AI-Driven Waste Management Solutions in the Mining Industry: Reducing Environmental Impact
Keywords:
AI-driven solutions, waste management, mining industry, environmental impactAbstract
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.
References
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Al Duhayyim, M., Mohamed, H. G., Aljebreen, M., Nour, M. K., Mohamed, A., Abdelmageed, A. A., Yaseen, I., & Mohammed, G. P. (2022). Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management. Sustainability (Switzerland), 14(18). https://doi.org/10.3390/su141811704
Ali Soofastaei. (2019). Energy-Efficiency Improvement in Mine-Railway Operation Using AI. Journal of Energy and Power Engineering, 13(9). https://doi.org/10.17265/1934-8975/2019.09.002
Alonso, S. L. N., Forradellas, R. F. R., Morell, O. P., & Jorge-Vazquez, J. (2021). Digitalization, circular economy and environmental sustainability: The application of artificial intelligence in the efficient self-management of waste. Sustainability (Switzerland), 13(4), 1–20. https://doi.org/10.3390/su13042092
Asif, Z., Chen, Z., & Zhu, Z. H. (2019). An integrated life cycle inventory and artificial neural network model for mining air pollution management. International Journal of Environmental Science and Technology, 16(4), 1847–1856. https://doi.org/10.1007/s13762-018-1813-9
Atadoga, A., Obi, O., Osasona, F., Onwusinkwue, S., Daraojimba, A., & Dawodu, S. (2024). AI in supply chain optimization: A comparative review of USA and African Trends. International Journal of Science and Research Archive, 11, 896–903. https://doi.org/10.30574/ijsra.2024.11.1.0156
Aznar-Sánchez, J. A., García-Gómez, J. J., Velasco-Muñoz, J. F., & Carretero-Gómez, A. (2018). Mining waste and its sustainable management: Advances in worldwide research. In Minerals (Vol. 8, Issue 7). MDPI AG. https://doi.org/10.3390/min8070284
Cabri, A., Masulli, F., Rovetta, S., & Mohsin, M. (2022). Recovering Critical Raw Materials from WEEE using Artificial Intelligence. 21st International Conference on Modeling and Applied Simulation, MAS 2022. https://doi.org/10.46354/i3m.2022.mas.023
Chunyong, H. (2021). AI Integrated System for Optimizing waste management by self-Guided robot. (Patent US2021/0060786 A1). United States Patent Application Publication.
Cotet, C. E., Deac, G. C., Deac, C. N., & Popa, C. L. (2020). An innovative industry 4.0 cloud data transfer method for an automated waste collection system. Sustainability (Switzerland), 12(5), 1–15. https://doi.org/10.3390/su12051839
Dhanushya, S., Chitra, K., Swetha, S., & Tharageswari, K. (2023). A Machine Learning Approach in Garbage Collection and Monitoring with IoT Sensors. 2023 International Conference on Inventive Computation Technologies (ICICT), 221–226. https://doi.org/10.1109/ICICT57646.2023.10134498
Dli, M., Puchkov, A., Meshalkin, V., Abdeev, I., Saitov, R., & Abdeev, R. (2020). Energy and resource efficiency in apatite-nepheline ore waste processing using the digital twin approach. Energies, 13(21). https://doi.org/10.3390/en13215829
Erkinay Ozdemir, M., Ali, Z., Subeshan, B., & Asmatulu, E. (2021). Applying machine learning approach in recycling. Journal of Material Cycles and Waste Management, 23(3), 855–871. https://doi.org/10.1007/s10163-021-01182-y
Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P. S. (2023). Artificial intelligence for waste management in smart cities: a review. In Environmental Chemistry Letters (Vol. 21, Issue 4, pp. 1959–1989). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s10311-023-01604-3
Gagnon, D., Vanier, F., Haddad, J. El, de Lima Filho, E. S., Hamel, A., Padioleau, C., Boismenu, F., Beauchesne, A., Bouchard, P., Vaillancourt, T., Sabsabi, M., Blouin, A., Plugatyr, A., & Harhira, A. (2022). Real-Time Quantitative Mineral Analysis (QMA) using Artificial Intelligence (AI) Enabled LIBS Sensor for Bulk Ore Sorting on Mining Conveyors. Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES), AM2F.5. https://doi.org/10.1364/AIS.2022.AM2F.5
Habibi, S., & Zaffagnini, T. (2019). Strategic Sustainable and Smart Development Based on User Behaviour. In A. Sayigh (Ed.), Sustainable Building for a Cleaner Environment: Selected Papers from the World Renewable Energy Network’s Med Green Forum 2017 (pp. 199–207). Springer International Publishing. https://doi.org/10.1007/978-3-319-94595-8_18
Hyder, Z., Siau, K., & Nah, F. (2019). Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry. Journal of Database Management, 30. https://doi.org/10.4018/JDM.2019040104
Jamwal, D. (2019). Strategic Approach: A Step towards Sustainable Solid Waste Management in the Developing Economies. Asian Journal of Empirical Research, 9, 99–110. https://doi.org/10.18488/journal.1007/2019.9.4/1007.4.99.110
Kumar, A., & Sarangi, S. (2023). Artificial Intelligence in Sustainable Development of Municipal Solid Waste Management. International Journal for Research in Applied Science and Engineering Technology, 11(5), 6744–6751. https://doi.org/10.22214/ijraset.2023.53247
Kumari, N., Pandey, S., Kumar Pandey, D., & Banerjee, M. (2023). Role of Artificial Intelligence in Municipal Solid Waste Management. British Journal of Multidisciplinary and Advanced Studies: Engineering and Technology, 4(3), 5–13. https://doi.org/10.37745/bjmas.2022.0180
Lahcen, G., Mohamed, E., Mohammed, G., Hanaa, H., & Abdelmoula, A. (2022). Waste solid management using Machine learning approch. 2022 8th International Conference on Optimization and Applications (ICOA), 1–5. https://doi.org/10.1109/ICOA55659.2022.9934356
Lèbre, É., Corder, G., & Golev, A. (2017). The Role of the Mining Industry in a Circular Economy: A Framework for Resource Management at the Mine Site Level. Journal of Industrial Ecology, 21(3), 662–672. https://doi.org/10.1111/jiec.12596
Lin, W., Jing, L., Zhu, Z., Cai, Q., & Zhang, B. (2017). Removal of Heavy Metals from Mining Wastewater by Micellar-Enhanced Ultrafiltration (MEUF): Experimental Investigation and Monte Carlo-Based Artificial Neural Network Modeling. Water, Air, and Soil Pollution, 228(6), 206. https://doi.org/10.1007/s11270-017-3386-5
Miller, T., Cembrowska-Lech, D., Kisiel, A., Łobodzińska, A., Kozlovska, P., Jawor, M., Kołodziejczak, M., & Durlik, I. (2023). HARNESSING AI FOR ENVIRONMENTAL RESILIENCE: MITIGATING HEAVY METAL POLLUTION AND ADVANCING SUSTAINABLE PRACTICES IN DIVERSE SPHERES. In Grail of Science. https://doi.org/10.36074/grail-of-science.14.04.2023.027
Mishra, S., Jena, L., Tripathy, H. K., & Gaber, T. (2022). Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment. PLOS ONE, 17(8), e0272383-. https://doi.org/10.1371/journal.pone.0272383
Mohammed, M. A., Abdulhasan, M. J., Kumar, N. M., Abdulkareem, K. H., Mostafa, S. A., Maashi, M. S., Khalid, L. S., Abdulaali, H. S., & Chopra, S. S. (2023). Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities. Multimedia Tools and Applications, 82(25), 39617–39632. https://doi.org/10.1007/s11042-021-11537-0
Mounadel, A., Ech-Cheikh, H., Lissane Elhaq, S., Rachid, A., Sadik, M., & Abdellaoui, B. (2023). Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review. Environmental Technology Reviews, 12(1), 316–336. https://doi.org/10.1080/21622515.2023.2205027
Muhammad, R., Mahboob, M., Mustafa, K., Khan, M., Fakhr-un-Nisa, Musaddiq, S., & Mahboob, R. M. S. (2022). Artificial Intelligence in Waste Management/Wastewater Treatment. In Omics for Environmental Engineering and Microbiology Systems (1st ed., pp. 493–507). CRC Press. https://doi.org/10.1201/9781003247883-25
Olawade, D. B., Fapohunda, O., Wada, O. Z., Usman, S. O., Ige, A. O., Ajisafe, O., & Oladapo, B. I. (2024). Smart waste management: A paradigm shift enabled by artificial intelligence. Waste Management Bulletin, 2(2), 244–263. https://doi.org/10.1016/J.WMB.2024.05.001
Pinhal Luqueci Thomaz, I., Fernando Mahler, C., & Pereira Calôba, L. (2023). Artificial Intelligence (AI) applied to waste management: A contingency measure to fill out the lack of information resulting from restrictions on field sampling. Waste Management Bulletin, 1(3), 11–17. https://doi.org/10.1016/J.WMB.2023.06.002
Reid, S., Tam, J., Yang, M., & Azimi, G. (2017). Technospheric Mining of Rare Earth Elements from Bauxite Residue (Red Mud): Process Optimization, Kinetic Investigation, and Microwave Pretreatment. Scientific Reports, 7(1), 15252. https://doi.org/10.1038/s41598-017-15457-8
Romero, M., Padilla, I., Contreras, M., & López‐delgado, A. (2021). Mullite‐based ceramics from mining waste: A review. In Minerals (Vol. 11, Issue 3). MDPI AG. https://doi.org/10.3390/min11030332
Savio, R. D., & Ali, J. M. (2023). Artificial Intelligence in Project Management & Its Future. Saudi Journal of Engineering and Technology, 8(10), 244–248. https://doi.org/10.36348/sjet.2023.v08i10.002
Sinuraya, E. W., Soetrisno, Y. A. A., Setianingrum, A. P., & Sari, T. W. I. P. (2022). Realtime Monitoring System Towards Waste Generation Management. 2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), 172–177. https://doi.org/10.1109/ELTICOM57747.2022.10038115
Soofastaei, A., & Fouladgar, M. (2021). Improve Energy Efficiency in Surface Mines Using Artificial Intelligence. In M. W. Shahzad, M. Sultan, L. Dala, B. Bin Xu, & Y. Jiang (Eds.), Alternative Energies and Efficiency Evaluation (p. Ch. 12). IntechOpen. https://doi.org/10.5772/intechopen.101493
Tayebi-Khorami, M., Edraki, M., Corder, G., & Golev, A. (2019). Re-thinking mining waste through an integrative approach led by circular economy aspirations. In Minerals (Vol. 9, Issue 5). MDPI AG. https://doi.org/10.3390/min9050286
Trocan, C., Mocan, M., Cioca, L.-I., Ivascu, L., & Ardelean, R. (2022). A review of the impact of mining operations on sustainable development. MATEC Web of Conferences, 354, 00075. https://doi.org/10.1051/matecconf/202235400075
Uganya, G., Rajalakshmi, D., Teekaraman, Y., Kuppusamy, R., Radhakrishnan, A., & Bashir, A. K. (2022). A Novel Strategy for Waste Prediction Using Machine Learning Algorithm with IoT Based Intelligent Waste Management System. Wirel. Commun. Mob. Comput., 2022. https://doi.org/10.1155/2022/2063372
Varannai, B., Johansson, D., & Schunnesson, H. (2022). Crusher to Mill Transportation Time Calculation—The Aitik Case. Minerals, 12(2). https://doi.org/10.3390/min12020147
Vriens, B., Plante, B., Seigneur, N., & Jamieson, H. (2020). Mine waste rock: Insights for sustainable hydrogeochemical management. In Minerals (Vol. 10, Issue 9, pp. 1–38). MDPI AG. https://doi.org/10.3390/min10090728
Wang, C., Harbottle, D., Liu, Q., & Xu, Z. (2014). Current state of fine mineral tailings treatment: A critical review on theory and practice. Minerals Engineering, 58, 113–131. https://doi.org/10.1016/J.MINENG.2014.01.018
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Published
2024-12-01
How to Cite
RAKOTOARISOA, S. R. (2024). AI-Driven Waste Management Solutions in the Mining Industry: Reducing Environmental Impact. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 2(1), 717–738. Retrieved from https://journal.unj.ac.id/unj/index.php/isc-beam/article/view/46967
