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Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN)
Journal of Hazardous Materials 179 (2010) 769–775
Contents lists available at ScienceDirect
Journal of Hazardous Materials journal homepage: www.elsevier.com/locate/jhazmat
Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN) M. Delnavaz, B. Ayati ∗ , H. Ganjidoust Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 30 September 2009 Received in revised form 15 March 2010 Accepted 15 March 2010 Available online 23 March 2010 Keywords: Aniline COD Moving bed biofilm reactor Neural networks
a b s t r a c t In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Aniline is commonly used in a number of industrial processes such as dyes, plastic, paint, pigments, herbicides, pharmaceutical preparation and rubber accelerators production [1–4]. It is an aromatic hazardous compound that readily dissolves in water up to 3.5%. Due to its high solubility in water, risk of possible pollution in water resources especially in case of a chemical spill is increased. Therefore, any wastewater containing aniline is a serious problem and should be treated before discharging to the environment [5]. Wastewater containing aromatic amines such as aniline has been shown to be treated by photo-decomposition, electrolysis, resin adsorption, ozone oxidation, and biodegradation methods. They are not completely decomposed by activated sludge process and also inhibit biodegradation of other chemicals because of their hard degradability. Therefore, further studies on advanced biological processes are necessary [6]. Moving bed biofilm reactor (MBBR) which was introduced about 14 years ago and now is popular in Europe, has been operated successfully in urban and some industrial wastewater treatment plants [7,8]. MBBR is designed to offer the advantages of the biofilm process (compact, stable removal efficiency and simplicity of operation) without its drawbacks (medium channeling and clogging). The idea behind MBBR development was to adopt the best features
∗ Corresponding author at: Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, P.O. Box 14155-4838, Tehran, Iran. Tel.: +98 21 8288 3328; fax: +98 21 8800 5040. E-mail address: ayati [email protected] (B. Ayati). 0304-3894/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jhazmat.2010.03.069
of activated sludge as well as those of biofilter processes in one reactor [8]. In biological wastewater treatment systems such as MBBR, influent characteristics variability might have influence on operational control. Therefore, a biological process modeling is a difficult task as most of the available models are just approximations based on probabilities and assumptions. Recently, artificial neural networks (ANNs) have been increasingly applied in environmental and water resources engineering area [9]. The main advantage of ANNs over physical based models is that they are data-driven. Consequently, unlike traditional parametric models, in an ANN model, it is not required to assume a predefined form for the relation between input and output variables. In these models, it is possible to construct a complex relation between input and output variables with an excellent level of accuracy as compared to the conventional methods [10]. Biological process prediction without limitation of conventional mathematical models is an important advantage of ANN for MBBR operation control. Cristea et al. [11] have studied the capacity of neural networks for the assessment of plant behavior in integrated urban wastewater system simulations. They have proposed that ANN based simulators reveal good accuracy for predicting important process variables and reduction of the simulation time, compared to the first principle wastewater treatment plant (WWTP) simulator. Sadrzadeh et al. [12] have used neural networks to predict separation of lead ions from wastewater using electro-dialysis. They have examined a number of different network structures and architectures to compute the amount of lead ions separation. The results of modeling have shown that there was an excellent agreement between the experimental data and predicted values [12].
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Fig. 1. Schematic picture of MBBR pilot.
ANN modeling for biotreatment of zinc-containing wastewater in a sulfidogenic CSTR was studied by Sahinkaya [13]. This developed model was very successful to match the measured and predicted effluent concentrations of sulfate, COD, acetate and zinc. Prakash et al. [14] used ANNs for predicting copper (II) biosorption efficiency. The performance of the network for predicting the sorption efficiency was found to be very impressive. This paper presents predictive models based on the concept of ANN in predicting aniline treatment efficiency using MBBR in various conditions. 2. Materials and methods 2.1. Reactor setup Cylindrical MBBR reactor was used in this study (Fig. 1) with each reactor internal diameter, height and wall thickness of 10, 70 and 0.4 cm, respectively. Basis of the process is elements of biofilm carrier that are made from different materials such as polyethylene, PVC and porous aggregates. The elements provide a large protected surface area for the biofilm and optimal conditions for the bacteria culture to grow and thrive. The biofilm created around each carrier element protects the bacterial cultures from operating excursions to yield a very robust system for those industrial facilities loaded with process fluctuations. DO ad pH were the only control parameters and essential nutrients (C/N/P = 100/5/1) were added to the
system. The effective depth of wastewater in the reactor was 60 cm filled with up to 50% floating biofilm carrier elements made of LECA (Light Expanded Clay Aggregate) with a density of 0.55 g/cm3 . LECA is a kind of aggregate that has been used in many structural projects in Iran such as light weight concrete. High surface area of this material makes it more suitable as carrier in the biofilm reactor. In order to keep biofilm carrier in the reactor, a sieve (mesh size = 5 mm) was placed at the inlet. The aeration system supplied fine bubbles and provided sufficient mixing to keep the carriers moving in the reactor. Feed synthetic wastewater has been prepared using aniline which was supplied by Merck Company. Aniline served as the sole carbon and energy substrate for the biomass in the MBBR. In order to have C/N/P = 100/5/1 and enough alkalinity for aerobic conditions (6.5–8), necessary nutrients (urea, KH2 PO4 , K2 HPO4 ) were added as supplement feed to the reactors for all experimental trials. The parameters of pH, soluble Chemical Oxygen Demand (COD) filtered through Whattman paper No. 42 and Dissolved Oxygen (DO) were daily measured. Mixed Liquor Suspended Solids (MLSS) and Mixed Liquor Volatile Suspended Solids (MLVSS) were examined on alternative days. Microscopic investigation was done regularly. All laboratory experiments were conducted at room temperature (25–28 ◦ C) and DO concentration was always controlled above 3 mg/L during the filling and reacting cycles by regulating the aeration rate. The pH of the inlet wastewater was kept neutral and MLSS and MLVSS varied between 4000–4500 and 3000–3500 mg/L, respectively. All analytical tests were done as outlined in the Standard Method Handbook [15]. To ensure that aniline has not been converted by other intermediate compounds, COD and aniline concentration tests were performed simultaneously. The aniline concentrations were measured by ultraviolet absorption procedure, as given in the Standard Methods (APHA, 1998), using a Lambda EZ 150 UV/Vis Spectrophotometer. During the start up the reactors (30 days) with the sludge seed obtained from Ekbatan wastewater treatment plant, which is located in the west part of the capital city of Tehran, microbial acclimation has been done with a solution of glucose and synthetic wastewater. In this stage, the removal efficiency was reached to 80% for CODaniline /CODglucose = 0.5. After that, the amount of Organic Loading Rate (OLR) was being increased stepwise within 90 days. Duration of reactor operation in acclimation and loading stage is shown in Fig. 2. 2.2. Neural networks ANNs implement algorithms that attempt to mimic a neurological performance, such as learning from past experience, making generalizations from similar situations and producing decisions out of incomplete knowledge of the states, which inherit great
Fig. 2. Duration of reactor operation in acclimation and loading stage.
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Fig. 3. Architecture of neural network model.
complexities and nonlinearities. ANN modeling, a paradigm for computation and knowledge representation is originally inspired by understanding and abstracting the biological structure of neurons and internal operation of human brain. Back-propagation neural network (BPN) is the most representative learning model for ANN. A feed-forward ANN consists of interconnected processing elements, called neurons that are arranged in layers as an input layer with one or more hidden layers and an output layer. A neural network is a network of many simple processors called nodes. Each node has a small amount of local memory. The nodes are connected by connections; each usually carrying numeric data called weights, encoded by any of the existing methods. The nodes operate only on the local data and on the inputs they receive via the connections [16,17]. The main principle of neural computing is the decomposition of the input–output relationship into a series of linearly separable steps using hidden layers [18].Development of ANN model is carried out as follows: • • • • •
Database selection and pre-processing; Selection of model inputs; Selection of model geometry; Model training; Model validation.
Nodes in the input layer represent possible influential factors that affect the network outputs and have no computation activities, while the output layer contains one or more nodes that produce the network output. Hidden layers may contain a large number of hidden processing nodes. A feed-forward back-propagation network propagates the information from the input layer to the output layer, compares the network outputs with known targets, and propagates the error from the output layer back to the input layer, using a learning mechanism to adjust the weights and biases [18]. In general, the net input to each node is calculated as shown in Eq. (1): Njl =
n
Wjil · Xil−1 + ˇjl
(1)
i=1
where Wjil is the weight that connects node j in layer l to node i in
layer l − 1, n is the number of nodes in layer l − 1, ˇjl is a threshold value assigned to node j in layer l, and Xil−1 is the input coming from node i in layer l − 1 to node j in layer l. The net input Njl is then
modified by an activation function f to generate an output value Yjl given by Eq. (2), where f is a nonlinear activation function assigned to each node in the network. Yjl = f (Njl )
(2)
Learning mechanism of this back-propagation network is a generalized delta rule that performs a gradient-descent on the error
space to minimize the total error between the calculated and desired values at the output layer during modification of connection weights. The implementation of this algorithm updates the network weights and biases in direction in which the error decreases most rapidly. Training is accomplished in an iterative manner. Each iteration cycle, involves feed-forward computation followed by an error-backward propagation to modify the connection weights. Convergence depends on the number of hidden layer nodes, learning rate parameters and the size of dataset require creating the proper results. Furthermore, there is no structured algorithm to obtain optimal structure and parameters of neural networks; therefore, one should find optimal network by trial and error [17,18]. Upon successful completion of the training process, a welltrained neural network is not only capable of computing the expected outputs of any input set of data used in the training stage, but should also be able to predict with an acceptable degree of accuracy the outcome of any unfamiliar set of input located within the range of training data [17,19]. 2.2.1. Selection of database The basic methodology for developing an ANN model is to train a neural network to learn the relationship between a set of inputs and corresponding outputs. The selection of the database to train a neural network is of great importance and it must be trained on large and comprehensive set of reliable experimental data. In this study, the database was collected in 3 months continuous period. Reactor was fed with aniline synthetic wastewater. Evaluation efficiency was done at different retention time of 8, 24, 48 and 72 h and different influent COD from 100 to 4000 mg/L. A total of 300 data pairs have been selected from the experimental database, as mentioned earlier. 2.2.2. Selection of model architecture One of the most important factors in a neural network model configuration is to determine the number of hidden layers to be used and the number of neurons in the hidden layer. The number of hidden layers and nodes are usually determined via a trial and error procedure. There are some rules to estimate the number of hidden nodes. According to the method suggested by Anderson and McNeill [10], an upper bound for the number processing nodes in the hidden layers can be calculated by dividing the number of input–output pairs in the training set by the total number of input and output nodes in the network multiplied by a scaling factor between 5 and 10. Larger scaling factors are used for relatively noisy data. In this study, numerous combinations of network geometry from one hidden layer to three hidden layers were experimented. The best results were obtained with a network consisted of two hidden layers. The first hidden layer included 7 nodes while the sec-
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ond hidden layer had only 5 ones. A full connection between nodes in adjacent layers was selected. Hyperbolic Tangent, Gaussian and Sigmoid were used as transfer function in input layer 1 & 2 and output layer, respectively. The best network geometry according to the highest correlation and the lowest Root-Mean-Square (RMS) error of the testing sets is shown in Fig. 3. A feed-forward ANN trained with the back-propagation algorithm was implemented using the software package net (Vesta Services, 2000) [20]. The program allows the user to select many network parameters and hence design flexible neural network architectures.
2.2.3. Model training The training procedure was carried out by presenting the network with the set of experimental data in a patterned format. Each training pattern included the input set of three parameters representing influent feed, influent COD and retention time and the corresponding output set representing reactor efficiency and aniline effluent concentration. The network was presented with the variables in the input vector of the first training pattern, followed by appropriate computation through the nodes in the hidden layers and prediction of appropriate outputs. Upon successful completion of the training process, a well-trained neural network is not only capable of computing the expected outputs of any input set of data used in the training stage, but should also be able to predict with an acceptable degree of accuracy the outcome of any unfamiliar set of input located within the range of training data [17,19]. The errors between the predicted output and target value was calculated and stored. The network was then presented with the second training pattern and so on until the network has gone through all the data available for training the network. RMS of the error was then calculated and back propagated to the network. Biases and weights or connection strength between the nodes were modified during the back-propagation phase such that the RMS errors were reduced. Process of training input–output pair introduction to the network, calculation of the RMS error and finally adjustment of weights and biases to reduce RMS errors were referred to as iteration. This process continued until convergence was achieved or the maximum number of iteration was reached [17,19]. The trained ANN model was represented by the connection weights once the above procedure was converged. This process is illustrated in Fig. 4. In this study, the associated ANNs analyses were carried out with an optimal value of learning rate of 0.0035, momentum of 0.80000 and maximum number of iteration of 20,000 with error goal of 0.020.
2.2.4. Model validation The testing subset data were used to measure the network generalization (i.e. how accurately the network predicts targets for inputs that were not in the training set). This is sometimes referred to as holdout validation. Improperly trained neural networks may suffer from either under-fitting or over-fitting. The former describes the condition when a network that is not sufficiently complex fails to fully detect the signal in a complicated dataset. On the other hand, the latter condition occurs when a network that is too complex may fit the noise, in addition to the signal. This condition must be avoided because it may lead to predictions that are far beyond the range of the training data and produce wild predictions even with noise-free data. There are many reported techniques to avoid under-fitting and over-fitting such as model selection, jittering, early stopping, weight decay and combining networks. The correlation and RMS error values were used as the performance criteria and monitored during training.
Fig. 4. Processing neural network model.
3. Results and discussion 3.1. Experimental results Effect of different retention time (8, 24, 48 and 72 h) on COD removal rate was studied after every step increase in COD. As shown in Fig. 5, at low influent COD (from 500 to 2000 mg/L) the maximum efficiencies were obtained 90% in COD = 2000 mg/L after 3 days. A
Fig. 5. Variation of influent COD in different retention time.
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Table 1 Results of training and testing steps of ANN. ANN model structure
3-2-2-2 3-2-2-2 3-2-2-2 3-2-2-2 3-4-2–2 3-5-3-2 3-5-3-2 3-5-3-2 3-5-3-2 3-5-3-2 3-7-5–2 3-7-5-2 3-7-5-2 3-7-5-2 3-7-5-2 3-7-5-2
Transfer function
S-S-S G-G-S HT-HT-S HS-HS-S G-G-S G-G-S G-HT-S G-HS-S HT-G-S HS-G-S G-HT-S G-HS-S G-G-S G-S-S HS-G-S HT-G-S
Training
Testing 2
Iteration 2
RMS
R
RMS
R
0.0634 0.0293 0.0299 0.0252 0.02 0.02 0.02 0.02 0.02 0.02 0.0199 0.02 0.02 0.02 0.02 0.02
0.95 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99
0.0718 0.0712 0.0591 0.0575 0.085 0.062 0.054 0.079 0.052 0.063 0.056 0.597 0.058 0.058 0.055 0.042
0.93 0.93 0.93 0.95 0.95 0.94 0.96 0.91 0.96 0.94 0.95 0.95 0.95 0.95 0.96 0.96
16,850 10,200 13,209 14,300 12,777 9800 13,926 16,982 8488 8140 7082 7135 7100 9839 7230 6772
S: sigmoid, G: Gaussian, HT: hyperbolic tangent, HS: hyperbolic secant.
decrease in the removal efficiencies was observed for influent COD of 2000–4000 mg/L. It is important to mention that all experiments were done at room temperature and under steady-state conditions.
3.2. ANN model The network was trained to predict MBBR performance for the treatment of aniline synthetic wastewater using a total of 300 training datasets and 120 testing datasets. Identifying the optimal network architecture was studied by changing the number of layers, nodes, transfer function and iteration number and fixed RMS error equal to 0.02. The results of some training and testing of ANN are given in Table 1. According to Table 1 the best results were obtained of (3-7-5-2) structure. Transfer functions for this model were Hyperbolic Tangent, Gaussian and Sigmoid for hidden layer 1 and 2 and output layer, respectively. As shown in the last row, the optimum ANN model has obtained the correlations for the training and testing sets 0.99 and 0.96 and the RMS errors were 0.02 and 0.042, respectively. Table 2 shows training parameters for ANN model that was adjusted for software. The predicted efficiency and effluent aniline concentration values for the training and the testing sets are plotted in Figs. 6–8. Fig. 9(a) and (b) shows convergence characteristics of the ANN model during training and testing phases, respectively. It is clearly seen that the RMS error is high in the early stages of the training process, but decreases in later iterations to reach optimum error that selected for model.
Fig. 6. Targets/outputs vs. pattern sequence (MBBR reactor efficiency).
Table 2 Model training information. Iterations Learn rate minimum Learn rate maximum Learn rate (ETA)a Momentum factor (ALPHA)b
20,000 0.001 0.047 0.0047 0.800000
a If the learning rate is too fast (i.e. eta is too large), network training can become unstable. If eta is too small, the network will learn at a very slow pace. b The momentum factor damps high frequency weight changes and helps with overall algorithm stability, while still promoting fast learning.
Fig. 7. Targets/outputs vs. pattern sequence (effluent aniline concentration).
The final biases and absolute maximum errors and correlation for output nodes are also listed in Table 3 . The correlation between predicted and measured MBBR efficiency and effluent aniline concentration are seen to be satisfactory.
Fig. 8. Net outputs vs. training targets.
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Table 3 Biases, max errors, and correlations for output layer. Node
Bias
1 2
Max error
Correlation
Training data
Test data
Training data
Test data
Training data
Test data
−0.03647 −0.0004
2.4312 −0.04693
5.96 0.043
14.22 0.052
0.99147 0.99540
0.96002 0.94662
months. The neural network models provided good estimates for the efficiency and aniline concentration datasets, which cover a range of data for training and testing purposes. Based on the findings of this investigation, the following conclusions can be drawn:
Fig. 9. RMS error history vs. iteration: (a) training and (b) testing.
• MBBR as an advanced biological process had a proper COD removal efficiency for the treatment of aniline synthetic wastewater. Up to maximum of 90% removal efficiencies were obtained for COD of 2000 mg/L after 3 days. The results of this study are comparable with similar researches in recent years. For example 91% of aniline has been removed using an isolate bacterial strain (Pseudomonas sp.) with an influent concentration of 770 mg/L [7]. But the same removal efficiency was obtained using mixed culture with more than two times higher influent COD in this study. • The proposed model demonstrates the ability of a feed-forward back-propagation neural network to predict the performance of MBBR with sufficient accuracy. The model performed quite well in predicting not only the efficiency of MBBR for the treatment of aniline used in training process, but also those of test data that were unfamiliar to the neural network. • Predicting the efficiency of a biological wastewater treatment rector using analytical and traditional method is difficult to achieve, whereas a trained neural network model can predict such properties easily and accurately. • Even with a limited amount of data, the results with ANNs were quite satisfactory. The correlation of output nodes for training data values was over 99%. It may be concluded that ANN models provided a robust tool for prediction in that the prediction error varied slightly, and smoothly, over the range of data sizes used in training and testing. References
Fig. 10. Performance of ANN model predictions for COD removal efficiency and aniline concentration.
For the validation of the ANN model, the final trained model was called upon to recall the data not used in the training process. A total of 30 data unfamiliar to the network in the range of training datasets, were presented to the ANN model and the network was required to predict the COD removal efficiency and effluent aniline concentration. The results of agreement between measured and predicted values of output nodes are shown in Fig. 10. It can be concluded that the proposed ANN model is adequately able to predict MBBR performance for the treatment of aniline. 4. Conclusions In this paper, the model based on ANNs was developed to predict the effluent concentrations of aniline and MBBR efficiency. The developed model was trained and tested on 8, 24, 48 and 72-h sets of COD and aniline concentration measurements over a period of 3
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Contents lists available at ScienceDirect
Journal of Hazardous Materials journal homepage: www.elsevier.com/locate/jhazmat
Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN) M. Delnavaz, B. Ayati ∗ , H. Ganjidoust Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 30 September 2009 Received in revised form 15 March 2010 Accepted 15 March 2010 Available online 23 March 2010 Keywords: Aniline COD Moving bed biofilm reactor Neural networks
a b s t r a c t In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Aniline is commonly used in a number of industrial processes such as dyes, plastic, paint, pigments, herbicides, pharmaceutical preparation and rubber accelerators production [1–4]. It is an aromatic hazardous compound that readily dissolves in water up to 3.5%. Due to its high solubility in water, risk of possible pollution in water resources especially in case of a chemical spill is increased. Therefore, any wastewater containing aniline is a serious problem and should be treated before discharging to the environment [5]. Wastewater containing aromatic amines such as aniline has been shown to be treated by photo-decomposition, electrolysis, resin adsorption, ozone oxidation, and biodegradation methods. They are not completely decomposed by activated sludge process and also inhibit biodegradation of other chemicals because of their hard degradability. Therefore, further studies on advanced biological processes are necessary [6]. Moving bed biofilm reactor (MBBR) which was introduced about 14 years ago and now is popular in Europe, has been operated successfully in urban and some industrial wastewater treatment plants [7,8]. MBBR is designed to offer the advantages of the biofilm process (compact, stable removal efficiency and simplicity of operation) without its drawbacks (medium channeling and clogging). The idea behind MBBR development was to adopt the best features
∗ Corresponding author at: Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, P.O. Box 14155-4838, Tehran, Iran. Tel.: +98 21 8288 3328; fax: +98 21 8800 5040. E-mail address: ayati [email protected] (B. Ayati). 0304-3894/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jhazmat.2010.03.069
of activated sludge as well as those of biofilter processes in one reactor [8]. In biological wastewater treatment systems such as MBBR, influent characteristics variability might have influence on operational control. Therefore, a biological process modeling is a difficult task as most of the available models are just approximations based on probabilities and assumptions. Recently, artificial neural networks (ANNs) have been increasingly applied in environmental and water resources engineering area [9]. The main advantage of ANNs over physical based models is that they are data-driven. Consequently, unlike traditional parametric models, in an ANN model, it is not required to assume a predefined form for the relation between input and output variables. In these models, it is possible to construct a complex relation between input and output variables with an excellent level of accuracy as compared to the conventional methods [10]. Biological process prediction without limitation of conventional mathematical models is an important advantage of ANN for MBBR operation control. Cristea et al. [11] have studied the capacity of neural networks for the assessment of plant behavior in integrated urban wastewater system simulations. They have proposed that ANN based simulators reveal good accuracy for predicting important process variables and reduction of the simulation time, compared to the first principle wastewater treatment plant (WWTP) simulator. Sadrzadeh et al. [12] have used neural networks to predict separation of lead ions from wastewater using electro-dialysis. They have examined a number of different network structures and architectures to compute the amount of lead ions separation. The results of modeling have shown that there was an excellent agreement between the experimental data and predicted values [12].
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Fig. 1. Schematic picture of MBBR pilot.
ANN modeling for biotreatment of zinc-containing wastewater in a sulfidogenic CSTR was studied by Sahinkaya [13]. This developed model was very successful to match the measured and predicted effluent concentrations of sulfate, COD, acetate and zinc. Prakash et al. [14] used ANNs for predicting copper (II) biosorption efficiency. The performance of the network for predicting the sorption efficiency was found to be very impressive. This paper presents predictive models based on the concept of ANN in predicting aniline treatment efficiency using MBBR in various conditions. 2. Materials and methods 2.1. Reactor setup Cylindrical MBBR reactor was used in this study (Fig. 1) with each reactor internal diameter, height and wall thickness of 10, 70 and 0.4 cm, respectively. Basis of the process is elements of biofilm carrier that are made from different materials such as polyethylene, PVC and porous aggregates. The elements provide a large protected surface area for the biofilm and optimal conditions for the bacteria culture to grow and thrive. The biofilm created around each carrier element protects the bacterial cultures from operating excursions to yield a very robust system for those industrial facilities loaded with process fluctuations. DO ad pH were the only control parameters and essential nutrients (C/N/P = 100/5/1) were added to the
system. The effective depth of wastewater in the reactor was 60 cm filled with up to 50% floating biofilm carrier elements made of LECA (Light Expanded Clay Aggregate) with a density of 0.55 g/cm3 . LECA is a kind of aggregate that has been used in many structural projects in Iran such as light weight concrete. High surface area of this material makes it more suitable as carrier in the biofilm reactor. In order to keep biofilm carrier in the reactor, a sieve (mesh size = 5 mm) was placed at the inlet. The aeration system supplied fine bubbles and provided sufficient mixing to keep the carriers moving in the reactor. Feed synthetic wastewater has been prepared using aniline which was supplied by Merck Company. Aniline served as the sole carbon and energy substrate for the biomass in the MBBR. In order to have C/N/P = 100/5/1 and enough alkalinity for aerobic conditions (6.5–8), necessary nutrients (urea, KH2 PO4 , K2 HPO4 ) were added as supplement feed to the reactors for all experimental trials. The parameters of pH, soluble Chemical Oxygen Demand (COD) filtered through Whattman paper No. 42 and Dissolved Oxygen (DO) were daily measured. Mixed Liquor Suspended Solids (MLSS) and Mixed Liquor Volatile Suspended Solids (MLVSS) were examined on alternative days. Microscopic investigation was done regularly. All laboratory experiments were conducted at room temperature (25–28 ◦ C) and DO concentration was always controlled above 3 mg/L during the filling and reacting cycles by regulating the aeration rate. The pH of the inlet wastewater was kept neutral and MLSS and MLVSS varied between 4000–4500 and 3000–3500 mg/L, respectively. All analytical tests were done as outlined in the Standard Method Handbook [15]. To ensure that aniline has not been converted by other intermediate compounds, COD and aniline concentration tests were performed simultaneously. The aniline concentrations were measured by ultraviolet absorption procedure, as given in the Standard Methods (APHA, 1998), using a Lambda EZ 150 UV/Vis Spectrophotometer. During the start up the reactors (30 days) with the sludge seed obtained from Ekbatan wastewater treatment plant, which is located in the west part of the capital city of Tehran, microbial acclimation has been done with a solution of glucose and synthetic wastewater. In this stage, the removal efficiency was reached to 80% for CODaniline /CODglucose = 0.5. After that, the amount of Organic Loading Rate (OLR) was being increased stepwise within 90 days. Duration of reactor operation in acclimation and loading stage is shown in Fig. 2. 2.2. Neural networks ANNs implement algorithms that attempt to mimic a neurological performance, such as learning from past experience, making generalizations from similar situations and producing decisions out of incomplete knowledge of the states, which inherit great
Fig. 2. Duration of reactor operation in acclimation and loading stage.
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Fig. 3. Architecture of neural network model.
complexities and nonlinearities. ANN modeling, a paradigm for computation and knowledge representation is originally inspired by understanding and abstracting the biological structure of neurons and internal operation of human brain. Back-propagation neural network (BPN) is the most representative learning model for ANN. A feed-forward ANN consists of interconnected processing elements, called neurons that are arranged in layers as an input layer with one or more hidden layers and an output layer. A neural network is a network of many simple processors called nodes. Each node has a small amount of local memory. The nodes are connected by connections; each usually carrying numeric data called weights, encoded by any of the existing methods. The nodes operate only on the local data and on the inputs they receive via the connections [16,17]. The main principle of neural computing is the decomposition of the input–output relationship into a series of linearly separable steps using hidden layers [18].Development of ANN model is carried out as follows: • • • • •
Database selection and pre-processing; Selection of model inputs; Selection of model geometry; Model training; Model validation.
Nodes in the input layer represent possible influential factors that affect the network outputs and have no computation activities, while the output layer contains one or more nodes that produce the network output. Hidden layers may contain a large number of hidden processing nodes. A feed-forward back-propagation network propagates the information from the input layer to the output layer, compares the network outputs with known targets, and propagates the error from the output layer back to the input layer, using a learning mechanism to adjust the weights and biases [18]. In general, the net input to each node is calculated as shown in Eq. (1): Njl =
n
Wjil · Xil−1 + ˇjl
(1)
i=1
where Wjil is the weight that connects node j in layer l to node i in
layer l − 1, n is the number of nodes in layer l − 1, ˇjl is a threshold value assigned to node j in layer l, and Xil−1 is the input coming from node i in layer l − 1 to node j in layer l. The net input Njl is then
modified by an activation function f to generate an output value Yjl given by Eq. (2), where f is a nonlinear activation function assigned to each node in the network. Yjl = f (Njl )
(2)
Learning mechanism of this back-propagation network is a generalized delta rule that performs a gradient-descent on the error
space to minimize the total error between the calculated and desired values at the output layer during modification of connection weights. The implementation of this algorithm updates the network weights and biases in direction in which the error decreases most rapidly. Training is accomplished in an iterative manner. Each iteration cycle, involves feed-forward computation followed by an error-backward propagation to modify the connection weights. Convergence depends on the number of hidden layer nodes, learning rate parameters and the size of dataset require creating the proper results. Furthermore, there is no structured algorithm to obtain optimal structure and parameters of neural networks; therefore, one should find optimal network by trial and error [17,18]. Upon successful completion of the training process, a welltrained neural network is not only capable of computing the expected outputs of any input set of data used in the training stage, but should also be able to predict with an acceptable degree of accuracy the outcome of any unfamiliar set of input located within the range of training data [17,19]. 2.2.1. Selection of database The basic methodology for developing an ANN model is to train a neural network to learn the relationship between a set of inputs and corresponding outputs. The selection of the database to train a neural network is of great importance and it must be trained on large and comprehensive set of reliable experimental data. In this study, the database was collected in 3 months continuous period. Reactor was fed with aniline synthetic wastewater. Evaluation efficiency was done at different retention time of 8, 24, 48 and 72 h and different influent COD from 100 to 4000 mg/L. A total of 300 data pairs have been selected from the experimental database, as mentioned earlier. 2.2.2. Selection of model architecture One of the most important factors in a neural network model configuration is to determine the number of hidden layers to be used and the number of neurons in the hidden layer. The number of hidden layers and nodes are usually determined via a trial and error procedure. There are some rules to estimate the number of hidden nodes. According to the method suggested by Anderson and McNeill [10], an upper bound for the number processing nodes in the hidden layers can be calculated by dividing the number of input–output pairs in the training set by the total number of input and output nodes in the network multiplied by a scaling factor between 5 and 10. Larger scaling factors are used for relatively noisy data. In this study, numerous combinations of network geometry from one hidden layer to three hidden layers were experimented. The best results were obtained with a network consisted of two hidden layers. The first hidden layer included 7 nodes while the sec-
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ond hidden layer had only 5 ones. A full connection between nodes in adjacent layers was selected. Hyperbolic Tangent, Gaussian and Sigmoid were used as transfer function in input layer 1 & 2 and output layer, respectively. The best network geometry according to the highest correlation and the lowest Root-Mean-Square (RMS) error of the testing sets is shown in Fig. 3. A feed-forward ANN trained with the back-propagation algorithm was implemented using the software package net (Vesta Services, 2000) [20]. The program allows the user to select many network parameters and hence design flexible neural network architectures.
2.2.3. Model training The training procedure was carried out by presenting the network with the set of experimental data in a patterned format. Each training pattern included the input set of three parameters representing influent feed, influent COD and retention time and the corresponding output set representing reactor efficiency and aniline effluent concentration. The network was presented with the variables in the input vector of the first training pattern, followed by appropriate computation through the nodes in the hidden layers and prediction of appropriate outputs. Upon successful completion of the training process, a well-trained neural network is not only capable of computing the expected outputs of any input set of data used in the training stage, but should also be able to predict with an acceptable degree of accuracy the outcome of any unfamiliar set of input located within the range of training data [17,19]. The errors between the predicted output and target value was calculated and stored. The network was then presented with the second training pattern and so on until the network has gone through all the data available for training the network. RMS of the error was then calculated and back propagated to the network. Biases and weights or connection strength between the nodes were modified during the back-propagation phase such that the RMS errors were reduced. Process of training input–output pair introduction to the network, calculation of the RMS error and finally adjustment of weights and biases to reduce RMS errors were referred to as iteration. This process continued until convergence was achieved or the maximum number of iteration was reached [17,19]. The trained ANN model was represented by the connection weights once the above procedure was converged. This process is illustrated in Fig. 4. In this study, the associated ANNs analyses were carried out with an optimal value of learning rate of 0.0035, momentum of 0.80000 and maximum number of iteration of 20,000 with error goal of 0.020.
2.2.4. Model validation The testing subset data were used to measure the network generalization (i.e. how accurately the network predicts targets for inputs that were not in the training set). This is sometimes referred to as holdout validation. Improperly trained neural networks may suffer from either under-fitting or over-fitting. The former describes the condition when a network that is not sufficiently complex fails to fully detect the signal in a complicated dataset. On the other hand, the latter condition occurs when a network that is too complex may fit the noise, in addition to the signal. This condition must be avoided because it may lead to predictions that are far beyond the range of the training data and produce wild predictions even with noise-free data. There are many reported techniques to avoid under-fitting and over-fitting such as model selection, jittering, early stopping, weight decay and combining networks. The correlation and RMS error values were used as the performance criteria and monitored during training.
Fig. 4. Processing neural network model.
3. Results and discussion 3.1. Experimental results Effect of different retention time (8, 24, 48 and 72 h) on COD removal rate was studied after every step increase in COD. As shown in Fig. 5, at low influent COD (from 500 to 2000 mg/L) the maximum efficiencies were obtained 90% in COD = 2000 mg/L after 3 days. A
Fig. 5. Variation of influent COD in different retention time.
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Table 1 Results of training and testing steps of ANN. ANN model structure
3-2-2-2 3-2-2-2 3-2-2-2 3-2-2-2 3-4-2–2 3-5-3-2 3-5-3-2 3-5-3-2 3-5-3-2 3-5-3-2 3-7-5–2 3-7-5-2 3-7-5-2 3-7-5-2 3-7-5-2 3-7-5-2
Transfer function
S-S-S G-G-S HT-HT-S HS-HS-S G-G-S G-G-S G-HT-S G-HS-S HT-G-S HS-G-S G-HT-S G-HS-S G-G-S G-S-S HS-G-S HT-G-S
Training
Testing 2
Iteration 2
RMS
R
RMS
R
0.0634 0.0293 0.0299 0.0252 0.02 0.02 0.02 0.02 0.02 0.02 0.0199 0.02 0.02 0.02 0.02 0.02
0.95 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99
0.0718 0.0712 0.0591 0.0575 0.085 0.062 0.054 0.079 0.052 0.063 0.056 0.597 0.058 0.058 0.055 0.042
0.93 0.93 0.93 0.95 0.95 0.94 0.96 0.91 0.96 0.94 0.95 0.95 0.95 0.95 0.96 0.96
16,850 10,200 13,209 14,300 12,777 9800 13,926 16,982 8488 8140 7082 7135 7100 9839 7230 6772
S: sigmoid, G: Gaussian, HT: hyperbolic tangent, HS: hyperbolic secant.
decrease in the removal efficiencies was observed for influent COD of 2000–4000 mg/L. It is important to mention that all experiments were done at room temperature and under steady-state conditions.
3.2. ANN model The network was trained to predict MBBR performance for the treatment of aniline synthetic wastewater using a total of 300 training datasets and 120 testing datasets. Identifying the optimal network architecture was studied by changing the number of layers, nodes, transfer function and iteration number and fixed RMS error equal to 0.02. The results of some training and testing of ANN are given in Table 1. According to Table 1 the best results were obtained of (3-7-5-2) structure. Transfer functions for this model were Hyperbolic Tangent, Gaussian and Sigmoid for hidden layer 1 and 2 and output layer, respectively. As shown in the last row, the optimum ANN model has obtained the correlations for the training and testing sets 0.99 and 0.96 and the RMS errors were 0.02 and 0.042, respectively. Table 2 shows training parameters for ANN model that was adjusted for software. The predicted efficiency and effluent aniline concentration values for the training and the testing sets are plotted in Figs. 6–8. Fig. 9(a) and (b) shows convergence characteristics of the ANN model during training and testing phases, respectively. It is clearly seen that the RMS error is high in the early stages of the training process, but decreases in later iterations to reach optimum error that selected for model.
Fig. 6. Targets/outputs vs. pattern sequence (MBBR reactor efficiency).
Table 2 Model training information. Iterations Learn rate minimum Learn rate maximum Learn rate (ETA)a Momentum factor (ALPHA)b
20,000 0.001 0.047 0.0047 0.800000
a If the learning rate is too fast (i.e. eta is too large), network training can become unstable. If eta is too small, the network will learn at a very slow pace. b The momentum factor damps high frequency weight changes and helps with overall algorithm stability, while still promoting fast learning.
Fig. 7. Targets/outputs vs. pattern sequence (effluent aniline concentration).
The final biases and absolute maximum errors and correlation for output nodes are also listed in Table 3 . The correlation between predicted and measured MBBR efficiency and effluent aniline concentration are seen to be satisfactory.
Fig. 8. Net outputs vs. training targets.
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Table 3 Biases, max errors, and correlations for output layer. Node
Bias
1 2
Max error
Correlation
Training data
Test data
Training data
Test data
Training data
Test data
−0.03647 −0.0004
2.4312 −0.04693
5.96 0.043
14.22 0.052
0.99147 0.99540
0.96002 0.94662
months. The neural network models provided good estimates for the efficiency and aniline concentration datasets, which cover a range of data for training and testing purposes. Based on the findings of this investigation, the following conclusions can be drawn:
Fig. 9. RMS error history vs. iteration: (a) training and (b) testing.
• MBBR as an advanced biological process had a proper COD removal efficiency for the treatment of aniline synthetic wastewater. Up to maximum of 90% removal efficiencies were obtained for COD of 2000 mg/L after 3 days. The results of this study are comparable with similar researches in recent years. For example 91% of aniline has been removed using an isolate bacterial strain (Pseudomonas sp.) with an influent concentration of 770 mg/L [7]. But the same removal efficiency was obtained using mixed culture with more than two times higher influent COD in this study. • The proposed model demonstrates the ability of a feed-forward back-propagation neural network to predict the performance of MBBR with sufficient accuracy. The model performed quite well in predicting not only the efficiency of MBBR for the treatment of aniline used in training process, but also those of test data that were unfamiliar to the neural network. • Predicting the efficiency of a biological wastewater treatment rector using analytical and traditional method is difficult to achieve, whereas a trained neural network model can predict such properties easily and accurately. • Even with a limited amount of data, the results with ANNs were quite satisfactory. The correlation of output nodes for training data values was over 99%. It may be concluded that ANN models provided a robust tool for prediction in that the prediction error varied slightly, and smoothly, over the range of data sizes used in training and testing. References
Fig. 10. Performance of ANN model predictions for COD removal efficiency and aniline concentration.
For the validation of the ANN model, the final trained model was called upon to recall the data not used in the training process. A total of 30 data unfamiliar to the network in the range of training datasets, were presented to the ANN model and the network was required to predict the COD removal efficiency and effluent aniline concentration. The results of agreement between measured and predicted values of output nodes are shown in Fig. 10. It can be concluded that the proposed ANN model is adequately able to predict MBBR performance for the treatment of aniline. 4. Conclusions In this paper, the model based on ANNs was developed to predict the effluent concentrations of aniline and MBBR efficiency. The developed model was trained and tested on 8, 24, 48 and 72-h sets of COD and aniline concentration measurements over a period of 3
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