Long-Term evaluation of biogas energy potential based on the neuronal network approach

Abstract

The energy potential of biogas is estimated from the biomass quantity, that is, a biodegradability values obtained from the organic fraction of municipal solid waste (MSW). In this study, the percentage contribution of each and every type of waste was quantified according to the waste classification., In addition, the waste generation data was projected by applying both artificial neural network (ANN) and mathematical models and 4 types of biomass wastes which accounts for a contribution of about 63% of the total waste sampled were obtained. The projection of the weights of the waste was carried out from 2015 to 2030, with the application of the neural network model with Back-propagation. All in all, under the application of the mathematical models, it has been shown that the Ecuadorian model predicted not only a high average volume, but also a large annual value of biogas energy.

Greenhouse gas emissions by the biogas from the Abandoned Solid Waste Final Disposal Site in City of Veracruz, Mexico

“…To determine the weighted methane generation index (k) was based on the Basic Diagnosis for the Integral Management of Waste of the Ministry of the Environment and Natural Resources of the Government of Mexico for the year 2021 for the state of Veracruz [3] and the values of k Mexican Biogas Model Version 2.0 for the Southeast Region and Sotavento Region in Veracruz State, tabulated as presented by Susunaga-Miranda in her PhD thesis in Environmental Sciences in 2023 (Table 5) [23]. The MCF Methane correction factor for the Abandoned Sanitary Landfill of the City of Veracruz, which had waste management with a medium height of 12.5 m has a value of 1.0 and the potential for methane generation L0 is 54.5 m³/Ton and the correction value for fires F given the severity of the constant fires that occurred will have a value of 1 [24].…”

A Perspective on Emerging Inter-Disciplinary Solutions for the Sustainable Management of Food Waste

“…In artificial intelligence and machine learning, neural networks have widely recognized patterns and delivered predictions based on previous experiences and acquired information [148]. The deployment of a neural network model enables data projection that could support the development of a machine learning algorithm to provide a more accurate estimation of results for biogas generation.…”