Otimização da produção de biogás em biodigestores batelada
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Data
2018-02-23
Autores
Marcucci, Leandro Willian
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Universidade Estadual Paulista (Unesp)
Resumo
A busca por energia tem sido crescente na atualidade. Por outro lado, o processo de obtenção de energia por diversas fontes nem sempre compensa economicamente e ambientalmente. Assim, as fontes renováveis tem ganhado cada vez mais espaço na matriz energética. Neste contexto, os biodigestores tem assumido um papel importante, por serem uma fonte barata de energia e reduzirem os resíduos e dejetos gerados pelo setor agropecuário. Este trabalho visa propor um modelo de otimização para biodigestores do tipo batelada visando maximizar a produção de biogás e ao mesmo tempo minimizar o tempo de retenção hidráulica, restrito a um sistema dinâmico que descreve o processo de conversão da matéria orgânica em biogás, tendo a temperatura como variável do sistema. Para a resolução do modelo de otimização foi proposto o uso das metaheurísticas Algoritmo Genético e Algoritmo Memético, implementado o Método de Runge-Kutta de quarta ordem para resolução do sistema de equações diferenciais de primeira ordem e um ajuste de curva aos dados coletados que descrevem o crescimento bacteriano em função da temperatura. Foram investigadas algumas curvas que descrevem o crescimento microbiano a partir do ajuste das curvas Gaussiana, Fourier e Soma de Senos. Os resultados alcançados indicam que o ajuste de curve feito pela curva Gaussiana se mostrou mais eficaz e que ambos Algoritmos, Memético e Genético, produzem soluções satisfatórias e realísticas. Os experimentos computacionais realizados mostram que a metodologia proposta tem grande potencial como ferramenta de auxílio ao planejamento e operação dos biodigestores em batelada
The search for energy has been growing nowadays. On the other hand, the process of obtaining energy from various sources does not always economically and environmentally compensate. Thus, renountable sources have gained more and more space in the energy matrix. In this context, biodigestors have assumed an important role, as they are an inexpensive source of energy and reduce the waste and waste generated by the agricultural sector. This work aims to propose an optimization model for batch type biodigesters in order to maximize the production of biogas and at the same time minimize the hydrological retention time , restricted to a dynamical system that describes the process of conversion of organic matter into biogas, with temperature varying from the system. In order to solve the optimization model it was proposed the use of the metaheuristic Genetic Algorithm and Memory Algorithm, implemented the fourth Runge-Kutta Method order to solve the system of rst order di erential equations and a curve t to the collected data that describe bacterial growth as a function of temperature. Some curves describing the microbial growth were investigated by adjusting the Gaussian, Fourier and Sum of Sine curves. The achieved results indicate that the curve tting done by the Gaussian curve has proved to be more e cient and that both Algorithms, Memetic and Genetic, produce satisfactory and real solutions. isticas. The computational experiments carried out show that the proposed methodology has great potential as an aid tool for the planning and operation of batch biodigesters.
The search for energy has been growing nowadays. On the other hand, the process of obtaining energy from various sources does not always economically and environmentally compensate. Thus, renountable sources have gained more and more space in the energy matrix. In this context, biodigestors have assumed an important role, as they are an inexpensive source of energy and reduce the waste and waste generated by the agricultural sector. This work aims to propose an optimization model for batch type biodigesters in order to maximize the production of biogas and at the same time minimize the hydrological retention time , restricted to a dynamical system that describes the process of conversion of organic matter into biogas, with temperature varying from the system. In order to solve the optimization model it was proposed the use of the metaheuristic Genetic Algorithm and Memory Algorithm, implemented the fourth Runge-Kutta Method order to solve the system of rst order di erential equations and a curve t to the collected data that describe bacterial growth as a function of temperature. Some curves describing the microbial growth were investigated by adjusting the Gaussian, Fourier and Sum of Sine curves. The achieved results indicate that the curve tting done by the Gaussian curve has proved to be more e cient and that both Algorithms, Memetic and Genetic, produce satisfactory and real solutions. isticas. The computational experiments carried out show that the proposed methodology has great potential as an aid tool for the planning and operation of batch biodigesters.
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modelos matemáticos, otimização, biodigestores, biogás