Wastewater mining: A new frontier for artificial intelligence in mining

Author Identifier (ORCID)

Hoda Khoshvaght: https://orcid.org/0000-0001-8766-419X

Mehdi Khiadani: https://orcid.org/0000-0003-1703-9342

Abstract

This chapter investigates the intricate aspects of mining wastewater, examining its characteristics, environmental impact, economic potential, and existing treatment methods. It highlights the transformative role of artificial intelligence (AI) in revolutionising mining wastewater management and treatment. The chapter explores various AI applications in mining wastewater, including process control, outlier detection, water quality monitoring and mineral extraction. AI algorithms can analyze vast datasets to optimiae treatment process parameters, leading to increased efficiency and reduced energy consumption. AI-powered anomaly detection systems can quickly identify and flag unusual events, enabling proactive intervention and preventing potential environmental hazards. AI-powered sensors and data analysis tools can provide real-time water quality insights, facilitating informed decision-making and ensuring compliance with environmental regulations. AI can assist in extracting valuable minerals from mining wastewater, enhancing resource recovery and promoting a circular economy. The chapter delves into popular AI algorithms, showcases successful case studies, and identifies challenges and future directions in mining wastewater management. This exploration of the intersection of mining wastewater and AI provides valuable insights for researchers, policymakers, and industry stakeholders aiming to optimize water management, minimize environmental impact, and harness the full potential of mining wastewater resources.

Document Type

Book Chapter

Date of Publication

1-1-2025

Publication Title

Artificial Intelligence in Future Mining

Publisher

Elsevier

School

School of Engineering

Comments

Khoshvaght, H., & Khiadani, M. (2025). Wastewater mining: A new frontier for artificial intelligence in mining. In Artificial Intelligence in Future Mining (pp. 249-307). https://doi.org/10.1016/B978-0-443-28911-8.00006-8

Copyright

subscription content

First Page

249

Last Page

307

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Link to publisher version (DOI)

10.1016/B978-0-443-28911-8.00006-8