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Back-propagationneuralnetworkforperformancepredictionintricklingbedairbiofilter

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382 Int. J. Environment and Pollution, Vol. 28, Nos. 3/4, 2006

Copyright © 2006 Inderscience Enterprises Ltd.

Back-propagation neural network for performance prediction in trickling bed air biofilter

Eldon R. Rene Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai – 600036, Tamilnadu, India E-mail: eldonrene@yahoo.com

Shihabudeen M. Maliyekkal and Ligy Philip* Department of Civil Engineering, Indian Institute of Technology Madras, Chennai – 600036, Tamilnadu, India Fax: +91 44 22578281 E-mail: ligy@iitm.ac.in E-mail: shihab@iitm.ac.in *Corresponding author

T. Swaminathan Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai – 600036, Tamilnadu, India E-mail: tswami@iitm.ac.in

Abstract: Experimental studies were carried out with a laboratory-scale biotrickling filter to treat a gaseous stream contaminated with benzene, toluene and xylene (BTX) operated in a continuous mode. The biotrickling filter initially acclimatised with toluene was used to treat BTX compound individually at loading rates ranging from 7.2 g/m3hr to 62.2 g/m3hr, operated in a sequential mode. The results showed removal efficiencies as high as 100% when operated with toluene as the sole carbon source. An application of the back-propagation neural network to this experimental data is presented in this paper. The performance parameters namely, elimination capacity and removal efficiency were predicted from the experimental observation by selecting the appropriate network topology. The sensitive internal parameters of the network were selected using the 2(k–1) fractional factorial design. The neural-network-based model was found to be an efficient data-driven tool to predict the performance of a biotrickling filter.

Keywords: artificial neural networks; biotrickling filters; performance prediction; back propagation; waste gas treatment; BTX.

Reference to this paper should be made as follows: Rene, E.R., Maliyekkal, S.M., Philip, L. and Swaminathan, T. (2006) ‘Back-propagation neural network for performance prediction in trickling bed air biofilter’, Int. J. Environment and Pollution, Vol. 28, Nos. 3/4, pp.382–401.

Back-propagation neural network for performance prediction 383

Biographical notes: Eldon R. Rene is a Doctoral student at the Department of Chemical Engineering, IIT Madras, India, currently involved in developing lab scale biofilters for the treatment of VOCs from waste gases.

Shihabudeen M. Maliyekkal is a Doctoral student at the Department of Civil Engineering, IIT Madras, India, working on the combined removal of NOx and SOx using biological treatment systems and who also focuses on developing mathematical models.

Ligy Philip, PhD, is an Associate Professor at the Department of Civil Engineering, IIT Madras, India. Her research interests include air and water quality management, toxic metal removal and recovery, bio remediation of pesticide-contaminated soils and developing novel reactors for waste minimisation. She has been actively involved in major consultancy and research projects supported by reputed national and international agencies.

T. Swaminathan, PhD, is a Professor at the Department of Chemical Engineering, IIT Madras, India. His major areas of specialisation are environmental management with special emphasis on hazardous waste management, environmental biotechnology, membrane technology and environmental risk assessment. He has also carried out many industrial consultancy and sponsored research projects.

1 Introduction

The concept of applying a biological treatment system to remove contaminants of environmental significance from process gas streams has increasingly been improved over the recent years. The main advantages of cost efficiency and environmental compatibility over the available conventional treatment techniques have drawn more attention towards biological treatment of volatile organic compounds (VOCs). Among the VOCs, benzene, toluene and xylene (BTX) compounds are used as potential solvents in most of the chemical industries. The emissions of these compounds into the atmosphere have been shown to have a detrimental effect on human health and natural environment. Biological treatment of these vapours applies the concept of biofiltration that is especially governed by the common biofilm process (Swanson and Loehr, 1997). The most widely used biological treatment systems for waste gas treatment are:

• biofilter

• biotrickling filter

• bioscrubber.

The simplicity in the operation of biotrickling filters has resulted in its emergence as a more practical treatment option in the process industry (Sorial et al., 1997). In trickling filters, apart from biofilters, sufficient water (mineral media contains all nutrients for microbial growth) is circulated over an inert packing material so that the aqueous phase is continuously moving downwards. In most cases, the liquid flows down the reactor, while the contaminated gas stream flows counter currently upward to achieve better contact between the gas and liquid phase. The gaseous contaminants are first adsorbed by the free liquid phase followed by diffusion into the biofilm and then biodegradation by the

384 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

microbes. The micro kinetics of this process within the biofilm is relatively complex, which involves a thorough understanding of the state variables, namely biofilm thickness, microbial growth and death rate, partition coefficient, O2 consumption rate, temperature and pH. In this paper, we present the performance of a biotrickling filter treating BTX operated in continuous mode for 55 days. The possibility of using an artificial neural network (ANN) as a predictive tool to indicate the performance parameters in biotrickling filters has been explored with a statistically significant approximation of the network parameters.

1.1 Motivation behind ANN modelling

Significant amount of knowledge-based models have been developed for biotrickling filters explaining the system behaviour under varied operating conditions (Hekmat and Vortmeyer, 1994; Deshusses et al., 1995; Escot et al., 1996; Alonso et al., 1999; Pederson and Arvin, 1999; Sa and Boaventura, 2001; Cox and Deshusses, 2004). The measurement of certain parameters like biofilm thickness, contaminant diffusion rate in the biofilm, O2 consumption rate within the biofilm, etc., is a complex process and involves elaborate kinetic studies. Hence, any incomplete information on this potentially significant micro parameter would lead to misinterpretation of the system performance. In order to overcome these practical difficulties and make the modelling process simpler, data-driven approaches such as fuzzy inference and neural networks can be used as an alternate modelling tool. ANNs such as the three-layer back-propagation network have been proved to be universal function approximators (Hornik et al., 1989; Poggio and Girosi, 1990). ANNs have already been applied to solve and predict a variety of environmental problems: biodegradation kinetics of organic compounds (Schuurmann and Muller, 1994), identifying unknown air pollution sources (Reich et al., 1999), predicting fed batch fermentation kinetics (Valdez-Castro et al., 2003), optimum alum doses in water treatment (Maier et al., 2004) and long term tidal waves (Lee, 2004). Moreover, algorithms such as back-error propagation, resilient propagation, scaled conjugate gradient, Pola-Ribiere conjugate gradient, Levenberg-Marquardt and particle swarm optimisation have been widely used to train the network for prediction purposes (Chau and Cheng, 2002; Chau, 2004a, 2004b; Sozen et al., 2004; Sozen and Arcakliolu, 2005; Chau, 2005; Reddy et al., 2005; Ramirez et al., 2005).

1.2 The ANN approach

Artificial neural networks are powerful data-driven modelling tools that have the ability to capture and represent complex input/output relationships. The development of neural computational techniques emerged from the desire to develop an artificial system that could perform multiple, complex and intelligent tasks similar to those performed by the human brain. ANNs consist of a system of simple interconnected processing elements called neurons. This gives the ability to model any non-linear process through a set of unidirectional weighted connections. The neuron accepts input from single or multiple sources and produces output by a simple calculating process guarded by a non-linear transfer function. A three-layered network with an input layer, hidden layer and output layer is shown in Figure 1. The input layer consists of a set of neurons NI, each representing an input parameter and propagates the raw information to the neuron in the hidden layer (NH), which in turn transmits them to the neurons in the output layer (NO).

Back-propagation neural network for performance prediction 385

Each layer consists of several neurons and the layers are connected by the connection weights (W). The most commonly used transfer function is the sigmoid function as described by

1( ) .1 xf x

e−=+

(1)

Figure 1 Basic structure of an ANN model. χ1–χ4: input vector; y1, y2: output vector

This produces output in the range of 0–1 and introduces non-linearity into the network, which gives the power to capture non-linear relationships. The back-propagation neural network (BPNN) is the most prevalent supervised ANN learning model (Rummelhart et al., 1986). It uses the gradient descent algorithm to correct the weights between interconnected neurons (Maier and Dandy, 2001). During the learning process of the network, the algorithm computes the error between the predicted and specified target values at the output layer. The error function at the output layer can be defined by

21 ( )2

= −∑ d pE O O (2)

where E is the error function, Od the desired output and Op is the output predicted by the network.

1.3 Network parameters

Good network architecture requires selecting the most dependable values of network parameters like: number of hidden layers, NH, f(x), the learning rate of the network (η), epoch size (ε), momentum term (α) and training cycles (TC). The best values for these parameters are normally estimated by a trial and error approach. In some instances, a guideline has been proposed for choosing the upper limit for NH (Hecht-Nielsen, 1987), which is given by

2 1.H IN N≤ + (3)

386 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

In this study, we choose values ranging between 4 and 8, which is less than the upper limit suggested in equation (3). The terms η and α can play an important role in the convergence of the network. The η value of a network affects the size of steps taken in weight space (Maier and Dandy, 1998). If η is too small, the algorithm would take more time to converge. The use of α is to accelerate the convergence of the error during the learning process by adding a fraction to the previous weight update. The values of η and α vary between 0–1 (Hamed et al., 2004). The specifications of network parameters used for training here are shown in Table 1. A detailed study on the effect of internal network parameters on the performance of BPNN and the procedure involved in selecting the best network topology has been described elsewhere (Maier and Dandy, 1998).

Table 1 Specifications of parameters used during network training

Parameter Value

Hidden neurons (NH) 4–8 Training cycles, Tc 1,000–10,000

Learning rate, η 0.4–0.7

Momentum, α 0.1–0.9

Epoch size, ε 30

Learning rate, ηho (between hidden and output layer) 0.95

Confidence interval 95% Error tolerance 0.0001

1.4 Data division and pre processing

A total of 55 data points from the experimental data was divided into two sets; training and testing. Seventy five percent of the data points were used for training the network, while the remaining 25% were used for testing the developed network. The data points in the test set were chosen in such a way that they were representative of the different pollutants treated in the biotrickling filter. The input data were scaled linearly between 0 and 1 to suit the transfer function in the hidden layer. More comprehensible and relevant information on the different types of transfer function, algorithms and internal parameters used for developing ANN models are given elsewhere (Fu, 1994; Haykin, 1999; Hoskins and Himmelblau, 1999; Cheng et al., 2005).

1.5 Model:inputs and outputs

Two ANN-based models were evaluated in this study for predictive purposes. The first model (ANN 1) predicts the output concentration (OC) in the biotrickling filter, while the second model (ANN 2) predicts the performance parameter, elimination capacity (EC, g/m3hr) and removal efficiency (RE, %) directly from the inputs. The EC and RE for any continuously operated biotrickling filter are computed by

( )−= gi goC C Q

ECV

(4)

Back-propagation neural network for performance prediction 387

( )100gi go

gi

C CRE

C−

= × (5)

where Cgi and Cgo are the inlet and outlet pollutant concentrations in the biotrickling filter (g/m3), Q the gas flow rate (m3/hr) and V is the volume of the filter bed (m3). The input vectors to the network were the easily monitored parameters, namely input concentration (χ1), flow rate (χ2), inlet loading rate (χ3) and pressure drop (χ4). The outputs for the two models were outlet concentration (ANN 1), EC and RE (ANN 2). The range of these parameter values used for network training (NTr) and testing (NTe) is shown in Tables 2(a) and 2(b), respectively. The software package NN Model (Version-1.4, Neural Fusion) was used to develop the ANN models.

Table 2 Ranges of values of input and output parameters used for testing and training the network

Training data (NTr = 39) Testing data (NTe = 16) Parameter Minimum Maximum Minimum Maximum (a) Model ANN 1 Input Inlet concentration (g/m3) 0.084 0.756 0.139 0.705 Inlet loading rate (g/m3hr) 7.231 62.279 11.902 60.442 Flow rate (m3/hr) 0.050 0.085 0.050 0.085 Pressure drop (cm of H2O) 2.100 6.800 3.600 6.800 Output Outlet concentration (g/m3) 0.000 0.206 0.017 0.132 (b) Model ANN 2 Input Inlet concentration (g/m3) 0.084 0.756 0.139 0.705 Inlet loading rate (g/m3hr) 7.231 62.279 11.902 60.442 Flow rate (m3/hr) 0.050 0.085 0.050 0.085 Pressure drop (cm of H2O) 2.100 6.800 3.600 6.800 Output Elimination capacity (g/m3hr) 5.928 52.422 8.784 51.699 Removal efficiency (%) 59.472 100.000 72.182 97.151

1.6 Selecting the best model architecture using factorial design

Factorial design of the network parameter was chosen to replace the conventional trial and error approach in determining the optimal or best values during network training. Fractional factorial designs are two-level designs (min and max of the factor), which include only a fraction of the total trial that comprise a full factorial design (Box et al., 1978). It consists of 2k–1 runs (trials), where 2 represents the two levels of the factor and k is the total number of factors. Moreover, when high-order interactions are negligible, reasonable information on the main effects and low-order interactions may be obtained by running only a fraction of the complete factorial design. In this study, the

388 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

four parameters (k = 4), namely NH, TC, η and α were chosen between their minimum and maximum levels as described earlier. The response was the root mean square error (RMS), which substantially determines the closeness of prediction between the desired and predicted output from the network. This is given by

2

1

2

1

( )RMS .

( )

n

d pi

n

pi

O O

O

=

=

−=

∑

∑ (6)

Hence, 2(4–1) design is a fraction of the 24 full fractional designs that consist of 8 runs instead of 16. Simulations done at the centre point were used for calculating the errors and the effects of the main variable and their mutual interactions were analysed using the F (Fisher’s F value), P (Probability value) and T (Student t-test) values. Table 3 indicates the 24–1 fractional factorial design carried out for estimating the best network topology along with the observed RMS in each runs. A total of 18 runs were performed to arrive at the best predictive network. This statistically significant experimental procedure reduces the number of trials and provides more information about the effects of individual factors through the main effects plot (Box et al., 1978; Montgomery, 1991). In order to carry out this analysis and interpret the results, we used the statistical software MINITAB (Version 12.2, PA, USA).

Table 3 2(4–1): fractional factorial design for estimating the best network architecture based on RMS values

Run order NH Tc η α RMS ANN 1 1 4 1,000 0.4 0.1 0.000134 2 8 1,000 0.4 0.9 0.000122 3 4 10,000 0.4 0.9 0.000110 4 8 10,000 0.4 0.1 0.000129 5 4 1,000 1 0.9 0.000113 6 8 1,000 1 0.1 0.000177 7 4 10,000 1 0.1 0.000146 8 8 10,000 1 0.9 0.000110 9 6 5,500 0.7 0.5 0.000157 ANN 2 1 4 1,000 0.4 0.1 0.003167 2 8 1,000 0.4 0.9 0.002261 3 4 10,000 0.4 0.9 0.001860 4 8 10,000 0.4 0.1 0.002594 5 4 1,000 1 0.9 0.002538 6 8 1,000 1 0.1 0.003656 7 4 10,000 1 0.1 0.002775 8 8 10,000 1 0.9 0.001991 9 6 5,500 0.7 0.5 0.002372

Back-propagation neural network for performance prediction 389

2 Materials and methods

2.1 Experimental set-up

The experimental set-up consisted of a laboratory-scale biotrickling filter and an influent gas supply scheme as shown in Figure 2. The biotrickling filter was made of acrylic hollow cylinder (50 cm in bed depth, 5 cm in internal diameter, ~980 ml working volume) and packed with 177 g of Fujjino material (packing material made of light weight PVC, thin sheets of PVC folded to give more or less a spherical shape) with an approximate diameter of 8.0 mm. The reactor was operated in an up-flow counter-current mode. The desired final concentration of BTX in the air stream was obtained by mixing humidified air with the pollutant-laden air, controlled by flow meters. The biotrickling filter was also facilitated with a nutrient feeding system at the top, which supplied nutrients continuously to the system at the flow rate of 24 l/day (0.51 m3/m2hr). The nutrient solution was controlled by using a peristaltic pump (Miclins, PP20, India). The pH of the nutrient medium was maintained at 7.0 ± 0.2 by adding either NaOH or HCl.

Figure 2 Schematic diagram of the experimental set-up of biotrickling filter

2.2 Microbial seed culture and media composition

Activated sludge from a secondary clarifier of a municipal sewage treatment plant (Nesapakam, Tamilnadu, India) was used for the preparation of seed culture. The municipal sewage was allowed to settle for a period of four hours and the settled concentrated sludge was collected. The concentrated sludge along with toluene

390 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

acclimatised microbial consortia, developed in a batch reactor, was directly fed into the filter bed and re-circulated for six hours. For all experiments, a well-defined mineral salt medium with the following composition was used as the carbon-free medium (in 1 litre of distilled water): 1.0 g KH2PO4, 1.0 g K2HPO4, 5.0 g KNO3, 1.0 g NaCl, 0.5 g MgSO4, 0.01 g CaCl2 and 1.0 ml of trace elements (Cox and Deshusses, 2000).

2.3 Analytical methods

Initial and residual concentrations of BTX in gaseous samples were measured by gas chromatography (Model 5765, Nucon gas chromatograph, Nucon Engineering, India) equipped with a flame ionisation detector. A Chromopak stainless steel column (6.56 ft, 1/8" ID, liquid – 10% FFAP, solid – ch – WIHP, 80/100 mesh) was used. Nitrogen was employed as the carrier gas at a flow rate of 25 ml/min. The temperatures at the gas chromatography injection port, oven and detection port were 150°C, 120°C and 150°C, respectively.

3 Results and discussion

3.1 Biotrickling filter studies

Experiments carried out in a continuous up-flow mode operation were divided into five distinct phases to evaluate the adaptability of the biosystem for various toxic compounds viz., BTX (Figure 3). Toluene in the vapour phase was passed through the reactor during the start-up period, to develop the biofilm and adapt the culture for degrading the target pollutant, for a period of three weeks. This development of biofilm in the system was mirrored by an increase in the pressure drop as shown in Figure 4. However, the fluctuations in pressure drop values from 2 cm to 7 cm of H2O can be attributed to the flow rate and changing biomass concentration within the system over the operating period. The complete operational schedule of the biotrickling filter is given in Table 4. Once the system had been stabilised (constant removal profiles), the inlet toluene concentration was varied between 0.17 g/m3 and 0.25 g/m3 at an empty bed residence time (EBRT) of 42 seconds for five days to achieve steady-state conditions. At an EBRT of 72 seconds and concentration of 0.55 g/m3 of toluene, the observed elimination capacity of the filter bed was between 20 g/m3 and 25 g/m3hr. This was reduced to 13–20 g/m3hr when EBRT was changed to 42 seconds. To examine the adaptability of the trickling filter to a different pollutant, the target pollutant was changed to xylene. For this pollutant, the elimination capacity was only 8–20 g/m3hr. The removal efficiency during this phase varied between 70% and 82%. The reason for low pollutant removal at low loading rates might be due to the high toxicity of xylene compared to toluene. Moreover, microorganisms were not well acclimatised to this particular pollutant during the start-up and first operational phase of the biotrickling filter. The system was again exposed to toluene to evaluate its recuperating potential after treating xylene vapors. The elimination capacity during this phase varied between 20 g/m3hr and 30 g/m3hr, which is almost equal to the initial performance of the system. In the fourth phase, the reactor was subjected to benzene at loading rates between 8 g/m3hr and 30 g/m3hr and the system elimination capacity ranged from 5 to 25 g/m3hr. To monitor the recuperation ability of the system and its microbial consortia, toluene was fed to the system at high loading rates

Back-propagation neural network for performance prediction 391

(20–62 g/m3hr). The removal efficiency varied between 80% and 85%, while the elimination capacity ranged from 20 to 52 g/m3hr. It could be observed that a maximum elimination capacity of 52.4 g/m3hr was achieved at a loading rate of 62.2 g/m3hr. The effect of inlet pollutant load on the elimination capacity of the filter bed is shown in Figure 5. A near-linear relationship between these two variables could be noticed for loading rates lesser than 30 g/m3hr. This study also indicates the ability of a toluene-acclimatised microbial population to degrade a variety of other VOCs. Among the VOCs studied, xylene appears to be more toxic than benzene and toluene. Toluene removal in the biotrickling filter was the highest (100%) followed by benzene (86%) and xylene (82%). As evident from our previous batch studies, if the culture was enriched with a high toxic pollutant and being used to treat a less toxic compound, the efficiency might have been higher (Maliyekkal et al., 2004).

Table 4 Scheme of biotrickling filter operation

Phase of operation EBRTa (s) Target pollutant

Days of operation

Max. ILRb

(g/m3hr) ECc

(g/m3hr) 42 1 72

Toluene 15 30.519 29.193

42 2 72

Xylene 15 25.708 19.087

42 3 72

Toluene 10 31.734 28.844

42 4 72

Benzene 10 37.791 27.480

5 42 Toluene 5 62.279 52.422 aEBRT: empty bed residence time. bILR: inlet loading rate. cEC: elimination capacity.

Figure 3 Variations in inlet and outlet concentration in the biofilter (π – Initial concentration: ∆ – final concentration)

392 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

Figure 4 Variations in pressure drop and flow rate during the experimental phase

Figure 5 Effect of inlet loading rate on the elimination capacity of the filter for different pollutants

3.2 ANN modelling

3.2.1 Network architecture

The two models were trained and tested adequately with the experimental data and evaluated by the RMS values between the measured and predicted outputs from the network. From Table 3, it is quite evident that parameter settings at run –3 for both the models gave less RMS values than the other runs as specified by factorial design. However, on a comparison between these two models, the model ANN 1 that predicts the outlet concentration (OC) gave less RMS value (0.00011) than model ANN 2. Statistical analysis in the form of analysis of variance was performed on both the models and their corresponding F, P and T values are shown in Table 5. As indicated by the high F values (ANN 1 – 1.28; ANN 2 – 14.0) and low P values, the main effect of the variable was found to be highly significant than the 2-way interactions. Among the linear effects of the factors on the RMS value, the learning rate (η) and number of neurons in the hidden layer (NH) were found to be rich in significance than the other factors, as seen from their T values (ANN 1 – 0.75, 0.51; ANN 2 – 1.83, 0.27). The main effects plot of the network internal parameters on the RMS value observed during model training is shown in Figures 6 and 7. Increasing the number of neurons in the hidden layer (NH) and learning

Back-propagation neural network for performance prediction 393

rate (η) appears to increase the RMS value (or) decrease the predictive accuracy in both the models. On the other hand, the training cycle (Tc) and the momentum term (α) showed a high influence in decreasing the RMS value during training. Interestingly, the effects of α appear to be more significant at the higher level for both the models. The best network architecture is 4 – 4 – 1 and 4 – 4 – 2 for the models developed in this study, as indicated by run 3 in the fractional factorial design. RMS values were also low during network testing, ranging from 0.069 – 0.091 while predicting the EC and RE of the system. The results from this study indicate that a low-learning rate (η – 0.4), high momentum term (α – 0.9) and a training cycle of 10,000 with 4 neurons in the hidden layer (NH) are favourable values of the internal network parameters.

Table 5 Analysis of variance and effect of individual factors on the RMS value observed after model training

Source Model ANN 1 Model ANN 2

F P F P

Analysis of variance Main effects 1.28 0.484 14.00 0.068 2-way interactions 0.40 0.773 0.51 0.716

Factors T P T P

Effect of individual factors NH 0.51 0.658 0.27 0.809 Tc –0.75 0.531 –4.07 0.055

η 0.75 0.531 1.83 0.209

α –1.93 0.194 –6.00 0.027

Figure 6 The main effects plot of network internal parameters on the RMS value during training of model ANN 1 ( – Centre point)

394 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

Figure 7 The main effects plot of network internal parameters on the RMS value during training of model ANN 2 ( – Centre point)

3.2.2 Predictive potentiality of the models

The performance indices namely EC and RE for model ANN 1 was evaluated by substituting the predicted outlet concentration (OCPred) in equations (4) and (5), whereas these values were directly predicted from model ANN 2. A comparison of the experimental (OCExpt) and predicted (OCPred) outlet concentrations from model ANN 1 is shown in Figure 8 for the best (Run 3) and the worst (Run 6) network parameter setting during training. The fact that four of the OCExpt lies above 0.12 g/m3 makes the model to inefficiently capture the trend (observations 12, 20, 32 and 38 in Figure 8). Moreover, corroborating these points is the low removal of benzene (86%) and xylene (82%) compared to those with toluene (100%) within the trickling bed. This decrease in removal has caused a significant impact in the networks generalisation pattern while predicting the outlet concentration. The EC and RE profiles observed after network training are shown in Figures 9 and 10 for model ANN 2. These results from ANN 2 also show good predictive ability for performance variables as seen from the closeness of the fit between the experimental and predicted observations. The RMS values observed for this model are lower at 0.00186 as inferred from run 3. Once the network is adequately trained, the test data were used to evaluate the performance of the models. The results for EC and RE predicted by the best run (Run 3) for both models are illustrated in the Figures 11 and 12 through line plots. The 16 data points presented were representative of the experimental system adopted in this study. The elimination capacities predicted by both the models showed good correlation with the experimentally observed values. In contrast, except a few, the other data points in Figure 12 of the test data predicting removal efficiency showed good distribution in the domain of the experimental data. The predictive capacity of the network was also evaluated in terms of its relative deviation, (ECexp – ECpred)/ECexp and (REexp – REpred)/REexp. These deviations for the elimination capacity and removal efficiency predicted by both the models are shown in Figures 13 and 14, respectively. The relative deviations are more significant while predicting removal efficiency than elimination capacities. REexp greater than 85% in the test data showed a strong positive deviation, while those below 85% showed less relative deviations. These negative deviations can be attributed to the degradation of the toxic pollutants like benzene and xylene in a toluene-acclimatised biotrickling filter. Moreover, factor like substrate inhibition and low gas residence time would also have contributed to the decrease in REs

Back-propagation neural network for performance prediction 395

of the compounds. The weight and bias term of the hidden layer connections obtained after network training is given in Tables 6 and 7 for the two models. In order to evaluate the significant effect of the input parameters on the developed models, a sensitivity analysis was carried out by estimating the absolute average sensitivity (AAS). The sensitivity is calculated by summing the changes in the output variables caused by moving the input variables by a small amount over the entire training set. The AAS is the absolute values of the change in the input (Zurada et al., 1994). The computed AAS value on different input parameters for the two models is shown in Tables 8(a) and 8(b), respectively. The inlet pollutant concentration and pressure drop (AAS value of 0.3979 and 0.3664) appear to have a significant effect in predicting the outlet concentration in model ANN 1. The elimination capacity predicted from ANN 2 is more sensitive to variations in inlet loading rate (0.4704) than the other parameters, while the removal efficiency is sensitive to variations in pressure drop (0.4597). On the other hand, the predictive variables in both the models are less sensitive to changes in flow rate. The results from this analysis reveal the degree of relevance of the input parameters to the outputs.

Figure 8 Comparison of experimental and predicted values of outlet concentration from model ANN 1 during training (NTr – 39)

Figure 9 Comparison of experimental and predicted values of elimination capacity from model ANN 2 during training (NTr – 39)

396 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

Figure 10 Comparison of experimental and predicted values of removal efficiency from model ANN 2 during training (NTr – 39)

Figure 11 Experimental and predicted values of elimination capacity during model testing (NTe – 16)

Figure 12 Experimental and predicted values of removal efficiencies during model testing (NTe – 16)

Back-propagation neural network for performance prediction 397

Figure 13 Relative deviations ((ECExpt–ECPred)/ECExpt) between predicted (ECPred) and experimental (ECExpt) values of elimination capacity from ANN models for data in the testing set

Figure 14 Relative deviations ((REExpt–REPred)/REExpt) between predicted (REPred) and experimental (REExpt) values of removal efficiency from ANN models for data in the testing set

Table 6 Weights of the trained neural network for the prediction of outlet concentration, ANN 1, (Run no – 3)

Wih Who Neurons χ1 χ2 χ3 χ4 Bias Neurons y

1 0.022 –0.765 –0.837 –0.400 –0.713 1 0.159 2 –1.201 –1.111 –0.832 –0.216 0.207 2 –0.323 3 –0.804 –0.423 –0.767 –1.889 –0.098 3 –0.765 4 –1.459 –0.018 –0.512 –1.132 –0.074 4 –0.675 Bias 0.261

398 E.R. Rene, S.M. Maliyekkal, L. Philip and T. Swaminathan

Table 7 Weights of the trained neural network for the prediction of elimination capacity and removal efficiency, ANN 2, (Run no – 3)

Wih Who Neurons χ1 χ2 χ3 χ4 Bias Neurons y1 y2

1 –1.105 –1.011 –1.932 –3.078 –0.749 1 –0.399 –0.014 2 3.28 4.317 1.392 –18.244 1.278 2 1.779 5.233 3 8.285 0.345 –7.801 –1.228 0.046 3 –2.395 0.241

4 –0.991 –0.342 4 –8.396 13.803 –15.918 2.931 2.271 Bias 1.948 0.795

Table 8 Sensitivity analysis for model input parameters after network training

Parameter Absolute average sensitivity (a) Model ANN 1 Model input Inlet concentration (g/m3) 0.3979 Inlet loading rate (g/m3hr) 0.1389 Flow rate (m3/hr) 0.0966 Pressure drop (cm of H2O) 0.3664 Model output Outlet concentration (g/m3) (b) Model ANN 2 Model input Inlet concentration (g/m3) 0.2533 Inlet loading rate (g/m3hr) 0.4704 Flow rate (m3/hr) 0.2063 Pressure drop (cm of H2O) 0.0699 Model output Elimination capacity (g/m3hr) Model input Inlet concentration (g/m3) 0.3025 Inlet loading rate (g/m3hr) 0.1305 Flow rate (m3/hr) 0.1071 Pressure drop (cm of H2O) 0.4597 Model output Removal efficiency (%)

The predictive ability of the two models was generally good, as ascertained from the RMS value between the measured and predicted outputs in the training and test data. The ANN approach in this study has proved to be highly efficient in predicting the performance of biotrickling filters with minimal error. This work would enable researches to extend and intensify research in ANNs for evaluating pilot-scale biotrickling filters and optimising their state variables.

Back-propagation neural network for performance prediction 399

4 Conclusions

In this paper, a laboratory-scale biotrickling filter acclimatised with toluene was evaluated to remove gas stream containing BTX compounds. In the concentration range studied, toluene was removed better (100%) followed by benzene (86%) and xylene (82%). The outlet concentration, elimination capacity and removal efficiencies in the biotrickling filter were predicted with the help of two ANN-based models. These models were adequately trained with the training data and later tested with a separate test data set. Both the models predicted the performance well with acceptable levels of error. The suitable network architecture for both models were determined with a fractional factorial design of experiments, which consists of the following network topology: 4–4–1 (model ANN 1) and 4–4–2 (model ANN 2). The robustness of ANN for predicting purposes makes it a tool that is worth considering in modelling biotrickling filters.

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