# AI and Robotics Advancement in Analytical Mineral Characterization and Mining Processes: A Review and Research Trends Analysis

## TL;DR

This paper reviews how AI, machine learning, and robotics are transforming mining by improving mineral analysis and operational efficiency.

## Contribution

It provides a comprehensive review and bibliometric analysis of AI and ML integration in mineral characterization and mining processes.

## Key findings

- AI and ML integration with spectroscopic techniques improves mineral identification accuracy and efficiency.
- Bibliometric analysis reveals growing research trends in AI-driven mining technologies over the past 20 years.
- The paper highlights gaps in standardization and suggests future directions for AI applications in mining.

## Abstract

The mining sector is undergoing a major transformation, as it moves shifting from traditional, labor-intensive methods to adopting digital technologies within the framework of Industry 4.0. Machine learning (ML), artificial intelligence (AI), and robotics are emerging as key innovative tools to improve safety, operational efficiency, and sustainability across the entire mining value-chain, from exploration and mineral processing to mineral characterization and environmental management. The integration of AI and ML with spectroscopic techniques has revolutionized the mining industry by enhancing efficiency, accuracy, throughput, and operational performance. This review discusses recent advances in AI, ML, and robotics applications in mining processes and mineral characterization. It explores the influence and highlights the integration of ML tools such as ANN, PCA, k-NN, and SVM with advanced analytical chemistry techniques, including XRF, XRD, SEM–EDX, LIBS, ICP-OES, ICP-MS, LA-ICP-MS, and HSI for mineral identification. Additionally, a bibliometric analysis using Scopus publications over the past 20 years provides insights into research trends and hotspots, providing recent insights into publication patterns and research. The review further offers an overview of recent technological developments, economic benefits, policy implication changes, and future directions, while emphasizing gaps related to the standardization of prospects for mining, demonstrating substantial growth in the integration of AI-driven analytical technologies within the analytical chemistry characterization of minerals, while also highlighting gaps related to the standardization of technologies.