SUMMARY
Municipal solid waste management and low electric energy supplied are two major problems affecting the development of Auchi metropolis. This work was aimed at determining the heat energy from municipal solid waste in Auchi, Nigeria and its potential for electricity generation.
Random truck sampling was used according to American Society for Testing and Materials (ASTM) in the collection of waste to the disposal site and characterized into seven (7) parameters; food waste, plastic waste, paper waste, cotton waste, garden trimming waste, wood waste, and tin cans waste. A sample of 2g weight was measured for experimental analysis of the chemical composition of carbon, nitrogen, sulphur, hydrogen, oxygen, and ash contents from the various municipal solid waste components using classic oxidation, decomposition and reduction technique to determine the heat energy for electricity estimation using Dulong’s model. Statistical design of experiment (DOE) using central composite design (CCD) matrix version (13.0.5.0) was employed to determine the optimum value of each input parameters that will minimize the rate of solid waste disposal and generate heat energy responses using Response Surface Methodology and Artificial Neural Network model. Reliability was produced to test the networks adequacy.
The average composition of the municipal solid waste was 33.3% of food waste, 17.2% of plastic waste, 16.9% of paper waste of 10.3%, textile waste of 13.3% of garden trimming waste, 5.9%, wood waste, and 3.0% tin cans waste. The laboratory results gave heating values of 23,600.05kJ/kg on food waste, 21,572.755kJ/kg on plastics waste, 19,230.02 on paper waste, 23,636.54kJ/kg on garden trimming waste, 22,364.72kJ/kg on textile waste, and 35,420.28kJ/kg on wood waste having total heat energy of 145,831.49kJ/kg. While the central composite design (CCD) matrix produced heat energy value of 26,102.6kJ/kg. A regression plot showing the correlation between the input and output was produced with R2 values of 97% for the training, 87% for the validation, 98% for testing and 97% for the overall. A reliability plot of 80.6% was obtained for response surface methodology (RSM) while a reliability of 81.7% was obtained for artificial neural network (ANN).The study established the heating values for energy potential of the municipal waste components in the area. The results of this research showed that energy recovery is a feasible option as part of an integrated municipal waste management plan in Auchi metropolis, Nigeria.
