早期粗粒脈石除去技術による低炭素・資源効率的な選鉱の推進：包括的レビュー

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🔬研究者:Provides a comprehensive comparison of ECGR technologies and identifies research gaps for AI integration in mineral processing.

🏢実務担当者:Offers guidance on selecting ECGR technologies to reduce energy and water costs, supporting sustainability reporting.

🏛政策担当者:Highlights the potential of ECGR for low-carbon mineral processing, informing policies for resource efficiency and circular economy.

The declining grade and increasing complexity of mineral resources, together with growing demands for energy efficiency and environmental sustainability, have renewed interest in early coarse gangue rejection (ECGR) as a strategy for reducing unnecessary downstream processing. This review critically examines the major ECGR technologies, including screening, differential/selective blasting, dense media separation (DMS), Reflux™ classification, coarse particle flotation (CPF), and sensor-based ore sorting (SBOS), by evaluating their separation principles, ore suitability, operating particle-size ranges, industrial maturity, engineering advantages, and practical limitations. Reported industrial and pilot-scale applications across gold, base metals, lithium, rare earth elements, and coal are synthesized to compare technology performance and identify commodity-specific applications. Particular attention is given to the emerging role of artificial intelligence (AI), including machine learning, deep learning, multi-sensor fusion, predictive modelling, digital twins, and adaptive process control, in improving ore characterization, process monitoring, and intelligent decision-making. The comparative assessment demonstrates that successful ECGR implementation depends primarily on ore-specific separation characteristics, including preferential breakage, density contrast, surface chemistry, mineralogical associations, and sensor-detectable properties, rather than on any single separation technology. The review further identifies cross-cutting technical challenges associated with ore variability, process integration, AI deployment, and industrial scalability, and discusses future research directions for developing integrated, adaptive, and intelligent beneficiation systems. Beyond improving beneficiation efficiency, ECGR supports low-carbon and circular mineral processing by reducing energy and water demand, minimizing waste generation, and enabling more resource-efficient utilization of mineral resources.

• openalex https://doi.org/10.1016/j.nxcen.2026.100110first seen 2026-09-07 04:39:58