Enhancing the Efficiency of Scada Systems in Industrial Automation Through Artificial Intelligence in the Mining Sector

Abstract

Supervisory Control and Data Acquisition (SCADA) systems are essential for industrial automation, facilitating real-time monitoring and control of processes in industries including energy, manufacturing, and utilities. With a focus on predictive maintenance, anomaly detection, and improved decision-making, this paper explores how Artificial Intelligence (AI) might be integrated into SCADA systems to increase operational efficiency in Zambia's industrial sectors. The paper places these developments in the context of Zambia's mining and energy sectors, which present both potential and problems. SCADA systems are widely utilized in these sectors, but they have limits with regard to automation and real-time analytics. The study outlines research topics intended to examine the key factors such as legacy infrastructure, real-time data limitations, and skilled workforce shortages that influence this advancement, while a comprehensive literature review outlines the current challenges and opportunities in AI-SCADA integration within Zambia's mining sector. The results indicate that AI can markedly enhance the performance of SCADA systems yet, organisational, technical, and ethical obstacles persist. Recommendations for industry practitioners and researchers are offered to promote the adoption of AI technology, highlighting standardisation, data quality, and explainability. This research enhances the existing knowledge on AI applications in industrial contexts, offering both theoretical and practical perspectives on the future of AI-augmented SCADA systems.