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Vol.4 , No. 6, Publication Date: Nov. 16, 2017, Page: 71-77
 Approach&description=The modelling of anaerobic co-digestion of household food solid wastes and wastewater are complex and this is due to the rigorous processes that take place during the digestion process. The development of a predictive model that is capable of the simulation of anaerobic digester (AD) performances can go a long way in helping the operation of the AD processes and the optimization for biogas yield. The artificial neural networks (ANNs) approach is considered to be suitable and straightforward modelling method for AD process. In this research work, a multi-layer ANNs model with six input layer, ten hidden layers was trained using Lavenberg-Marquardt back propagation algorithm to simulate the digester operation and to predict the outcome of biogas yield. The performance of the developed ANNs models was validated and the results obtained from the research work reveal the effectiveness of the model to predict biogas yield with a mean squared error (MSE) of best validation performance of 5.1 x 10<sup>-4</sup>. Moreover, the anticipated artificial neural networks model has a close correlation between the outputs and the targets. The outcome of the results showed that the R values of the training set, the testing set, the validation set and the all data set were found to be high, the values being 0.97193, 0.96510, 0.98378 and 0.97229 respectively.&picture=http://www.aascit.org/aascit/decorators/img/journal/925.jpg)
| [1] | Ejiroghene Kelly Orhorhoro, Department of Mechanical Engineering, University of Benin, Benin City, Nigeria. |
| [2] | Patrick Okechukwu Ebunilo, Department of Mechanical Engineering, University of Benin, Benin City, Nigeria. |
| [3] | Godwin Ejuvwedia Sadjere, Department of Mechanical Engineering, University of Benin, Benin City, Nigeria. |
The modelling of anaerobic co-digestion of household food solid wastes and wastewater are complex and this is due to the rigorous processes that take place during the digestion process. The development of a predictive model that is capable of the simulation of anaerobic digester (AD) performances can go a long way in helping the operation of the AD processes and the optimization for biogas yield. The artificial neural networks (ANNs) approach is considered to be suitable and straightforward modelling method for AD process. In this research work, a multi-layer ANNs model with six input layer, ten hidden layers was trained using Lavenberg-Marquardt back propagation algorithm to simulate the digester operation and to predict the outcome of biogas yield. The performance of the developed ANNs models was validated and the results obtained from the research work reveal the effectiveness of the model to predict biogas yield with a mean squared error (MSE) of best validation performance of 5.1 x 10-4. Moreover, the anticipated artificial neural networks model has a close correlation between the outputs and the targets. The outcome of the results showed that the R values of the training set, the testing set, the validation set and the all data set were found to be high, the values being 0.97193, 0.96510, 0.98378 and 0.97229 respectively.
Keywords
Biodegradable Organic Waste, Anaerobic Co-digestion, Artificial Neural Networks (ANNs), Optimization, Predictive Model, Simulation
Reference
| [01] | Orhorhoro, E. K., Ebunilo, P. O., Tamuno, R. I. and Essienubong, I. A., “The Study of Anaerobic Co-Digestion of Non-Uniform Multiple Feed Stock Availability and Composition in Nigeria”, European Journal of Engineering Research and Science, (EJERS), 1 (1), 2016, 39-42. |
| [02] | Peter, M. K. Alfa, M. I., Datau, G., Aluwong, K. C., and Hadi, K. C., “Design, Development, and Performance Evaluation of an Anaerobic Plant”, American Journal of Engineering Research (AJER), 6 (4), 2017, 28-33. |
| [03] | Orhorhoro, E. K., “Performance Study of a Biogas Pilot Plant using Domestic Waste from Benin Metropolis”, Master Thesis, University of Benin, Nigeria, 2014. |
| [04] | Sajeena, B. B., Jose, P. P., and Madhu, G., “Effect of Total Solid Concentration on Anaerobic Digestion of the Organic Fraction of Municipal Solid Waste”, International Journal of Scientific and Research Publications, 3 (8), 2013. 1–5. |
| [05] | Orhorhoro, E. K., Ebunilo, P. O., and Ikpe, A. E., “Effect of pH on Anaerobic Digestion (AD) of Organic Municipal Solid Waste in Benin City”, Nigeria Journal of the Nigerian Association of Mathematical Physics, 36 (1), 2016, 369-374. |
| [06] | Santos-Dueñas, I. M., Hornero, J. E. J., Cañete-Rodriguez, A. M., and Garcia-Garcia, I., “Modeling and optimization of acetic acid fermentation: A polynomial-based approach”, Biochemical Engineering Journal, 99, 2015, 35-43. |
| [07] | Orhorhoro, E. K., Ebunilo, P. O. B., and Sadjere, E. G., “Design of Bio-Waste Grinding Machine for Anaerobic Digestion (AD) System”, European Journal of Advances in Engineering and Technology, 4 (7), 2017, 560-568. |
| [08] | Vavouraki, A. I., Angelis, E. M., Kornaros, M., “Optimization of thermos chemical hydrolysis of kitchen wastes”, Waste Management, 33, 2013, 740–745. |
| [09] | Wang, L. H., Wang, Q., Cai, W., Sun, X., “Influence of mixing proportion on the solid-state anaerobic co-digestion of distiller’s grains and food waste”, Biosystem Engineering, 112, 2012, 130–137. |
| [10] | Wang, L. H., Shen, F., Yuan, H., Zou, D., Liu, Y., Zhu, B., Li, X., “Anaerobic co-digestion of kitchen waste and fruit/ vegetable waste: Lab-scale and pilot-scale studies”, Waste Management, 34, 2014, 2627–2633. |
| [11] | Syaichurrozi, L., Sumardiono, S., “Predicting kinetic model of biogas production and biodegradability organic materials: biogas production from vinasse at variation of COD/N ratio”, Bioresource technology, 149, 2013, 390-397. |
| [12] | Subramani, T., Nallathambi, M., “Mathematical Model for Commercial Production of Bio-Gas from Sewage Water and Kitchen Waste”, International Journal of Modern Engineering Research (IJMER), 2 (4), 2012, 1588-1595. |
| [13] | Sewsynker, Y., Gueguim, K. E. B., Lateef, A., “Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs)”, Biotechnol Biotechnol Equip, 29 (6), 2015, 1208–1215. |
| [14] | Santos-Dueñas, I. M., Hornero, J. E. J., Cañete-Rodriguez, A. M., Garcia-Garcia, I., “Modeling and optimization of acetic acid fermentation: A polynomial-based approach”, Biochemical Engineering Journal, (99), 2015, 35-43. |
| [15] | Yeshona, S., Funmilayo, F., Evariste, B., Gueguim, K., “Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review)”, Biotechnology & Biotechnological Equipment, 31 (2), 2017, 221-235. |
| [16] | Whiteman, J. K., Gueguim, K. E. B., “Comparative assessment of the Artificial Neural Network and Response Surface Modelling efficiencies for biohydrogen production on sugar cane molasses”, Bioenergy Res., 7 (1), 2015, 295–305. |
| [17] | Tardast, A., Rahimnejad, M., Najafpour, G., “Use of Artificial Neural Network for the prediction of bioelectricity production in a membrane less microbial fuel cell”, Fuel, 117, 2014, 697–703. |
| [18] | Tardast, A., Rahimnejad, M., Najafpour, G., “Prediction of bioelectricity production by Neural Network”, Journal of Biotechnol Pharm Res, 3 (3), 2012, 62–68. |
| [19] | Prakasham, R. S., Sathish, T., Brahmaiah, P., “Imperative role of Neural Networks coupled Genetic Algorithm on optimization of biohydrogen yield”, Int J Hydrog Energy, 36, 2011, 4332–4339. |
| [20] | Parthiban, R., “Parthiban. Back propagation Neural network modeling approach in the anaerobic digestion of wastewater treatment”, International Journal of Environmental Sciences, 2 (4), 2012, 44-51. |
| [21] | Osita, O. O., Lawan, O. U., “The Optimum Mesophilic Temperature of Batch Process Biogas Production from Animal-based Wastes”, Research Journal of Applied Sciences, Engineering and Technology, 8 (16), 2014, 1772-1776. |
| [22] | Nimas, M. S., Minming, Z., Zhezi, Z. Dongke, Z., “Effect of Biochar Addition and initial pH on Hydrogen Production from the First Phase of Two-Phase Anaerobic Digestion of Carbohydrate Food Waste. Energy”, Procedis-Elsevier Journal, 105, 2017, 379-384. |
| [23] | Mahanty, B., Zafar, M., Park, H. S., “Characterization of Co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models”, Environ Technol. 34, 2013, 2145–2153. |
| [24] | Karthic, P., Joseph, S., Arun, N., “Optimization of biohydrogen production by Enterobacter species using Artificial Neural Network and response surface methodology”, Journal of Renewable Sustainable Energy, 5 (3), 2013, 33-104. |
| [25] | Edmond, D., Etleva, J., Valdete, V., Erta, D., Enkeleida, S., “Temperature and Stirring Effect of Biogas Production from Two Different Systems”, American Journal of Energy Engineering, 5 (2), pp. 6-10. |
| [26] | Behera, S. K., Meher, S. K., Park, H. S., “Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste”, Clean Technologies and Environmental Policy, 2014, 1-11. |
| [27] | Dhussa, A. K., Sambi, S. S., Kumar, S. Kumar, S., Kumar, S., “Nonlinear Autoregressive Exogenous modeling of a large anaerobic digester producing biogas from cattle waste”, Bioresource Technology, Vol. 170, 2014, 342-349. |
| [28] | Yetilmezsoy, K., Turkdogan, F. I., Temizel, I., Gunay, A., “Development of Ann-Based Models to Predict Biogas and Methane Productions in Anaerobic Treatment of Molasses Wastewater”, International Journal of Green Energy, 10 (9), 2012, 885-907. |
