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Abstract:The aim of this work was to determine the appropriate moisture regime, attained by leachate recycling, to achieve the highest municipal solid waste (MSW) biodegradation rate. To this end, leachate characteristics, methane production rate and changes in degraded refuse were studied. Twenty laboratory-scale bioreactors were loaded with MSW from the landfill of Pátzcuaro (Mexico), four were used as controls and sixteen were operated under leachate recycling to achieve moisture content regimes (%MC) of 50, 60, 70 and 80%, bioreactors operated for 264 days. Hydrolysis, acidogenic and methanogenic phases were determined and studied. ANOVA and Tukey's HDS tests revealed significant differences in leachate concentration characteristics when using different recycling volumes. Maximum methane production rate was found in the 70% MC regime, whereas the highest volume was found to produce a wash out effect in the refuse matrix. Also, the highest total volatile solids removal was found in the solid phase of the 70% MC regime.

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& This article presents the application of a technique of artificial intelligence (AI) that explores the possibility of using a model to estimate the biomethanization of municipal solid waste (MSW). The model uses data from an experiment in which MSW is anaerobically digested under three different moisture regimes by leachate recycling. A method utilizing a neurofuzzy inference system is used because AI systems have a high capacity for empiric learning.Considering the importance of finding an effective selection of the most valuable variables for the model, this methodology includes the following techniques: Exhaustive Search (or brute-force search); Stepwise, a step-by-step regression method; and the use of Expert Knowledge. With the use of the fuzzy logic toolbox (MATLAB 1 ), nine models were generated. However, when a case study is used to detail the method, the proposed methodology can also be used with any other system with a set of input and output data.