Artificial intelligence in quartz beneficiation and high-purity quartz production: A review

The rising demand for high-purity quartz (HPQ) from the photovoltaic, semiconductor, optical, and advanced materials sectors has heightened the need for efficient and sustainable beneficiation technologies. Traditional quartz processing methods such as gravity separation, magnetic separation, flotation, and chemical purification can be complex and challenging to optimize. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide powerful tools for modelling, prediction, and process control. Techniques like artificial neural networks (ANN), support vector machines (SVM), and deep learning (DL) algorithms have shown strong potential in ore characterization and flotation modelling. However, the application of AI in quartz beneficiation and HPQ production remains limited. This review highlights current AI and ML applications in quartz processing, with a focus on automated mineral characterization, flotation optimization, and digital process control. It explores opportunities for AI integration in quartz-feldspar separation and addresses challenges such as data availability and model interpretability. Finally, it considers future prospects of digital twins and AI-assisted sustainable processing, emphasizing AI's potential to enhance beneficiation strategies and satisfy the growing demand for HPQ in advanced technology sectors.