An Integrated AI-Based Water–Energy-Cabon Nexus Optimization Framework for Sustainable Phosphate Mining under Saudi Vision 2030

Sustainable resource management has become a critical priority in modern mining operations, particularly in water-scarce and energy-intensive environments such as integrated phosphate complexes. This study proposes an integrated Artificial Intelligence (AI)-based Water–Energy-Carbon Nexus (WECN) optimization framework designed to enhance sustainability, operational efficiency, and resource resilience from phosphate mining to phosphate fertilizer value chain including Sulfuric acid, phosphoric acid and DAP. The framework leverages advanced AI techniques, including machine learning, predictive analytics, and decision support systems, to model the interdependencies between water consumption and energy usage across mining processes, SAP, PAP & DAP. By integrating real-time data from Industrial Internet of Things (IIoT) sensors with intelligent optimization algorithms, the proposed system enables dynamic resource allocation, improved water recycling strategies, and energy-efficient process control. The framework also incorporates sustainability performance indicators aligned with national transformation goals such as Saudi Vision 2030 & Maaden Carbon Neutrality by 2050, focusing on environmental protection, resource conservation, and digital transformation. The results demonstrate that the integrated WECN approach significantly improves resource utilization, reduces operational costs, and minimizes environmental impact. This research contributes to the development of smart, sustainable mining ecosystems by providing a scalable and policy-aligned AI-driven framework for holistic resource optimization.