Determination of Calorific Values from Municipal Solid Waste as a Potential for Electricity Generation using Artificial Neural Network

Abstract One option of utilizing municipal solid waste is energy recovery. Municipal solid waste management low electric energy supplied is major problems affecting the development of Nigeria. In order to evaluate the feasibility of energy recovery as an integral part of a solid waste management system, it is of great importance to determine the energy content or calorific value (CV) of the solid waste. This work aimed at determination of heat energy from municipal solid waste and its potential for electricity generation. Hence, the municipal waste streams were characterized into four parameters to determine the heating value of the various municipal waste components with Dulong’s model. Predict and optimize the heat energy response using Artificial Neural Network.  Solid waste sampling and analysis from the characterized waste components food waste, wood waste, plastic waste, and cotton waste in the Benin municipality were carried out to determine the waste composition and proximate analysis (moisture, content, volatile matter, ash content and fixed carbon) according to the random sampling method based on the American society of Testing and Materials (ASTM) standard. (2) Grams sample of the various municipal waste component were prepared for laboratory experimentation for energy estimation. Central composite design matrix version (13.0.5.0) for 30 experimental runs was applied for optimization and prediction of Heat Energy response from with Artificial Neural Network model. The result of the study shows that the Dulong’s model produced calorific value for heat energy of 22,354.7195kJ/kg. While the Central composite design matrix produces Heat Energy value of 29,897.8kJ/kg. A regression plot showing the correlation between the input and output is produced with R-values of 97% for the training, 87% for the validation, 98% for testing and 97% for the overall. Reliability was produced to test the networks adequacy a reliability plot of 89.8% was obtained for Artificial Neural Network. As municipal solid waste is a potential energy source, the analysis shows heat values ranging from 10024.1kJ/kg to 29897.8kJ/kg which indicate the feasibility of waste to energy plan to produce electricity. The study established the calorific values for energy potential of the municipal waste components in the area.