Forecasting greenhouse gas emissions from coal-based resource in power plant using a nonsupervisory artificial neural network

Abstract

Machine learning can be a game-changer for a global warming prediction. About 75% global greenhouse gas (GHG) emissions cause by energy sector and this indicate a major concern to global warming community. In this study, non-supervisory machine learning technique has been used to predict the GHG effect relate to net calorific value based on intergovernmental panel on climate change (IPCC) standard. The study focuses on the characteristic of coal that is used in power generation sector and its chemical effluent that obtained from ultimate analysis (dried basis; Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur and Ash) as gas emissions is concerned. The dataset shows, coal from different origin and type produce GHG emissions range approximately between 86.95 and 108.23 k-tonne CO2/TJ with the net calorific value of 19.77 to 27.17 MJ/kg-coal. While, for ultimate analysis, the percentage of Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur and Ash are in the range of [65.05 – 73.3], [1.46 – 5.49], [1.2 – 19.06], [0.3 – 1.20] and [4.82 – 15.96] respectively. In this study, principal component analysis is used to screen the training dataset and feed forward structure from artificial neural networks are used which allows the trained model to determine the GHG emission factor based on the given input data. The network relative errors of year 2017 dataset were used to adjust the weight value and as a result, the networks give r-square of 0.91678, which subsequently the trained networks are simulated for GHG emissions prediction for year 2018 at accuracy of r-square 0.82191. Furthermore, the study also shows, they are significant effect from coal characteristic towards GHS emissions and study proposed an optimal solution to simultaneously maximise power generation (in net calorific value per consumption weight) and reducing GHG value (k-tonne CO2/TJ) of coal plant.