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.