ARTICLE IN PRESS Resources, Conservation and Recycling xxx (2009) xxx xxx

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1 Resources, Conservation and Recycling xxx (2009) xxx xxx Contents lists available at ScienceDirect Resources, Conservation and Recycling journal homepage: Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm H. Abu Qdais a,, K. Bani Hani b,1, N. Shatnawi a a Civil Engineering Department, Jordan University of Science & Technology, P.O. Box 3030, Irbid , Jordan b Civil and Architecture Engineering Department, Qatar University, P.O. Box 2713, Doha, Qatar article info abstract Article history: Received 10 March 2009 Accepted 30 August 2009 Available online xxx Keywords: Biogas Digester Waste Artificial neural networks Optimization Genetic algorithms Jordan Artificial neural networks (ANNs) and genetic algorithms (GA) are considered among the latest tools that are used to solve complicated problems that cannot be solved by conventional solutions. The present study utilizes the ANN and GA as tools for simulating and optimizing of biogas production process from the digester of Russaifah biogas plant in Jordan. Operational data of the plant for a period of 177 days were collected and employed in the analysis. The study considered the effect of digester operational parameters, such as temperature (T), total solids (TS), total volatile solids (TVS), and ph on the biogas yield. A multi-layer ANN model with two hidden layers was trained to simulate the digester operation and to predict the methane production. The performance of the ANN model is verified and demonstrated the effectiveness of the model to predict the methane production accurately with correlation coefficient of The developed ANN model was used with genetic algorithm to optimize the methane size. The optimal amount of methane was converged to be 77%, which is greater than the maximum value obtained from the plant records of 70.1%. The operational conditions that resulted in the optimal methane production were determined as temperature at 36 C, TS 6.6%, TVS 52.8% and ph Elsevier B.V. All rights reserved. 1. Introduction Anaerobic digestion is a process in which the biodegradation of organic matter occurs in the absence of dissolved oxygen. It is a well established and world-wide applied technology (DeBaere, 2000) to stabilize municipal sewage sludge, treat organic wastes, products and wastewaters from industries, households and farms. The main product of this process is a group of gases (biogas), which consists mainly of methane gas (50 60%) and carbon dioxide (30 40%) with some other trace gases, such as hydrogen sulfide and ammonia. The resulted methane gas is a highly energetic biogas which is used in combined heat and power generators. The development of biogas technology took place at the beginning of the 19th century. However, owing to the energy crises of the 1970s, anaerobic digestion technology underwent significant development (Alvarez, 2003; Strik et al., 2005). Anaerobic digestion systems for fermentation of organic matters used widely with commercial digesters of m 3, small units are used mainly for heating, while large units for generation of electricity. Much of the technology is based in Europe, with Germany Corresponding author. Tel.: ; fax: address: hqdais@just.edu.jo (H. Abu Qdais). 1 On leave from Jordan University of Science and Technology. P.O. Box 3030, Irbid Jordan. and Denmark leading the field (Nickolas et al., 2004). According to Nacke et al. (2005), by the end of 2005, in Germany there were more than 2000 biogas plants with different sizes. The time during in which the mixture of wastewater stay in the digester with microbial population to produce the biogas is called the hydraulic retention time (HRT). This time is very important in the design of biogas digesters (Jatinder and Sarbjit, 2004). Anaerobic bacteria (the methanogens), are sensitive to the acid concentration where the optimum ph value found within a range of (Nickolas et al., 2004) also according to Nickolas the concentration of ammonia increases at the end of the processes so the ph level will be between 7.2 and 8.2. Temperature is an important factor that determines the rate of digestion. Most of the digesters are operated in the mesophilic range (30 35 C), but it is possible to operate the digesters in the thermophilic range (approximately 55 C) with higher operating costs, lower process stability, and more structural requirements (de la Rubia et al., 2002). The carbon to nitrogen (C/N) ratio for optimal biogas production should be of the range When a batch of waste received with high C/N ratio, this implies a complex organic matter which is not easily biodegradable; therefore, some adjustment is required, like the addition of high content of nitrogenous waste. On the other hand, low C/N ratio needs straw or crop residues to be added, so as to adjust the carbon content (El-Hinnawi and Biswas, 1981) /$ see front matter 2009 Elsevier B.V. All rights reserved. doi: /j.resconrec

2 2 H. Abu Qdais et al. / Resources, Conservation and Recycling xxx (2009) xxx xxx According to Koelsch et al. (2001) the total solids content range is about 8 13% and 80% of the solids are volatile solids. One-half of the volatile solids (the biodegradable ones) are converted to methane and carbon dioxide. Typical solid separation of the effluent will remove 4% of the solids from the effluent. About one-third of the solids are converted to gas, one-third can be separated out mechanically, and one-third remains in the separated liquid effluent (Mattocks and Mark, 2000). An important step in the operation of biogas digesters is the control of the digestion process to maximize the methane production from the waste biodegradation process. Artificial neural networks are massively parallel computational models for data representation and information processing. Neural network-based have attributes that make them potentially successful in dealing with most of simulation and prediction problems. They are capable of learning complex highly nonlinear relations and associations from a large body of data due to their intrinsic nonlinearity, adaptability, noise immunity, generalization ability and robustness. ANNs are powerful data modeling that are able to capture and represent complex input/output relationships like the case in anaerobic digestion. Therefore, ANN lends itself as an efficient tool to control and simulate the anaerobic digestion process to produce the biogas. Neural networks have been used in anaerobic digestion systems to describe trace gases (Strik et al., 2005), controlling the addition of NaHCO 3 buffer (Guwy et al., 1997), digester start up and recovery (Holubar et al., 2003), advanced control and prediction of biogas (Holubar et al., 2002) Holubar et al. (2002) used different ANNs to model and control the production of methane from anaerobic continuously stirred tank reactors that were operated under different organic loading rates. It was concluded that the developed models could effectively predict the gas production and composition from the reactors. Strik et al. (2005) developed ANN model to predict trace gases in biogas stream such as hydrogen sulfide and ammonia. The model was capable to predict the trace gases successfully even under dynamic conditions. In their study Ozkaya et al. (2007) presented a neural network model for predicting the methane fraction in landfill gas originating from field-scale landfill bioreactors with and without leachate recirculation. The methane fraction in landfill gas from the bioreactors was modeled using a two layer ANN. Parameters such as ph, alkalinity, chemical oxygen demand, sulfate, conductivity, chloride and waste temperature were used as model input parameters. The study determined the optimal architecture of the ANN and recommended further development of ANN to be applied in predicting the hourly methane production from landfills and optimizing the leachate recirculation strategy. Zhou et al. (2001) introduced a way for optimizing low nitrogen oxides (NO x ) combustion process for a pulverized coal utility boiler by using ANN and GA. ANN was used to describe the NO x emissions, while GA was used to optimize the solution of the ANN model. The main objective of this study is to utilize the ANN as a tool for simulating, monitoring and controlling the production of methane biogas from the digester of Russeifah biogas plant in Jordan. In addition, the trained ANN model is employed in optimizing the production of methane from the reactor by using the GA tool is another objective of the study. Genetic algorithm (GA) is a biologically inspired computational model that imitates the natural processes of evolution and adaptation to exhibit a complex computational behavior (Holland, 1975 and Goldberg, 1989). This computational model does not require prior knowledge of the problem domain or the solution space. This feature limits the designer and makes problems more complex and insufficiently understood. In engineering design, the computational model using the genetic algorithm differs fundamentally from using the traditional approach. In the traditional approach the design problem is modeled as mathematical problem and use mathematical solution technique, but in the genetic algorithm no need to separate the modeling and solution parts 2. Biogas plant description Russeifah biogas plant belongs to Jordan Biogas Company (JBC). It is located at a closed Russeifah landfill site, about 5 km east to Amman City. The main objective of the plant is to reduce the green house gas emissions form the landfill, as well as utilizing the fresh organic waste in the production of methane gas for power generation. The plant consists of two parts. The first part receives the biogas from wells of the closed landfill, while the second part receives the biogas from the digester, where fresh organic waste is received and subjected to biomethanization process. The primary objective of the present study is to optimize the digestion process in the second part of the plant. The digester daily capacity is 60 tons of organic waste. The main waste received by the plant is coming from slaughterhouse, restaurants, fruits and vegetable markets, and from dairy industry. Fig. 1 shows the layout of the digestion part of the plant. 3. Development of ANN model Operational parameters of Russaifah biogas plant were collected and acquired from the plant records for the year The data Fig. 1. Layout of the digestion process at Russaifah biogas plant.

3 H. Abu Qdais et al. / Resources, Conservation and Recycling xxx (2009) xxx xxx 3 Table 1 Plant operating parameters that are used in the development of ANN model. Unit Parameter No. of Data Mean Max Min SD Mode Mixing tank % Total solids (TS) % Volatile solids (VS) ph value Temperature ( C) % Total solids (TS) Reactor % Volatile solids (VS) ph value % Total solids (TS) Buffering tank % Volatile solids (VS) Fig. 2. Architicture of the ANN model. collected included total solids (TS), total volatile solids (TVS) and ph of the feed materials to the digester. In addition, data on the operating temperature of the reactor were obtained. The collected data were subjected to a screening process to decide which are the most consistent and relevant operational parameters that should be used in the development of the ANN model. Statistical analysis for the selected data was conducted to identify the data gap and to interpolate the missed data. A group of operational parameters with each having 177 daily data points were selected as shown in Table 1. In order to introduce the data into the neural network model to simulate the plant operations, scaling of the data was performed. The scaling level depends on the maximum value of each category of data. Doing that, all of the screened data were converted into ranges of [0 1], so as to be suitable for treatment by the sigmoid activation function in the ANN. Collected data were fed to initial ANN model and connection weights were adjusted using back-propagation (BP). The training method (BP) was conducted using the ANN toolbox embedded in MATLAB (2004). In order to obtain the best model that simulates the digester operations with minimal errors, several ANN models were generated and tested. A sigmoid activation function was used as it is the most popular function that describes nonlinear relationships such as the case in biogas production (Kanat and Saral, in press). An ANN model with two hidden layers (each includes 25 neurons) and sigmoid activation functions were able to simulate the digester operation with a good accuracy. The model topology and architecture are depicted in Fig. 2. Fig. 3. Training and performance curve for the reactor model. solids and ph, while the output was the fraction of methane (%CH 4 ) in the produced biogas stream from the digester. The model training process at 100 epochs and 177 data point for each of the input parameters revealed a performance MSE of (Fig. 3). To enhance the ANN and to decrease the value of error during the simulation process, the simulation was conducted by using time-series data learning in which the model output is set as a time dependent for the previous three days in concern. The produced model was 4. Results and discussion The inputs to the ANN model were selected to be four operational parameters, namely temperature, total solids, total volatile Fig. 4. Simulation and validation of methane production from the digester.

4 G Model 4 H. Abu Qdais et al. / Resources, Conservation and Recycling xxx (2009) xxx xxx operational parameters to optimal level as given in Table 2, will lead to increase of methane products by 6.9%. Almost similar increase value was obtained by Misi and Foster (2001) by digesting of a mixture of agricultural wastes. 5. Conclusion and recommendations Fig. 5. Actual versus predicted methane fraction from the digester. validated using a set data for another 50 operating days that were not used in the training of the original model. Fig. 4 illustrates the simulation and the validation processes for the methane production from the digester. It can be seen that the ANN was capable of prediction the methane fraction very accurately, which implies that the ANN learned the relation between the input to the digester and the methane fraction in the biogas stream very well. The correlation between the data used in the validation process and the predicted values of methane fraction is presented in Figs. 4 and 5. The relatively high determination coefficient (R 2 ) value of 0.87 suggests that the modeled values of the methane fit well with the measured values used for validation. GA is a class of parallel iterative and global search algorithm with certain learning ability by using crossover and mutation operators to solve optimization problems (Gen, 1997). In an attempt to obtain the optimal combination of the digester operational parameters for maximum methane production, the ANN digester model was integrated with a GA model. To achieve that, the output of the developed ANN was utilized in calculating the values of the fitness function for the GA routine available in MATLAB Genetic Algorithm toolbox. First, the GA model was run without constraints with a default value of 0.8 for crossover fraction, and default population size of 20. This returned a best fitness value of about 74%. Another run was carried out with a population size of 200 and crossover fraction equals to This has resulted in an increase of the fitness value to 75.2%. It was concluded that changing the constraints of the GA problem has a direct and important effect on the enhancement of the optimization. Upper and lower boundaries were changed iteratively and the values of fitness function were studied according to the corresponding changes. Values of the best combination of biogas parameters are also summarized in Table 2. These values were selected according to the maximum value of CH 4 %. The maximum value of methane fraction was found to be 77% as obtained by GA optimization process. Comparing this value of the methane, with the maximum value of 70.1% of the methane produced in the real digester, implies that controlling the digester Table 2 Optimal values of digester operational parameters for maximum methane production as determined by GA optimization process. Parameter Temperature ( C) Total solids (mg/1) 6.59 Volatile solids (mg/1) ph 6.4 Optimal value In this study a diagnostic model based on neural logic was developed to simulate the production of biogas from the digester of biogas plant in Jordan. The back-propagation ANN model with two hidden layers and sigmoid activation functions was found to capture most of the important patterns in methane generation from the biogas digester as it fitted well with the measured methane fraction. The validation of ANN model was also found highly correlating to the real data with determination coefficient (R 2 = 0.87), which suggests that the ANN is a useful tool in predicting biogas production from digesters. The study also illustrated the importance of model learning with history in accurate description of the methane production process. Integration of the ANN model with GA model resulted in identification of the optimal operational digester parameters that lead to increase of methane yield by 6.9%. The study demonstrated that ANN and GA are useful tools for simulating and optimizing the biogas production from biogas digester under various operational conditions. It is recommended that future studies should be directed to study the impact of the feed waste composition on the operational parameters, as well as, on the amount of the biogas generated from the digester. The results obtained from this study should be tested and verified on the full scale operation of the plant in order to optimize the methane generation. References Alvarez JM. Biomethanization of the organic fraction of municipal solid wastes. 1st ed. IWA Publishing; 2003, 1. DeBaere L. 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