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Research Article Open access CC BY 3.0

Utilization of Genetic Algorithm to Optimize Biogas Production from Livestock Waste to Use in a CHP Plant in Agricultural Farms

Mostafa Kamalinasab, Alireza Vakili

Biotechnology Journal International · pp. 1149–1164 · Published 28 Sep 2014

10.9734/BBJ/2014/12755

Abstract

Utilization of Genetic Algorithm to Optimize Biogas Production from Livestock Waste to Use in a CHP Plant in Agricultural Farms The optimization of biogas production with respect to external influences and various process disturbances is essential for efficient plant operation. However, the optimization of such plants is a challenging issue due the underlying nonlinear and complex digestion processes. One approach to solving this problem is to use the flexibility and power of computational intelligence methods such as Genetic Algorithms (GAs). The present study utilizes GA as tools for simulating and optimizing of biogas production process. Considering the effect of digester operational parameters, such as temperature (T), total solids (TS), volatile fatty acid (VFA), pH and A/TIC-ratio (amount of Acids (A) compared to Total Inorganic Carbon (TIC)), the optimal amount of biogas was converged to be 53910 cubic meters per month. In order to reach the main goals on the energy problems, it is important to study and analyze the distributed CHP plant for agricultural companies and farms. This paper also describes a feasibility study of a biogas CHP (Cogeneration of Heat and Power )plant in a cow farm in Iran. With the developed model, it is specified that using 53,910 cubic meters of biogas, an internal combustion engine with electrical power of 375 kW can be operated continuously. Obtained results illustrated how the utilization of gaseous product from cow farm effluent (biogas) as fuel for heat and power generation can reduce primary energy consumption and its associated costs.Genetic algorithm

Genetic algorithm optimization biogas CHP system economic model

References (12)

  1. 1 Adaptation in Natural and Artificial Systems [DOI]
  2. 2 Optimization of Control Parameters for Genetic Algorithms [DOI]
  3. 3 Genetic Algorithms and Engineering Design [DOI]
  4. 4 Genetic Algorithms in Search, Optimization & Machine Learning
  5. 5 Evolutionary computation: comments on the history and current state [DOI]
  6. 6 Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm [DOI]
  7. 7 Optimal Sizing for Residential CHP System [DOI]
  8. 8 Alternative Fuels for Spark-Ignition Engines [DOI]
  9. 9 Technical and economic feasibility study of using Micro CHP in the different climate zones of Iran [DOI]
  10. 10 The expansion of biogas fuelled power plants in Germany during the 2001-2010 decade: Main sustainable conclusions for Poland
  11. 11 Genetic algorithms in search, optimization, and machine learning [DOI]
  12. 12 The Behavior Of Adaptive Systems Which Employ Genetic And Correlation Algorithms. [DOI]

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