• J.O. Rico-Contreras División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba
  • A.A. Aguilar-Lasserre División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba
  • J.M. Méndez-Contreras División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba
  • G. Cid-Chama División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba
  • G. Alor-Hernández División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba
Keywords: bioenergy, poultry litter, calorific value, anaerobic digestion, anaerobic co-digestion, direct combustion, artificial neural network


Poultry industry identifies an area of opportunity to generate bioenergy by using poultry litter. It is produced at broiler chicken farms for use it as biomassic fuel for to implement the bioenergetic technology most profitable (anaerobic digestion, anaerobic co-digestion, or direct combustion). The adequate control variables like external temperature (°C), stay days, density per square meter, extractors, foggers, shading, handling, coverage, lining, feeder, watering, fans, area, improving quality of poultry litter and in consequently is reduced the moisture content. These variables are used for the development of artificial neural network (ANN), to control the system that affects the moisture content in the poultry litter. The results of model artificial intelligence show that the variables that most impact the moisture content of the poultry litter are handling, number of extractors, and density per square meter, control contributes to improving conditions the production of farm and reduce the percentage moisture content of less than 25%. By using Montecarlo Simulation, it is performed a risk analysis that includes the results of artificial neural network whose best economic alternative is the bioenergy generation through direct combustion.


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How to Cite
Rico-Contreras, J., Aguilar-Lasserre, A., Méndez-Contreras, J., Cid-Chama, G., & Alor-Hernández, G. (2020). MOISTURE CONTENT PREDICTION IN POULTRY LITTER TO ESTIMATE BIOENERGY PRODUCTION USING AN ARTIFICIAL NEURAL NETWORK. Revista Mexicana De Ingeniería Química, 13(3), 933-955. Retrieved from
Simulation and control

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