In this paper, we proposed
the Emulator Based Optimization (EBO) for reducing number of runs and improving
conceptual model efficiency. Where, emulator models are used to represent the
response surface of the simulation models and it can play a valuable role for
optimization. In this study evaluates EBO for calibrating of SWAT hydrological model
with following steps like input design, simulation model, emulator modelling,
convergence criteria and validation. The results show that EBO calibrates the model
with high accuracy and it captured the observed model with consuming less time. This
study helps for decision making, planning and designing of water resources.
Unit commitment has been solved with many techniques viz., genetic algorithms evolution ary programming, simulated annealing, optimization and tab along with the combination of dynamic programming. This paper proposes Particle swarm Optimization combined with Lagrange Relaxation method (LR) for solving Unit Commitment (UC). The results from the test samples are compared with those obtained by Particle swarm optimization for solving unit commitment, Genetic algorithm and LR. The shortcoming of branch-and-bound is the exponential growth in the execution time with the size of UC problem. The integer and mixed integer methods adopt linear programming technique to solve and check for an integer solution. These methods have only been applied to small UC problems and have required major assumptions which limit the solution space. Lagrange relaxation for UC problem was superior to dynamic programming due to its faster computational time. However, it suffers from numerical convergence and solution quality problems. Furthermore, solution quality of LR depends on the method to update Lagrange multipliers. This paper proposes a new hybrid method for solving UC problem. The proposed method is developed in such way that a particle swarm optimization technique is applied to update Lagrange multipliers and improves the performance of LR method. To illustrate the effective of the proposed method, it is tested and compared to the conventional LR [69], GA [69], and HPSO [79] on 4 units test system and 10 units test system, respectively.
Capacitor Placement Using Bat Algorithm for Maximum Annual Savings in Radial ...IJERA Editor
This paper presents a two stage approach that determines the optimal location and size of capacitors on radial distribution systems to improve voltage profile and to reduce the active power loss. In first stage, the capacitor locations can be found by using loss sensitivity method. Bat algorithm is used for finding the optimal capacitor sizes in radial distribution systems. The sizes of the capacitors corresponding to maximum annual savings are determined by considering the cost of the capacitors. The proposed method is tested on 15-bus, 33 bus, 34-bus, 69-bus and 85-bus test systems and the results are presented.
Comparison of Emergency Medical Services Delivery Performance using Maximal C...IJECEIAES
Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered.
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
This paper presents a solution to solve the network reconfiguration, DG coordination (location and size) and capacitor coordination (location and size), simultaneously. The proposed solution will be determined by using Artificial Bee Colony (ABC). Various case studies are presented to see the impact on the test system, in term of power loss reduction and also voltage profiles. The proposed approach is applied to a 33-bus test system and simulate by using MATLAB programming. The simulation results show that combination of DG, capacitor and network reconfiguration gives a positive impact on total power losses minimization as well as voltage profile improvement compared to other case studies.
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
these filters in reaching a practical trade-off between the desired field estimation accuracy and the
required computational load.
ENERGY EFFICIENT ROUTING PROTOCOL BASED ON DSRijasuc
Energy consumption is a major concern in most of the present day devices in wireless networks. Especially
in Ad hoc networks, energy is a limited factor. Random movement in nodes add to the frequent failure of
routes which adds to the energy consumption in the network. In this paper, a routing protocol is proposed
which is based on a modification of the conventional DSR (Dynamic Source routing). A comparative
analysis is performed with respect to energy consumption, maximum throughput and delay. The routing
protocols used for reference in this analysis are DSDV, AODV and conventional DSR. Experimental results
show that the proposed modified DSR shows a reduced energy consumption, improved rate of maximum
throughput and a reduced delay compared to above mentioned routing protocols
Performance comparison of SVM and ANN for aerobic granular sludgejournalBEEI
To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
Enhancement of student performance prediction using modified K-nearest neighborTELKOMNIKA JOURNAL
The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered as the main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance, which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered asthe main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance,which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Multimode system condition monitoring using sparsity reconstruction for quali...IJECEIAES
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
Energy efficiency has recently turned out to be primary issue in wireless sensor networks.
Sensor networks are battery powered, therefore become dead after a certain period of time. Thus,
improving the data dissipation in energy efficient way becomes more challenging problem in order to
improve the lifetime for sensor devices. The clustering and tree based data aggregation for sensor
networks can enhance the network lifetime of wireless sensor networks. Non-dominated Sorting Genetic
Algorithm (NSGA) -III based energy efficient clustering and tree based routing protocol is proposed.
Initially, clusters are formed on the basis of remaining energy, then, NSGA-III based data aggregation
will come in action to improve the inter-cluster data aggregation further. Extensive analysis demonstrates
that proposed protocol considerably enhances network lifetime over other techniques.
PERFORMANCE PREDICTION OF AN ADIABATIC SOLAR LIQUID DESICCANT REGENERATOR USI...IAEME Publication
This paper presents an artificial neural network (ANN) algorithm developed and
trained to predict the performance of a solar powered adiabatic packed tower regenerator using LiBr desiccant. A reinforced technique of supervised learning based
on the error correction principle rule coupled with the perceptron convergence
theorem was used. The input parameters to the algorithm were temperature, flow rates
and humidity ratio of both air and desiccant fluid and their respective outputs used to
determine regenerator effectiveness and moisture removal rate. The optimum
performance of the ANN algorithm was shown by structures 6-4-4-1 and 6-14-1 for
moisture removal rate (MRR) and effectiveness respectively. Upon comparison, the
predicted and experimental MRR profiles aligned perfectly during training with a
maximum and mean difference of 0.18 g/s and 0.11 g/s. The regenerator effectiveness
profiles also agreed well with a few negligible disparities with a mean and maximum
difference of 0.6 % and 1 %. With respect to humidity ratio, the algorithm predicted
the experimental MRR values to maximum and mean accuracies of 0.0925 % and -
0.012 %. The maximum and mean accuracies of 4.14 % and 0.53 % were realized in
the prediction of experimental effectiveness by the neural network algorithm. The ANN
model precisely predicted the experimental MRR with respect to inlet desiccant
temperature with an average deviation of -0.5290 % while the highest difference was
3.496 % between predicted and measured temperature. With change in inlet desiccant
temperature, the ANN predicted and experimental values revealed maximum and
mean deviations of 2.61 % and 0.21 %. While the regenerator moisture removal rate
varied proportionally with the air temperature, the predicted MRR values matched
perfectly with the measured data with a mean and highest difference of -0.12 % and
3.2 %. In all the aforementioned cases, the mean and maximum differences between
the ANN model and experimental values were way below the allowable limit of 5 %
hence the algorithm was deemed to be successful and could find use in air
conditioning scenarios.
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
An algorithm for fault node recovery of wireless sensor networkeSAT 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
Neural Network Model Development with Soft Computing Techniques for Membrane ...IJECEIAES
Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
Performance Evaluation of Percent Root Mean Square Difference for ECG Signals...CSCJournals
Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. The signal compression is meant for detection and removing the redundant information from the ECG signal. Wavelet transform methods are very powerful tools for signal and image compression and decompression. This paper deals with the comparative study of ECG signal compression using preprocessing and without preprocessing approach on the ECG data. The performance and efficiency results are presented in terms of percent root mean square difference (PRD). Finally, the new PRD technique has been proposed for performance measurement and compared with the existing PRD technique; which has shown that proposed new PRD technique achieved minimum value of PRD with improved results.
Advance Data Mining - Analysis and forecasting of power factor for optimum el...Shrikant Samarth
Task: Execute a research project using data mining techniques
Approach: The topic chosen was ‘Analysis and Forecasting of Power Factor for Optimum Electric Consumption in a Household.’ Research question – What can be the best short term range of forecast for power factor patterns so that optimum energy consumption can be achieved for a household?
To answer the question, CRISM- DM method was used. The ARIMA machine learning model was developed using R.
Findings: The best short term range of forecasts for the power factor was achieved for 6 months and 12 months duration using the ARIMA model. The MAPE value for the ARIMA model was around 1.83.
Tools: Rstudio
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
1. International Journal of Research in Advent Technology, Vol.7, No.10, October 2019
E-ISSN: 2321-9637
Available online at www.ijrat.org
6
doi: 10.32622/ijrat. 710201911
Abstract—The process of an anaerobic reactor in
predicting the COD Level was modeled by the application of
Neural Networks. The effluent COD is the most common
factor used in all types of Wastewater Treatment Systems.
This research is about the prediction of the concentration of
|COD by using the ANN, a simple feed forward, Back
Propagation (BP) neural network. The three layered feed
forward ANN has been trained using four back propagation
algorithms. The efficiency and accuracy were measured
based on MSE and Regression Coefficient(R) to evaluate
their performance. The model trained with Levenberg –
Marquardt algorithm provided best results with MSE = 0.533
and R=0.991. The model performed up to the expectation and
the result of the prediction is appropriate.
Index Terms—Wastewater Treatment System (WWTS),
Anaerobic Reactor, Chemical Oxygen Demand (COD),
Artificial Neural Networks (ANN), Back-Propagation,
Levenberg-Marquardt Algorithm, Gradient Descent with
Adaptive Learning Rate Algorithm, Gradient Descent with
Momentum Algorithm, Resilient Back-Propagation
Algorithm.
I. INTRODUCTION
According to the great classic text ‘Thirukkural’, Water is
known as the true ambrosial food of all the lives. Water has
been used for various purposes consequently producing
sewage from the households, commercial and economic
sectors. This wastewater needs proper intervention before
discharging them into the water bodies since the sewage
contains numerous of harmful toxins and pathogens causing
serious calamities [1]. Thus, the Wastewater Treatment
System (WWTS) has been popularly implemented in
physical, biological and chemical progressions. Thus, the
linear mathematical models have been proven to be
inappropriate for these non-linear wastewater processes [2].
The well- known process for the decaying of domestic and
industrial sewage is through the microbial activity in the
absence of oxygen, called the Anaerobic Digestion [3]. The
Anaerobic reactors produce a combination gas of methane
and carbon dioxide called the biogas, a good source of
renewable energy [4]. Studies have been carried out to
Manuscript revised October 25, 2019 and published on November 10,
2019
S.Harikishore, Student, PSG College of Technology, Coimbatore, India.
Dr.S.Shanthi, Civil Department, Avinashilingam University for Women,
Coimbatore, India.
maximize the production of Biogas through various
techniques. The methanogenic bacteria are involved to
produce massive amount of biogas from assorted solid and
water wastes [5]. Thus the flourish process has paid scope for
the development of various anaerobic reactors.
This paper studies about the design of the prediction model to
find the COD level in an anaerobic wastewater reactor. The
inconsistency of the influent are recognized with the
alteration of specific parameters’ composition, strength and
flow rates levels [6]. The Chemical Oxygen Demand (COD)
has been used as the indicator to determine the cause of the
effluents [7]. In WWTS, the water is consider more toxic to
the ecological life and would be a great threat to the marine
lives if the concentration of COD is higher [8]. The
Anaerobic reactors forms granular sludge in minimum time
which can thus helps better in the COD deduction from the
affluent water [9].
The paper is about the implementation of the four different
back-propagation ANN algorithms on the prediction of the
COD value. The performances are compared by means of
their regression and mean square values.
The stream-flow forecasting and the cohesion less soils,
lateral stress resolve studied by Ozgur Kisi et al [10] has
compared three back propagation Neural Network training
algorithms namely Levenberg-Marquardt, Conjugate
gradient and Resilient Back Propagation algorithms. The
study has been concluded with result of convergence speed
and the estimation accuracy. In which Levenberg-Marquardt
has proven to be the best in terms of the convergences while
Resilient Back Propagation has performed accurately at the
determination.
Likewise Rama Subbanna et al [11], has also implemented
the above mentioned three ANN training algorithms to
observe the saturation level in the magnetic core of a welding
transformer. Various performance metrics such as
computational time, algorithm complexity, root square error
and the gradient are used to determine the finest algorithm.
Accordingly, Resilient Back Propagation algorithm has been
chosen to be the best with less computational time and good
algorithm complexity and also Levenberg-Marquardt
algorithm has also proven to be good.
.
II. MODEL DEVELOPMENT
The Fig 2 shows the schematic diagram during the model
development process. During the design of the model, an
appropriate development tool, MATLAB has been employed.
The ANN being a mathematical development modeling tool
becomes an admirable platform for various learning and
optimization algorithms.
Regression Analysis of ANN Training Algorithms for an
Anaerobic Wastewater Treatment System
S.Harikishore, Dr.S.Shanthi
2. International Journal of Research in Advent Technology, Vol.7, No.10, October 2019
E-ISSN: 2321-9637
Available online at www.ijrat.org
7
doi: 10.32622/ijrat. 710201911
Fig 1.Systematic Flow Diagram of the Model
A. Data Preprocessing
The quality of the data has been screened for the detection
of instability in the source data that might be caused
because of the electromagnetic disturbance, miscalculation
of the data records, uncertain errors, etc. Thus the data has
been pre-processed crucially before the analytic
proceedings.
B. Recognizing and removing/replacing Outliers
The mean or the median values of the data are widely
used to remove/replace the missing values. When the total of
WWTS parameters has minimum empty values, the
Regression becomes a preeminent method. The trimmed
means can be used to hold the outliners in the data. [12].Here
K-Means clustering has been taken to remove the blankness
and then clusters has been used to collect the interrelated data
for processing [13].
C. K-Means Clusters
The K-Means a clustering technique with a collection of
samples with specific measures of distance to form ‘k’
number of clusters, based on a defined distance
measurements [14]. K-Means frequently uses the partition
algorithm with square-error criterion, which helps to
minimize the following error function
C
E = ||xi-ck||2
.
K=1 xQk
where C is the number of clusters, ck is the centre of cluster k
and x is a data sample that belongs to cluster Qk.
The effluent COD values obtained from the anaerobic
digester are used analyse by the K- Means algorithm in Java.
From the initial partitions of the clusters, the centre values are
calculated therefore the minimizing the square error function.
The total numbers of records accounted are about 200
and the pre-processing time has been expressed in
nanoseconds. In this work, it takes 78 nanoseconds to
remove/replace the missing for 61 records. With
pre-processing time taken for analysis of the given data
samples the K-Means has been resulted as the graphical
vision.
D. Model Design Artificial Neural Network
The ANN is used as the prediction and forecasting modeling
tool which have a equivalent ability of the human brain.
There are massive amount of unified, highly processing
neurons working simultaneously to resolve simple to
complex problems. The Feed- Forward Back-Propagation is
the supervised learning ANN algorithm is known best in the
field for the calculation and updating the predicting models
with minimum error [15].
Model Training using BPN
The four different Artificial Neural Network (ANN) Back
Propagation training algorithms accounted in this work are
Levenberg – Marquardt (LM), Gradient Descent with
Adaptive Learning (GDA), Gradient Descent with
Momentum (GDM) and Resilient Back-Propagation (RP)
[16].
The ANN model has been trained to consider the influent
COD levels and the controlling parameters as the input while
consequent effluent COD levels are predicted as the output.
In order to optimize the model and to have minimum error
function, the model has been well trained with the flow rate
value, influent COD concentration and OLR training subset.
The hidden and output layers of the network are activated by
using the tansig and purelin functions respectively [17].
The performance model has been evaluated by using the
MSE and Regression Coefficient values.
III. EXPERIMENTAL RESULTS
With the learning ability the model adjusts the weights to
predict the expected result; the network has been trained with
four different back propagation training algorithms.
The model has been tested and the performance has
been evaluated at each trial by means of the MSE and R. The
performance graphs for the algorithms are shown in Fig.2,
Fig.3, Fig.4 and Fig 5. The Regression (R) values for the
respective algorithms are shown Fig. 6, Fig.7, Fig. 8, and Fig.
9.
3. International Journal of Research in Advent Technology, Vol.7, No.10, October 2019
E-ISSN: 2321-9637
Available online at www.ijrat.org
8
doi: 10.32622/ijrat. 710201911
IV. PERFORMANCE MEASURES
The four training algorithms were applied on the network
model based on the inputs considered in order to predict the
target COD levels. The LM algorithm has performed well by
minimizing the errors and with the minimum MSE the
accuracy of the prediction has been satisfactory. The R value
acquired as the result in the prediction of resultant output has
been proved to be the finest match of the network model. The
Table 1 proves the performance of the network obtained for
LM algorithm.
Table 1. Performance Metrics
Algorithms Regression(R) MSE
LM 0.991 0.533
GDA 0.092 2.90
GDM 0.629 0.843
RP 0.798 0.946
Thus the network response for accurate output was calculated
using the statistical indices viz., MSE and R.