AI-Driven Waste Management Solutions in the Mining Industry: Reducing Environmental Impact - Rakotoarisoa, Solofo Roger (International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 2024)
Soil, water, and air pollution are mainly resulted from the waste of mining industry. This ecological pollution resulted from mining activities must be managed and reduced effectively. With the emergence of AI, AI has the ability to help in driving an innovative and technological waste management model. The capacity of AI in predicting, modelling and automating waste management process will be studied through the use of the theory and approach based on Machine learning’s methodologies and algorithms. A comparative analysis between traditional methods and AI integrated methods of waste management will be conducted to evaluate the effectiveness of AI in driving waste management. Thus, this will require a specific case of mining activity to be studied. The data collection and data analysis consist of conducting a literature review and documents analysis, which includes a qualitative and quantitative analysis. All in all, the study will assess the effectiveness, operationality, efficiency and economic viability of AI-driven methods, in reducing pollution from mining activities. The results will emphasize the significant enhancement brought by AI-driven waste management. The prediction made by AI in the waste production will be demonstrated as accurate and optimizing waste discharge methods. This improvement is crucial in the reduction of environmental impact of mining activities. Seen as efficient, sustainable and cos-effective operations, the AI-driven waste management will transform and improve the environmental protection in the mining industry. The research offers innovative recommendations to stakeholders such as mining companies, policymakers and researchers.