Development of a Genetic Algorithm Optimization Model for Biogas Power Electrical Generation

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.


I. INTRODUCTION
Bioenergy is renewable energy obtainable from materials derived from biological sources such as animal manure.This renewable energy supply is biological material from living organisms including plants and animals.The foremost promising amongst the renewable energy sources is Biomass, but there is more research to prove that power generation from biomass is both economically and technically viable.Biomass may be burned to provide steam for creating electricity, or to provide heat to industries and homes.In addition, biomass may be regenerate to alternative usable forms like methane series gas, ethanol fuel and biodiesel fuel.Biomass power plants [16].
The fact that fossil fuel resources required for the energy generation are becoming scarce and that climate change is related to carbon emissions to the atmosphere has increased interest tremendously in energy saving and environmental protection [20].The minimizing energy consumption depends on fossil resources by applying energy savings programs focused on energy demand reduction and domestic fields spheres and energy efficiency in industrial [7], [10].Renewable energy technologies have less competitive than traditional electric energy conversion systems, mainly because of their relatively high maintenance cost and intermittency.The benefits of renewable energy sources are the reduction in carbon emissions to the atmosphere and reduction in dependence on fossil fuel resources.Furthermore, renewable energies source prevent the safety problems derived from atomic power [17], which is why, renewable energy power plants has become more desirable and acceptable to adopt from the social point of view [18].The most important decision businesses and governments are to establish renewable energy systems in a given place and to decide the best of renewable energy source or combination of sources.Lund et al. [8] analyzed strategies for a sustainable development of renewable energy observing three major technological changes: the energy savings on the side of demand, improvements of efficiency in energy production, and the replacement of fossil fuels with many sources of renewable energy.The renewable energy technologies improvement will assist sustainable development and give several solutions to energy related environmental problems.
In this sense, algorithms improvement represents an acceptable and best tool for resolution advanced issues within the renewable energy systems field.
Optimization can be defined as a discipline with finding inputs of a function that minimize or maximize its value, which always subjected to constraints [12].Combinatorial optimization is a branch of optimization which deals with the discrete variables of function optimization [5].Computational optimization is the process of designing implementing, and testing algorithms for analyzing a large quantity of optimization problems.Computational optimization includes the disciplines of mathematics model formulation, model the system to research operations, computer science for design algorithmic and analysis, and model implementation of software engineering.

II. RELATED WORKS
Energy resources are very important and crucial for the development of the nation, which is why change in energy system technological is a very inevitable factor that researchers need to deal with [9].In the many papers optimization methods was propose for solving problems found in renewable energy systems.

Development of a Genetic Algorithm Optimization Model for Biogas Power Electrical Generation
Timothy Oluwaseun Araoye, C. A. Mgbachi, Olushola Adebiyi Omosebi, Oluwaseun Damilola Ajayi and Adeleye Qasim Olaniyan Reference [13] presented a binary PSO-based method to achieve biomass optimal location fueled systems for distributed power generation with biomass source of forest residues, and the results out performed those obtained by a GA when maximizing index profitability taking into technical constraints.Reference [15] proposed an optimization method for multi-biomass energy conversion applications dealing with various technical, regulatory, social and logical constraints.Also PSO has been applied for the optimal location and supply area for biomass-based power plants where the maximum electric power generated by the plant is considered as a constraint [14].Reference [19] applied an algorithm for the optimal location of a biomass power plant with the aim of providing the best profitability for investors which is nature-inspired.Reference [3] develops simplex optimization model to optimize the biogas energy.The results reveal the economics important with increase in power output.Reference [11] discovered an interesting review of the first and second generations of biofuels from the sustainable point resources.There are some promising alternatives among this second generation of biofuels, such as thermochemical conversion of biomass to biofuels.However, the modeling and optimization of the process integration methods to demonstrate an effective way for the exploitation of these interactions which requires the complexity of the conversion [4].Reference [2] developed a method to assess optimal management and energy use of distributed biomass resources, considering features such as biomass resources properties, plant size effect, heat and solid biofuels generation, CO 2 emissions balance, available technologies for power, and quantification of potential biofuel consumers.
Some authors have reviewed different types of models such as emission reduction models, renewable energy models, energy supply demand models, forecasting models, energy planning models, and control models using optimization methods [6], For this reason, this paper Develop a Genetic Algorithm optimization model for improving Biogas Electrical Power generation.

III. GENETIC ALGORITHM
Genetic Algorithm is a technique with general principles that generated from the genetic mechanisms in living being populations and evolution of natural systems.This principle includes the solution of population maintenance to a problem (genotype) that evolve in the individual time information [1].
Therefore, genetic algorithm has basic three operators which include (a) The operator recombination also called crossover, which selects two individuals within the crossover site and the generation and moves a swapping operation of the strings bits to the crossover site of both individual's right hand [7].
Recombination operations synthesize bits of gained from both parents exhibiting better than average performance.Hence, increase the probability of the offspring more productive (b) the production operator, which produces one or more duplicate of any individual that possess high fitness value.(c) Mutation operator always acts as a background operator which can be used to explore some of invested point in space by flipping randomly a 'bit' in a population strings.

A. Proposed Algorithm
The Algorithm used in this paper for analyzing the Biogas electrical power system is presented in the Visual Basic model for proper analysis of animal dung used and the optimization method which was programmed in MATLAB/Simulink.
The power output is formulated in order to consider the power flow in the thermal engine when the mass of dung varies with operational load.The optimization is ascertained from the result gotten from the experimental research done in Ilora, Oyo, Oyo State.The Algorithm is designed in such a way that the system will trigger the thermal engine to work with high power at low methane gas.

B. Materials and Method
This research paper develops an optimization model for Ilora in Oyo state with the aims of improving accessibility and durability of Electricity in the community.The waste materials used in the production of biogas include: Pig dung which was obtained from Slaughter house, Ilora, Oyo state, and poultry manure collected from BODFEM farms, Ilora, Oyo state.This system was designed calculating daily power generated under thermophilic condition.The pigs dung was mixed with water in the ratio 1:1.Poultry dung was also mixed with water in the ratio 1:1.The pig slurry and poultry slurry were mixed 50:50%.The mixture was fed into the digester through an inlet pipe in the inlet tank and the slurry flow to the digester vessel for digestion.The methane gas produced through fermentation in the digester is collected in the Gas holder.The digested slurry flows to the outlet tank through the main pipe.The slurry then flows through the overflow opening in the outlet tank to the compost pit.The gas is supplied from the gas holder to the gas Compressor which generates output Power.In Fig. 2 shows the visual Basic used to Analysis the gas flow rate when the system is on full load and Power generated.The optimization of power generated is done using Genetic Algorithm.The mass of dry solid in waste is given by: The volume of Biogas is given by: The volume of fluid in the digester is given by: The volume of the digester is given by: The Energy generated is given as: Where Hb is the heat of combustion per unit volume biogas, ɳ is the combustion efficiency of burners.Where R is the biogas yield per unit dry mass of whole input 0.2-0.4m 3 kg -1 and M0 is the mass of manure input.Where Vf is the flow rate of the digester fluid and tr is the retention time in the digester.Where ρm is the density of dry matter in the fluid.Where Na is the number of animals that produced the dung and Cw the solid in waste per animal per day/kg.

D. Optimization Modeling
The empirical or measured data collected from Ilora, Oyo State was used to develop a Mathematical model for the optimizing power generated.The result obtained from Linear Programming optimization is embedded in Genetic Algorithm toolbox of MATLAB, in order to obtain the best Biogas Electrical power generation.Maximize Where P is the generated electric biogas power X1 is mass of the dung X2 is volume of the biogas X3 is generated electric energy   IV.DISCUSSION OF RESULTS The result of Genetic Algorithm optimization is represented in Fig. 3 to 9. Fig. 3 shows designed Simulink model for optimizing power production in biogas based electrical power generation with and without using genetic algorithm.After processing the 150kg of dung, the volume of biogas generated is 36m 3 .The electric generated power is 5KW/day.135kg of manure produced 32.4m 3 of biogas and 4.5KWh/day of electrical Power.120kg, 105kg, 90kg, 75kg, 60kg, 45kg, 30kg, and 15kg mass of dungs produced 28.8m 3 , 25.2m 3 , 21.6m 3 , 18m 3 , 14.4m 3 , 10.8m 3 , 7.2m 3 and 3.6m 3 of biogas and thus generated 4KW/day, 3.5KW/day, 3KW/day, 2.5KW/day,2KW/day, 1.5KW/day, 1KW/day, and 0.5KW/day of electrical Power respectively.With optimized Genetic Algorithm the Electrical Power generated is 1.118KW/day, 2.236KW/day, 3.354KW/day, 4.472KW/day, 5.59KW/day, 6.708KW/day, 7.708KW/day, 7.826KW/day, 8.944KW/day, 10.05KW/day and 11.18KW/day and mass of dungs 150kg, 135kg, 120kg, 105kg, 90kg, 75kg, 60kg, 45kg, 30kg, 15kg was produced respectively.Therefore, there is positive linear relationship between the Masses of dung used, the volume of the biogas produced and the electrical Power produced.
Fig. 4 shows optimized genetic algorithm result.The optimized result obtained were input in the genetic algorithm and run in MATLAB environment to ascertain the authenticity of the result and it gave the same power output of 0.5KW.Fig. 5 shows result of the optimized mathematical model.The result shows that the mass of the dung X1 is 0.0295 kg, volume of the biogas X2 is 0.0160m 3 and the generated biogas electric power output is 0.5kw.Hence the Linear optimization result is embedded in Genetic Algorithm toolbox of MATLAB.
Fig. 6 shows empirical power output without using optimized genetic algorithm.The result shows that there is positive relationship between mass of animal dung and the power generated.Fig. 7 shows biogas power output using optimized genetic algorithm.The result indicates the increase in power output of Biogas electrical plant Fig. 8 shows comparing power output without and with optimized genetic algorithm.The result reveals that there is increase in Biogas power output when Optimized Genetic Algorithm is used.The result of the experimental power output was 5KW while the result of Optimized genetic algorithm is 11.18KW with percentage improvement of 38.2%.Fig. 9 shows the Optimized Genetic Algorithm model for Biogas Electrical Power generation.The Genetic optimization model is given by: 6 .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.

V. CONCLUSION
A Genetic Algorithm optimization model for Biogas Electric power generation in Ilora, Oyo state has been formulated.The Amount of methane gas in Biogas production will affect Thermal rotating shaft of Biogas Electrical Plant.Therefore, the more the methane gas in the Biogas thermal engine the greater the power produced.The mixture of pig dung and poultry dung were used to prepare the digester of Biogas Electrical power generation in the same proportion.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/day and 11.18KW/day respectively.The biogas power generation was increased by 6.18KW/day, 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 power generation when amount of methane gas varies with speed of thermal rotating shaft.
Published on February 16, 2019.T. O. Araoye and C. A. Mgbachi are with the Department of Electrical and Electronics Engineering, Enugu State University of Science and Technology, Enugu, Nigeria (e-mail: timmy4seun@yahoo.com).O. A. Omosebi is with the Department of Works and Services, Federal College of Education, (special), Oyo, Nigeria.O. D. Ajayi and A. Q. Olaniyan are with the Department of Electrical and Electronics Engineering, University of Ibadan, Ibadan, Nigeria.

Fig. 2 .
Fig. 2. Visual basic GA optimization model for Biogas Electrical Power

Fig. 3 .
Fig. 3. Simulink for Genetic Algorithm optimization for Biogas Electrical Power Generation.
of methane gas produce from Biogas plant.z= Thermal rotating shaft of Biogas Electrical Plant.y = Total Electrical Power generated Araoye Timothy Oluwaseun is a postgraduate student of Power system engineering and renewable energy in the department of Electrical and Electronics Engineering of Enugu State University of Science and Technology, Enugu, Nigeria C.A Mgbachi is a senior Lecturer of power and computer system engineering in the Department of Electrical and Electronics Engineering Enugu State University of Science and Technology, Enugu, Nigeria.He has published over forty international journals accruing from numerous research activities.He has attracted and successfully completed many research grants.Omosebi Olushola Adebiyi is a senior Engineer of federal college of Education, (special), Oyo in the Department of Works and Services.Ajayi Oluwaseun Damilola is a postgraduate student of Renewable Energy and Power system in the department of Electrical and Electronics Engineering of University of Ibadan, Nigeria Olaniyan Adeleye Qasim is a postgraduate student in the department of Electrical and Electronics Engineering of University of Ibadan, Nigeria.