Coal Energy Production Efficiency Improvement Strategy with Integrated Genetic Algorithm and Its Economic Impact Assessment

INTRODUCTION: Global energy systems heavily rely on coal energy generation, particularly in emerging nations. OBJECTIVES: Strategies that maximize the efficiency of coal energy generation while limiting environmental harm are essential to addressing these issues. With an emphasis on increasing productivity and minimizing environmental effects, this study suggests an integrated strategy for optimizing coal energy production processes using Genetic Algorithms (GA). METHODS: Key factors, including GDP growth rate, pollution abatement investment, coal intensity, and clean technology efficiency, are all optimized using GA in the suggested approach. Finding the best combination of these factors to maximize coal production efficiency while reducing CO2 emissions and other pollutants is made possible by GA-based optimization. A Social Cost-Benefit Analysis (SCBA) and environmental impact appraisal are also included to assess various scenarios' economic and environmental consequences. The findings show that, particularly in situations with slower GDP growth, more pollution abatement expenditures and cleaner technology adoption result in notable emissions reductions and increased overall efficiency. RESULTS: The results show how crucial it is to balance environmental sustainability and economic prosperity. The study offers insightful information to industry executives and regulators, highlighting GA's potential to maximize the efficiency of coal energy generation. CONCLUSION: Scenario A provided the best economic advantages, with a greater GDP growth rate and higher environmental costs.