Biogas power generation is renewable energy made from biological materials. Biogas power production is technology which helps in development of sustainable energy supply systems. This paper develops Genetic Algorithm optimization model for Biogas electrical power generation of Ilora in Oyo, Oyo state. The production is done using co-digestion system of pig dung and Poultry dung under the process of anaerobic digestion. The pig dung and poultry dung were mixed 50:50%. MATLAB and VISUAL BASIC Software was used to carry out simulations to develop optimized Genetic Algorithm model for Biogas power production with aims to improving electricity accessibility and durability of the community. The results of the research reveal the Empirical Biogas power production without and with Genetic Algorithm optimization. The Result showed that biogas electrical power generated without and with Genetic Algorithm Optimization were 5KW and 11.18KW respectively. The biogas power generation was increased by 6.18KW, which is 38.2% increase after Genetic Algorithm optimization. The results show the application of the Genetic Algorithm optimization model which can be used to improving Biogas power generation when amount of methane gas produced from the animal dung varies with speed of thermal rotating shaft.
A. Chambers, et al., “Distributed Generation: A Nontechnical Guide: Penn Well.
Alfonso D, Perpin˜á C, Pérez-Navarro A, Pen˜ alvo E, Vargas C, Cárde- nas R. Methodology for optimization of distributed biomass resources evaluation, management and ﬁnal energy use. Biomass and Bioenergy 2009;33(8):1070–9.
Araoye T.O, Mgbachi C.A, Ajenikoko G.A. Development of a simplex optimization Techniques for Biogas generation of electrical energy. vol.66, 2018
Damartzis T, Zabaniotou A. Thermochemical conversion of biomass to second generation biofuels through integrated process design—a review. Renewable and Sustainable Energy Reviews 2010, doi: 10.1016/j.rser.2010.08.003.
Du D-H, Pardalos PM. Handbook of combinatorial optimization: supplement volume B. Springer; 2005.
Jebaraj S, Iniyan S. A review of energy models. Renewable and Sustainable Energy Reviews 2006;10(4):281–311.
Lee T-Y, Chen C-L. Wind-photovoltaic capacity coordination for a time of use rate industrial user. IE Transactions on Renewable Power Generation 2009;3(2):152–67.
Lund H. Renewable energy strategies for sustainable development. Energy 2007;32(6):912–9.
Ma T, Nakamori Y. Modeling technological change in energy systems—from optimization to agent based modeling. Energy 2009;34/7:873–9.
Martiskainen M, Coburn J. The role of information and communication technologies (ICTs) in household energy consumption/prospects for the UK. Energy Efﬁciency 2010, doi:10.1007/s12053-010-9094-2.
Naik SN, Goud VV, Rout PK, Dalai AK. Production of ﬁrst and second gener- ation biofuels: a comprehensive review. Renewable and Sustainable Energy Reviews 2010;14(2):578–97.
Pardalos PM, Resende MGC. Handbook of applied optimization. Oxford University Press; 2002.
Reche P, Jurado F, Ruiz N, García S, Gómez M. Particle swarm optimization for biomass-fuelled systems with technical constraints. Engineering Applications of Artiﬁcial Intelligence 2008;21(8):1389–96.
Reche P, García S, Ruiz N, Jurado F. A method for particle swarm optimization and its application in location of biomass power plants. International Journal of Green Energy 2008;5(3):199–211.
Rentizelas A. A, Tatsiopoulos IP, Tolis A. An optimization model for multi-biomass tri-generation energy supply. Biomass and Bioenergy 2009;33(2):223–33.
Renewables 2010. Global status report. Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH
Strupczewskim A. Accident risks in nuclear-power plants. Applied Energy 2003;75(1–2):79–86
Skoglund A, Leijon M, Rehn A, Lindahl M, Waters R. On the physics of power, energy and economics of renewable electric energy Sources-Part II. Renew- able Energy 2010;35(8):1735–40.
Vera D, Carabias J, Jurado F, Ruiz-Reyes N. A honey bee foraging approach for optimal location of a biomass power plant. Applied Energy 2010;87(7):2119–27.
Vine E. Breaking down the silos: the integration of energy efﬁciency, renewable energy, demand response and climate change. Energy Efﬁciency 2008; 1:49–63.
This work is licensed under a Creative Commons Attribution 4.0 International License.
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.
Submission of the manuscript represents that the manuscript has not been published previously and is not considered for publication elsewhere.