ARTIFICIAL INTELLIGENCE APPLICATIONS FOR SUSTAINABLE RISK MITIGATION IN THERMAL POWER PLANTS: PATHWAYS TO INDUSTRY–SUSTAINABILITY CONVERGENCE

Abstract

This study investigates how artificial intelligence (AI) can support the transition of thermal power plants toward sustainability. Thermal plants continue to face persistent challenges, including dependence on fossil fuels, high emission levels, and safety risks. To address these issues, a qualitative exploratory design was employed, using thematic analysis of 15 semi structured interviews with experts in energy, AI, and sustainability. Data were analyzed using MAXQDA software through open, axial, and selective coding. Five major themes emerged: (1) environmental protection through intelligent systems, (2) data driven operational excellence, (3) economic resilience enabled by forecasting, (4) proactive safety and organizational culture, and (5) transition toward a sustainable technological ecosystem. Together, these themes form an integrated framework demonstrating that AI can reconcile industrial efficiency with environmental and social responsibilities. For instance, predictive algorithms reduced fuel consumption by up to 5% and lowered NOx emissions, providing measurable evidence of AI’s benefits. The novelty of this research lies in presenting a holistic framework that integrates technical, economic, environmental, and organizational dimensions—moving beyond the predominantly quantitative focus of earlier studies. The findings enrich the literature on sustainability transitions and intelligent risk management while offering practical guidance for managers and policymakers. The central message is that thermal power plants can shift from being a sustainability challenge to becoming part of the solution, provided that investments in data infrastructure, ethical governance, and workforce empowerment are prioritized.