Journal of Environmental Treatment Techniques                                     Download PDF version

2018, Volume 6, Issue 2, Pages: 15-25

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal weblink: http://www.jett.dormaj.com

Predictive Performance Modeling of Habesha

Brewery’s Wastewater Treatment Plant Using

Artificial Neural Networks

Elias Barsenga Hassen1 and Abraham M. Asmare2

1Faculty of Chemical and Food Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Ethiopia

2Institute of disaster Risk management and Food Security Studies, Bahir Dar University, Ethiopia

Received: 25/07/2017

Accepted: 07/02/2018

Published: 30/07/2018

Abstract

Recently, process control is, mostly, accomplished through examining the quality of the product water and adjusting the processes through an operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for predicting the performance. Owing to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are getting attention in the predictive performance modeling of WTPs. This paper focus on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of Habesha Brewery’s WTP. About 11 months of data (from May 2016 to March 2017) of influent and effluent water quality were used to build, train and evaluate the models. The study signifies that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output variables reaching up to 0.969. Model architecture of 3-21-3 for pH and TN and 1-76-1 for COD were selected as optimum topology for predicting the performance of Habesha Brewery’s WTP. The linear correlation between predicted outputs and target outputs for the optimal model architectures described above are 0.9201 and 0.9692, respectively.

Keywords: Artificial Neural Network, Wastewater Treatment Plant, Performance Modeling

1Introduction1

1.1Background

The aim of wastewater treatment process is to

achieve a treated effluent and sludge that is environmentally safe for disposal and/or reuse [1]. Taking into account current environmental problems and water scarcity, it is not unrealistic to believe that the trend of development of new WTPs will be continued all over the world. At the same time, loads on existing plants are expected to increase due to the growth of production capacity of industries. Moreover, the advent of more stringent environmental regulations relative to WTP and discharge puts more pressure on operators and decision makers to better manage and improve the reliability of their treatment plants [2]. Currently, process control is generally accomplished through examining the quality of the product water and adjusting the processes through an operator’s experience. This practice is inefficient and slow in control response [3]. This situation demands more efficient and economic control and assessment tool for proper functioning of WTPs.

Modeling and simulation is an elegant and cost- effective tool to assess performance and control of WTPs

Corresponding author: Elias Barsenga Hassen, Faculty of Chemical and Food Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Ethiopia, E-mail: elibarsa@gmail.com.

[4].Models of WTPs can be divided into two main categories: linguistic and mathematical models. Linguistic models (such as expert systems) can relate cause to effect, without the construction of a mathematical model. They are most suitable for describing phenomena in environmental systems that are very difficult to represent mathematically. Most of the expert systems that have been developed in the field of wastewater treatment are directed into diagnosis. This category of application helps to identify the causes of malfunctions and their remedies in pollution control facilities [5].

Mathematical models of wastewater treatment processes can be divided into two broad classes: white box (deterministic) models and black box models (empirical) models. White box models are most useful to understand the events occurring in a system [5]. Generally, deterministic models incorporate direct links between inputs and outputs through ordinary and partial differential equations that seek to mimic the mechanistic reactions. Development of such model which can accurately describe a system requires a detail knowledge and evaluation of the system, the factors that act in the system and the interaction between those factors. Although these models give a good insight into the mechanics of the system, they require a lot of hard work and time before applying them to a specific WTP [6]. Activated Sludge Model No. 1 (ASM1) and Activated Sludge Model No. 2 (ASM2), developed by International

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2018, Volume 6, Issue 2, Pages: 15-25

Water Association (IWA), are among the few mechanistic models that are developed in the field of wastewater treatment [7].

The other type of mathematical model, black box or empirical type of model is designed to represent hard-to- describe systems. They are very useful to operators because they reflect real world responses. Artificial neural network (ANN) is one of the most widely used black box modeling tools that are used to model wastewater treatment processes. The fact that black box models can be continually updated with minimal resource requirement has made them attractive in real- time control scenario [5]. Since its introduction it into the field of environmental engineering in the late 1980’s, its application has increased at a steady rate [8].

Artificial Neural Network (ANN) is an artificial intelligence technique that mimics the human brain’s biological neural network in the problem solving processes. As human solves a new problem based on the past experience, a neural network take previously solved examples, looks for patterns in these examples, learns the pattern and develops the ability to correctly classify new patterns and predict and forecast process parameters

[8].Experimental based laboratory tests are used for determination of the quality of the wastewater throughout the treatment process which is time consuming, expensive and slow in response. This study is aimed at developing a robust predictive model for determination of the quality of the treated effluent by using ANN.

Wastewater treatment plants (WTPs) are dynamic, non-linear systems subjected to variation in the volume and composition of the incoming wastewater. Nevertheless, these plants have to be operated properly; otherwise they possibly will be a cause for serious environmental and public health problem. Therefore, environmental regulations set restrictions on the quality of effluent that must be met by any WTP. These stringent discharge standards and time-dependent, non-uniform influent characteristics make the proper management of treatment systems a concern.

To date, almost all the industrial WTPs in Ethiopia, including Habesha Brewery, use conventional experimental approach to determine the quality of the treated effluent water before discharge or reuse. However, this method is expensive, time consuming and slow in response. In addition, this approach requires experienced professional to obtain the best result. A better operation and control of the WTP can be achieved by developing a robust mathematical tool for that enable prediction of the quality of the treated effluent based on past observations of certain key parameters. To this end, in this thesis work, an ANN modeling technique was used to develop predictive performance model of

Habesha Brewery’s WTP. Therefore, the general objective of this thesis work was to develop a robust model that enables to predict the performance of Habesha Brewery’s WTP using Artificial Neural Networks.

2 Description of the Wastewater Plant

2.1Plant description

Habesha Brewery with a capacity of producing

700,000 hectoliters of beer annually. Its WTP is located in Debre Birhan Town, 120 kilometers North of Addis

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Ababa City. It uses a biological based wastewater treatment plant. The wastewater has a maximum capacity of treating 1000 m3 of wastewater at a time (Figure 1).

Figure 1: Habesha Brewery’s WTP

2.2Process description of the wastewater plant

The wastewater collected from the production plant

is first passes through the screen chamber to remove large solid parts like broken bottles and sticker papers then it will be collected in the equalization tank where it will be neutralized by addition of HCl or NaOH depending on the initial pH of the wastewater. Then the neutralized wastewater will pass to Up-flow Anaerobic Sludge Blanket (UASB) where it is slowly pumped vertically in upward direction through a bed of anaerobic bacteria where it will be decomposed anaerobically. The anaerobically treated wastewater will be pumped to the aerobic reactor where it will be treated with addition of air before it will be discharged to the nearby farms for irrigation. The simple illustrative flow diagram of the WTP is presented in Figure 2 below.

Figure 2: Process flow diagram of Habesha Brewery's

WTP

2.3Effluent Guidelines and standards

Globally there is a great political and social pressure

to reduce the pollution arising from industrial activities. The brewing industry is one such industry that generate large amount of wastewater; it has been documented that 3 to 10 liters of wastewater is generated per liter of beer production. Due to increasing environmental concerns and regulations, attempts were made to utilize treated wastewater in environmental friendly manner [9].

The Environmental Protection Agency of Ethiopia (EPA) has developed general national pollutant discharge

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2018, Volume 6, Issue 2, Pages: 15-25

limit to control water pollution. The EPA’s effluent standard limit for reuse of wastewater for agricultural purpose is shown Table 1 below.

Table 1: EPA's Standard limit for discharge of treated industrial wastewater for selected physicochemical parameters [10]

Parameters

Limits

Temperature

Do not change ambient temperature by

more than 10C

pH

5-9

COD

≤ 125 mg/l

TN

≤ 30 mg/l

3 Methodology

3.1 Materials

3.1.1 Historical Operational Data

The historical data used for this study were obtained from Habesha Brewery’s WTP laboratory. About 11 months of records, between May 2, 2016 March 20, 2017, for COD, pH, and Total Nitrogen (TN) of raw influent wastewater and treated effluent were obtained from the plant laboratory.

The gathered data were carefully investigated and after considering the available options for modeling the treatment plant performance, it is decided to relate the quality of the raw influent wastewater to the quality of the final treated effluent. The descriptive statistical

analysis for the raw data operational data obtained for the influent and effluent water are presented in Tables 2 and 3 below respectively.

3.1.2 Software

MATLAB® (R2014b) software with neural network toolbox, which is a high performance interactive software package for scientific and engineering computation, was used for designing, building, training and testing the neural networks. MiniTab® v 17 and Microsoft Excel® 2016 were also used for the data organization and pre-processing tasks.

3.2 Methods

3.2.1 Data Pre-Processing

The quantity and quality of the available data sets will ultimately determine the performance and complexity of the ANN. Mostly, raw data collected from plant operations are noisy and incomplete by nature. Therefore, the raw data that was collected from the plant laboratory was examined for completeness, missing values and outliers by using statistical analysis software called MiniTab.

Missing values were estimated by using interpolation and outlier removal was accomplished by removing measurements that were not within the range of ±3 standard deviations. The descriptive statistics of the pre- processed data is presented in the Tables 4 and 5 below.

Table 2: Descriptive Statistics for raw operational data of Influent wastewater

Variable

Units

Min

Max

Mean

St. Dev

Var.

 

 

 

 

 

 

 

pH

-

6.030

9.710

7.208

0.551

0.303

COD

mg/l

920.000

2764.000

1605.400

437.000

190985.100

TN

mg/l

8.440

49.600

31.337

6.817

46.477

 

 

 

 

 

 

Table 3: Descriptive Statistics for raw operational data of treated effluent

 

Variable

Unit

Min

Max

Mean

St. dev

Var.

 

 

 

 

 

 

 

pH

-

7.560

8.330

7.960

0.140

0.020

COD

mg/l

14.600

422.000

89.690

67.420

4545.250

TN

mg/l

2.691

27.575

8.721

5.305

28.142

 

 

 

 

 

 

 

To ensure the statistical distribution of the values for each net input and output is roughly uniform, data scaling was carried out by using MATLAB function mapminmax function in MATALB by which the data sets were scaled to a specified range of -1 to +1.

3.2.2 Data Division

When training multilayer networks, the general practice is to first divide the data into three subsets. The first subset is the training set, which is used for

computing the gradient and updating the network weights and biases. The second set is the validation set. The error on validation set is monitored during the training process. The validation error decreases during the initial phase of the training and the error tends to rise as the network begins to rise. Therefore, the validation set is used to decide when to stop training in order to avoid over training. The test set is not used during the training phase, but it is used to compare the performance of different models.

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Table 0: Descriptive Statistics for pre-processed data of Influent

Variable

 

Units

Min

Max

Mean

St. Dev

Var.

 

 

 

 

 

 

 

 

pH

-

6.030

8.750

7.138

0.413

0.170

COD

mg/l

920.000

2764.000

1602.200

408.700

167036.500

TN

mg/l

14.820

49.600

31.562

5.980

35.765

 

 

 

 

 

 

 

 

Table 5: Descriptive Statistics for pre-processed data of treated effluent

 

Variable

 

Units

Min

Max

Mean

St. Dev

Var.

 

 

 

 

 

 

 

 

pH

 

-

7.560

8.290

7.951

0.135

0.0183

COD

 

mg/l

14.600

258.000

83.48

52.200

2724.44

TN

 

mg/l

2.690

21.150

8.356

3.968

15.747

 

 

 

 

 

 

 

 

MATLAB provides four alternative functions for dividing data into training, validation and test sets. They are dividerand (the default), divideblock, divideint, and divideind. Normally the data division process is carried out automatically during the training process. In this study all the four data division functions are used alternatively in an attempt to increase the performance of the networks.

Table 6: List of MATLAB data division functions [11]

Function Algorithm

dividerand Divide the data randomly (default)

divideblock Divide the data into contiguous blocks

divideint Divide the data using an interleaved selection

divideind Divide the data by index

3.2.3 Model architecture selection

Model architecture determines the overall the structure and direction of information flow in the model. Generally, artificial neural network architectures are divided into feed forward and recurrent networks. In feed forward networks the information flows in one direction from the input layer to the output layer. In contrast to feed forward networks recurrent information moves in both forward and backward direction. Multilayer Perceptron (MLP) Networks, the most commonly used type of feedforward neural network, were used in this thesis work.

3.2.4 Network Structure Selection

Network structure, together with model network architecture, defines the functional form of the relationship between network inputs and outputs. The optimal network structure generally strikes a balance between generalization ability and network complexity. Optimal network structure was achieved by investigating

the effect of the following network characteristics on the network performance.

i.Network configuration: In this study two types of configurations were developed based on the pre- processed data. pH, COD and TN of the raw influent wastewater were used as input variables to predict the quality of the treated effluent. A total of 13 models (9 SISO, 3 MISO and 1 MIMO configurations) were designed and evaluated in this study.

ii.Selection of number of hidden neuron: Determination of optimal network structure involves the selection of appropriate number of hidden neurons. Generally, a two-layer perceptron will be developed in this thesis work which was represented with a I-J-K topology, where I, J and K are the number of input, hidden and output neurons in the network respectively. The input and output layer are easily known from the input and target data. Unfortunately, currently there is no universally accepted guideline to determine the number of hidden neurons. Therefore, in this study, the optimal number of neuron was determined by using a trial and error method by adding one or two neurons at a time.

iii.Selections of proper transfer function: Generally, the hyperbolic tangent and sigmoid functions are appropriate for most types of networks, especially for prediction problems. The hyperbolic tangent function was preferred over the log sigmoid function in this work for the following reasons:

a.The output varying from -1 to +1 for the hyperbolic tangent and only 0 to 1 for the log sigmoid function. This means that the hyperbolic tangent function has a negative response for a negative input value and a positive response for a positive input value, while

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2018, Volume 6, Issue 2, Pages: 15-25

the log sigmoid function always has a positive response.

b.The slope of the hyperbolic tangent is much greater than the slope of the sigmoid function. Which means the hyperbolic tangent function is more sensitive to small changes in input.

iv.Training algorithm selection: It is difficult tasks to determine which training algorithm provides fast learning experience for a given problem. Training algorithm selection depends on many factors including complexity of the problem, the number of data points in the training set, the number of biases in the network, the error goal and to which purpose the network is being used. MATLAB provides 13 training algorithms for training of a neural network. Among which Bayesian Regularization algorithm (trainbr) was used in this study. Even if the algorithm takes more time for training compared to other algorithms, it has a good generalization capacity for difficult, small or noisy datasets [11].

3.2.5 Model Training

Model Training is an iterative process that seeks to modify the network through numerous presentations of data. Mainly, there are two type of model training; unsupervised and supervised training. Unsupervised training the model is only fed with the input values and uses it to adjust its connection weights. In the case of supervised data, feed with both input and target data values. Supervised mode of training was used for this study since it requires shorter time compared to the unsupervised training mode.

3.2.6 Model Evaluation

In order to determine which network structure is optimal, the performance of the calibrated models was evaluated. In this thesis work, Mean Squared Error (MSE) and Correlation coefficient (R) were used to evaluate the performance of the networks, where MSE (Mean Square Error) is the average square difference between outputs and targets. Lower MSE values are considered better and zero means no error. R values

measure the correlation between outputs and targets. An R value of 1 means a close relationship, 0 a random relationship.

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4 Results and Discussion

After pre-processing the raw data was completed by using MiniTab statistical software, different SISO, MISO and MIMO models were created by using MATLAB. The network type that was used in this thesis work was Feed Forward network architecture with Back Propagation with learning paradigm. Bayesian Regularization (trainbr) algorithm which is the best algorithm in generalizing noisy and difficult data sets was used to train the models. Hyperbolic tangent sigmoid (tansig) and linear transfer function (purelin) transfer function were used in the hidden and output layer of the networks respectively. The optimal number of hidden neurons was determined by trial and error and R value and MSE were used to evaluate and compare the performance of the models developed.

3.3pH prediction models

Over all 5 networks (3 SISO, 1 MISO and 1 MIMO)

models were developed to determine the optimum network topology for prediction of pH of the treated effluent. Table 7 below presents the statistical performance results of these models.

Based on the statistical performance of all the three configurations for prediction of pH, the MIMO model is the best by scoring R value of 0.920. This means, the MIMO model generalizes the data well and is likely to make accurate prediction when new data (data that is not from the training or testing set) is provided compared to the other models. The linear regression plot for the best performing models from each configuration is presented in Figure 3 below.

Table 7: Performance statistics for pH prediction models

Configuration

Input

I-J-K

Training

Testing

All

MSE

R

MSE

R

R

 

 

 

 

pH

1-52-1

0.120

0.276

0.161

0.421

0.455

SISO

COD

1-41-1

0.054

0.805

0.057

0.747

0.804

 

TN

1-76-1

0.135

0.254

0.178

0.050

0.172

MISO

pH, COD, TN

3-29-1

0.060

0.771

0.070

0.773

0.771

MIMO

pH, COD, TN

3-21-3

0.037

0.921

-

0.917

0.920

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Figure 3: Regression plot for best performing pH prediction (A) CODin-pHout (SISO) (B) MIMO and (C) MISO models

(A)

 

 

 

 

 

 

 

 

 

 

(B)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(C)

Figure 4: Comparison between predicted and actual data for pH prediction using (A) CODin-pHout (SISO) (B) MISO and (C)

MIMO models

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2018, Volume 6, Issue 2, Pages: 15-25

As it can be seen the comparison plot between the predicted and actual data and based on the MSE values of the models, the MIMO model indicates a good fit

compared to the other models. Therefore, the appropriate architecture for prediction of pH was determined to consist input layer with 3 neurons, a hidden layer with 21 neurons and 3 output layer neurons.

3.4COD prediction models

For predicting COD of the treated effluent 3 SISO

configuration models with different input, 1 MISO and 1 MIMO configuration models were developed. The statistical performance for best performing models from each configuration is presented in Table 8.

Among the models developed for prediction COD, the SISO model where COD is used as an input has shown excellent generalization and predictive efficiency by scoring R value of 0.9692. Even if the MISO and MIMO models have scored a lower R value compared to the SISO model, both the configurations have shown a good accuracy by scoring R values greater than 0.9 for

all the training and testing sets. These results were also supported by the regression plot and comparison plots presented in Figure 5 and 6 below respectively. Based on the R value and MSE value of the models the best topology for prediction of COD is a SISO configuration with 1 neuron in the input layer, 76 neurons in the hidden layer and 1 neuron in the output layer.

3.5TN prediction models

For predicting TN of the treated effluent 3 SISO

configuration models with different input, 1 MISO and 1 MIMO configuration models were developed. The statistical performance for best performing models from each configuration is presented in Table 9.

As presented in Table 9 above, the best performing model for prediction of TN of the treated effluent is the MIMO model with topology of 3-21-3 by scoring R value of 0.920. The result is also supported by the regression and comparison plot between the actual output data and predicted data that is presented in Figure 7 and 8 below.

Table 8: Performance Statistics for COD prediction models

Configuration

Input

I-J-K

Training

Testing

All

MSE

R

MSE

R

R

 

 

 

 

pH

1-33-1

0.202

0.136

0.181

0.247

0.305

SISO

COD

1-76-1

0.007

0.982

0.057

0.885

0.970

 

 

TN

1-48-1

0.111

0.305

0.092

0.079

0.275

MISO

pH, COD, TN

3-35-1

0.022

0.941

0.015

0.942

0.945

MIMO

pH, COD, TN

3-21-3

0.037

0.921

-

0.917

0.920

Figure 5: Regression plot for best performing COD prediction (A) CODin-CODout (SISO) (B) MISO and (C) MIMO models

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(A)

(B)

(C)

Figure 6: Comparison between predicted and actual data for COD prediction using (A) CODin-CODout (SISO) (B) MISO

and (C) MIMO models

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Table 9: Performance Statistics for TN prediction models

Configuration

Input

I-J-K

Training

Testing

All

MSE

R

MSE

R

R

 

 

 

 

pH

1-96-1

0.133

0.340

0.184

0.729

0.330

SISO

COD

1-68-1

0.024

0.937

0.131

0.779

0.913

 

 

TN

1-51-1

0.145

0.229

0.172

0.024

0.208

MISO

pH, COD, TN

3-24-1

0.035

0.911

0.033

0.817

0.907

MIMO

pH, COD, TN

3-21-3

0.037

0.921

-

0.917

0.920

Figure 7: Regression plot for best performing TN prediction (A) CODin-TNout (SISO) (B) MISO and (C) MIMO models

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(A)

(B)

(C)

Figure 8: Comparison between predicted and actual data for TN prediction using (A) CODin -TNout (SISO) (B) MISO and

(C) MIMO models

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5 Conclusion

The performance prediction of wastewater treatment processes is important in order to keep the system stable under a wide range of circumstances. This thesis work presented a step by step procedure for developing a neural network performance predictive model for Habesha Brewery’s WTP by using ANN. During the model development the raw influent wastewater quality were used as input variables to develop 13 distinct SISO, MISO and MIMO network models for prediction of treated effluent water quality. The raw data obtained from the treatment plant were analyzed and pre- processed before they were used for training and evaluating the networks. Trial and error method was used to identify the best performing network topology. Based on the observed results of the models developed, it can be concluded that the outputs of the models are in very good agreement with the raw data obtained from the WTP laboratory. Generally, based on the performance statistical results, the MIMO model have shown a better predictive performance compared to the SISO and MISO configuration by scoring R value of 0.9201. In the case of the input variables, the models with COD as input variables have a better quality of prediction and accuracy than the models where pH and TN are used as input variable. Model architectures 3-21-3 (MIMO configuration) for pH and TN and 1-76-1 (SISO configuration with COD as input) for COD were selected as optimum topology for predicting the performance of Habesha Brewery’s WTP. The linear correlation between predicted outputs and target outputs for the optimum model architecture described above are 0.9201 and 0.9692 respectively. Finally, this study has demonstrated the effectiveness of using neural networks to capture the non-linearity and complexity of the relationship between raw influent and treated effluent water quality of industrial WTPs. In conclusion, the result showed that the trained and tested ANN models developed could potentially be employed for predicting the performance of Habehsa Brewery’s WTP, thus provide a good supportive tool for controlling and monitoring performance of the WTP.

References

1.Fezzi, M., A Pragmatic Approach to Wastewater Treatment Modelling: The Kallby Wastewater

Treatment Plant as a Case Study, in Department of Chemical Engineering. 2015, Lund University: Lund, Seweden. p. 121.

2.Dairi, S., et al., Dynamic Simulation for the requirements of oxygen about the Municipal Wastewater Treatment Plant- Case of Souk-Ahras/ Algeria. Journal of Material and Environmental Science, 2011. 2(S1): p. 507-512.

3.Zhang, Q. and S.J. Stanley, Real-time water treatment process control with artificial neural networks. Journal of Environmental Engineering, 1999. 125(2): p. 153-160.

4.Vandejerckhove, A., W. Moerman, and S.W.H. Hulle, Full-scale modelling of a food industry wastewater treatment plant in view of process upgrade. Chemical Engineering Journal, 2008. 135: p. 185-194.

5.El-Din, A.G., D.W. Smith, and M.G. El-Din, Application of Artificial neural networks in wastewater treatment. Journal of Environmental Engineering and Science, 2004. 3: p. S81-S95.

6.Moral, H., Modeling of Activated Sludge Process by using Artificial Neural Networks, in Department of Environmental Engineering. 2004, Middle East Technical University: Ankara, Turkey. p. 1126.

7.Gernaey, K.V., et al., Activated sludge wastewater treatment plant modelling and simulation: state of the art. Journal of Environmental Modelling and Software, 2004. 19: p. 763-783.

8.Zhang, Q.J., Artificial Neural Network—Advanced Theories and Industrial Applications, in Department of Civil and Environmental Engineering. 2002, University of Alberta: Edmonton, Canada. p. 183.

9.Senthilraja, K., P. Jothimani, and G. Rajannan, Effect of brewery wastewater on growth and physiological changes in maize, sunflower and sesame crops. International Journal for Life Science and Educational Research, 2013. 1(1): p. 36-42.

10.Fikresilasie, T., Impact of Brewery Effluent on River Water Quality: The Case of Meta abo Brewery Factory and Finchewa River in Sebeta, Ethiopia, in School of Graduate Studies. 2011, Addis Ababa University: Addis Ababa. p. 85.

11.MathWorks, Neural Network Toolbox User's GuideTM. 2017, MathWorks Inc.: Massachusetts, USA.

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