Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

Abstract

Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.