Applied Chemical Engineering

AI Applications in Tailings and Waste Management: Improving Safety, Recycling, and Water Utilization

Manjusha Tatiya

Department of Artificial Intelligence and Data Science, Indira College of Engineering and Management, Indira Chanakya Campus (ICC), Parandwadi, Pune - 410506, Maharashtra ,India

Milind Manikrao Darade

Department of Civil Engineering, Dr. D. Y. Patil College of Engineering, Akurdi, Pune – 411044, Maharashtra, India

Babaso A. Shinde

Department of Artificial Intelligence and Data Science, Marathwada Mitramandal’s Institute of Technology, Lohgaon, Pune - 411047, Affiliated to Savitribai Phule Pune University, Maharashtra, India

Mahesh Prakash Kumbhare

Department of Mechanical Engineering, ABMSP’s Anantrao Pawar College of Engineering and Research, Parvati, Pune - 411009, Maharashtra, India

Rupali Dineshwar Taware

MCA Department (Commerce and Management), Vishwakarma University, Laxminagar, Kondhwa (Bk.), Pune – 411048, Maharashtra, India

Sukhadip Mhankali Chougule

Department of Mechanical Engineering, PCET’s Pimpri Chinchwad College of Engineering and Research, Ravet, Pune - 412101, Maharashtra, India

Swati Mukesh Dixit

Department of Electronics and Telecommunication Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, 411018, Maharashtra, India ; Dnyaan Prasad Global University (DPGU), School of Technology and Research - Dr. D. Y. Patil Unitech Society, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India

Anant Sidhappa Kurhade

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India ; Dnyaan Prasad Global University (DPGU), School of Technology and Research - Dr. D. Y. Patil Unitech Society, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India

DOI: https://doi.org/10.59429/ace.v8i4.5789

Keywords: Artificial intelligence; tailings management; waste valorization; machine learning; predictive maintenance; water reuse optimization; computer vision; sustainable mining

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Abstract

Artificial Intelligence (AI) is transforming tailings and waste management in the mining sector by improving safety, enhancing recycling efficiency, and optimizing water utilization. Traditional monitoring and waste handling approaches often lack scalability, real-time responsiveness, and predictive accuracy, limiting their effectiveness in preventing environmental and operational failures. This review systematically examines AI-driven applications across tailings dam safety, waste recycling, and intelligent water management, drawing insights from over 80 recent studies. Quantitative evidence indicates that AI-based monitoring systems can detect potential dam failures up to 30–40% earlier than conventional methods, while reinforcement learning and neural-network models improve mineral recovery by 10–25% with reduced chemical consumption. In water reuse operations, machine learning optimization achieves up to 35% savings in freshwater demand through closed-loop control. The paper highlights emerging integrations of AI with Explainable AI (XAI), Federated Learning (FL), and Circular Economy (CE) models that collectively support sustainable and transparent mining practices. Persistent barriers such as poor data quality, inadequate infrastructure, and lack of regulatory clarity are also discussed, along with future research directions. The findings demonstrate AI’s potential to transition mining operations toward safer, more efficient, and environmentally responsible systems.

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