FUZZY MODELLING AND OPTIMIZATION OF ANAEROBIC CO-DIGESTION PROCESS PARAMETERS FOR EFFECTIVE BIOGAS YIELD FROM BIO-WASTES

Year 2020, Volume: 5 Issue: 2, 43 - 61, 16.10.2020

Aniekan Ikpe Akanu-ıbiam Ndon Promise Etim

Abstract

In this study, Adaptive Neuro Fuzzy Inference System (ANFIS) was employed in the modelling and optimization of anaerobic process parameters from co-digestion of bio-waste (food waste and Pig slurry) with different masses at constant water content. In six different experimental scenarios, mixture ratios of the bio-waste and water were 0.5:1, 1:1, 2:1, 2.5:1, 3:1 and 3.5:1. The range of parameters measured from the experimental process were used as input variables in the ANFIS model. Five experimentally measured parameters that led to maximum biogas yield as well as ANFIS input parameters and their corresponding output results in terms of maximum biogas yield were selected for validation. Optimum bio-digester temperature of 38oC, pH of 7.1, Hydraulic Retention Time (HRT) of 11 and mixture ratio of 2:1 in the experiment process produced overall maximum biogas yield of 247g while optimum input parameters such as bio-digester temperature of 40oC, pH of 7.1, HRT of 11 and mixture ratio of 2:1 in the ANFIS model produced overall maximum biogas yield of 248g. There was proximity between the experimental and predicted results, indicating that ANFIS model can be used as alternative tool for optimizing anaerobic process parameters from multiple feedstocks for desired biogas yield.

Keywords

Biogas yield, Bio-wastes, Mixture ratio, Water content, Modelling and optimization

References

• [1] Ikpe, A. and Owunna, I. Review of Municipal Solid Waste Management Technologies and Its Practices in China and Germany. International Journal of Technology Enhancements and Emerging Engineering Research 2016; 4(5): 1-7.
• [2] Ikpe, A. E., Ndon, A. E. and Etim, P. J. Assessment of the Waste Management System and Its Implication in Benin City Metropolis, Nigeria. Journal of Applied Research on Industrial Engineering 2020; 7(1): 79-91.
• [3] Ebunilo, P. O., Okovido, J. and Ikpe, A. E. Investigation of the energy (biogas) production from co-digestion of organic waste materials. International Journal of Energy Applications and Technologies 2018; 5(2): 68-75.
• [4] Ikpe, A. E., Imonitie, D. I. and Ndon, A. E. Investigation of Biogas Energy Derivation from Anaerobic Digestion of Different Local Food Wastes in Nigeria. Academic Platform Journal of Engineering and Science 2019; 7(2): 332-340.
• [5] Ramachandran, A., Rustum, R. and Adeloye, A. J. Review of Anaerobic Digestion Modelling and Optimization Using Nature-Inspired Techniques. Processes 2019; 7(953): 1-12.
• [6] Hosseini, S. and Khaled, A. A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Applied Soft Computing 2014; 24: 1078-1094.
• [7] Khaled, A. A. and Hosseini, S. Fuzzy adaptive imperialist competitive algorithm for global optimization. Neural Computing and Applications 2015; 26(4): 813-825.
• [8] Tay, J. and Zhang, X. (1998) Neural fuzzy modelling of anaerobic biological wastewater treatment systems. Transactions on Ecology and the Environment, UK: WIT Press, 1998.
• [9] Zadeh, L. A. Fuzzy sets. Information and Control 1965; 8(3): 338-353.
• [10] Turkdogan-Aydınol, F. I. and Yetilmezsoy, K. A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. Journal of Hazardous Materials 2012; 182: 460-471.
• [11] Salehi, K., Khazraee, S. M., Hoseini, F. S. and Mostafazadeh, F. K. Laboratory Biogas Production from Kitchen Wastes and Applying an Adaptive Neuro Fuzzy Inference System as a Prediction Model. International Journal of Environmental Science and Development 2014; 5(3): 290-293. [12] Addario, M. D. and Ruggeri, B. Fuzzy approach to predict methane production in full-scale bioreactor landfills. Environmental Research and Technology 2018; 1(1): 4-13.
• [13] Regoa, A. S. C., Leiteb, S. A. F., Leiteb, B. S., Grilloc, A. V. and Santosa, B. F. Artificial Neural Network Modelling for Biogas Production in Biodigesters. Chemical Engineering Transactions 2019; 74: 25-30.
• [14] Zareei, S. and Khodaei, J. Modelling and Optimization of Biogas Production from Cow Manure and Maize Straw using an adaptive neuro-fuzzy interference system. Renewable Energy 2017; 14: 423-427.
• [15] Huang, Z., Wu, R., Yi, X., Liu, H., Cai, J., Niu, G., Huang, M. and Ying, G. A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process. Complexity 2019; 1-13.
• [16] Loussifi, H., Nouri, K. and Braiek, N. B. A new efficient hybrid intelligent method for nonlinear dynamical systems identification: the wavelet kernel fuzzy neural network. Communications in Nonlinear Science and Numerical Simulation 2016; 32: 10-30.
• [17] Yi, X., Zhang, C., Liu, H. Occurrence and distribution of neonicotinoid insecticides in surface water and sediment of the Guangzhou section of the Pearl River, South China. Environmental Pollution 2019; 251: 892-900.
• [18] Wei, X. and Kusiak, A. Optimization of Biogas Production Process in a Wastewater Treatment Plant. Proceedings of the 2012 Industrial and Systems Engineering Research Conference, USA: University of Iowa, 2012.
• [19] Kana, E. B. G., Oloke, J. K., Lateef, A. and Adesiyan, M. O. Modelling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm. Renewable Energy 2012; 46: 276-281.
• [20] Jørgensen, P. J. Biogas-Green Energy: Process, Design, Energy supply, Environment. 2nd Edition, Digisource Danmark A/S, Faculty of Agricultural Science, Aarhus University, Denmark, 2009.
• [21] Adelekan, B. A. and Bamgboye, A. I. Effect of Mixing Ratio of Slurry on Biogas Productivity of Major Farm Animal Waste Types. Journal of Applied Biosciences 2009; 22: 1333-1343.
• [22] Deepanraj, B., Sivasubramanian, V. and Jayaraj, S. Biogas Generation through Anaerobic Digestion Process- An Overview. Research Journal of Chemistry and Environment 2014; 18(5): 80-93.
• [23] Vogeli, Y., Lohri, C. R., Gallardo, A., Diener, S. and Zurbrugg, C. Anaerobic Digestion of Biowaste in Developing Countries. Practical Information and Case Studies. Dubendorf, Switzerland, 2004.
• [24] Gaby, J. C., Zamanzadeh, M. and Horns, S. J. (2017) The Effect of Temperature and Retention time on Methane Production and Microbial Community Compisition in Staged Anaerobic Digesters fed with Food Waste. Biotechnol Biofuels 2017; 10(302): 1-13.