Coal-mining companies should clearly be investing in energy savings and treatment technologies aimed at reducing both their energy consumption and their pollution levels in order to meet the requirements of government regulations relating to environmental protection; however, when considering the types of technical equipment to use and the timing of their investment, these companies also need to consider their profits and the costs of such investment. We therefore propose a "multi-objective mixed integer non-linear programming" (MMINLP) model of investment in energy savings and emission reductions designed to handle this type of decision-making problem. Given three objectives (maximum profits, minimal energy consumption and minimal pollution), we develop a hybrid mixed-coding ``particle swarm optimization and multi-objective non-dominated sorting genetic algorithm-II" (PSO–NSGA-II) to optimize the continuous and discrete decision variables as a means of helping companies to reach the optimum decision. We also integrate the subtractive clustering-multi-criteria tournament decision-gain analysis method (SC–MTD–GAM) to select the best trade-off solutions on the optimal Pareto fronts. Finally, we carry out a case study of investment decisions on energy savings and emission reductions in the Zhenzhou Coal Industry (Group) Co., Ltd., China, with the results revealing that the proposed model can support decision making for energy savings and emission reductions in coal mining areas. As compared with the NSGA-II and ``non-dominated sorting particle swarm optimization" (NSPSO) algorithms, the proposed PSO–NSGA-II is found to have better convergence, coverage and uniformity.
['Shiwei Yu', 'Shuhong Zheng', 'Shiwei Gao', 'Juan Yang']
A multi-objective decision model for investment in energy savings and emission reductions in coal mining