Analysis of the behavior for operation parameters in the anaerobic digestion process with thermal pretreatment, using fuzzy logic.

This article presents a study that identifies the variables with greatest impact on the biogas and methane production over a process with thermal pretreatment, to accelerate anaerobic digestion process in sewage sludge in a water treatment plant, for a poultry processing factory, by using fuzzy logic. The designed fuzzy logic model includes 688 inference rules, with a correlation of 99.3% between prediction data against experimental data, for the biogas variable; and 97% for the methane variable. The predictions of the fuzzy logic model were analyzed with response surface models, and it is concluded that the temperature and operating time variables are mutually determining in the biogas and methane production. Likewise, this research provides a methodology for the design of an expert decision support system that allows to evaluate and optimize a mesophilic anaerobic digestion process through a previous thermal treatment in order to improve the yields of biogas and methane in the treatment of effluent sludge from agroindustry. These results propose to diffuse logic as a reliable tool to make comparisons, and predictions for operation variables management on the treatment of residual sludge with thermal pretreatment on anaerobic digestion.

Use of artificial neuronal networks for prediction of the control parameters in the process of anaerobic digestion with thermal pretreatment.

This article focuses on the analysis of the behavior patterns of the variables involved in the anaerobic digestion process. The objective is to predict the impact factor and the behavior pattern of the variables, i.e., temperature, pH, volatile solids (VS), total solids, volumetric load, and hydraulic residence time, considering that these are the control variables for the conservation of the different groups of anaerobic microorganisms. To conduct the research, samples of physicochemical sludge were taken from a water treatment plant in a poultry processing factory, and, then, the substrate was characterized, and a thermal pretreatment was used to accelerate the hydrolysis process. The anaerobic digestion process was analyzed in order to obtain experimental data of the control variables and observe their impact on the production of biogas. The results showed that the thermal pre-hydrolysis applied at 90°C for 90 min accelerated the hydrolysis phase, allowing a significant 52% increase in the volume of methane produced. An artificial neural network was developed, and it was trained with the database obtained by monitoring the anaerobic digestion process. The results obtained from the artificial neural network showed that there is an adjustment between the real values and the prediction of validation based on 60 samples with a 96.4% coefficient of determination, and it was observed that the variables with the major impact on the process were the loading rate and VS, with impact factors of 36% and 23%, respectively.