SMART CONTROL AND INSTRUMENTATION STRATEGIES FOR OPTIMIZING GASIFICATION PROCESSES IN SAUDI ARABIAS INTEGRATED ENERGY FACILITIES

Gasification is a strategically important method of conversion for integrated energy facilities since it connects heterogeneous carbon-based feedstocks with dispatchable synthesis gas, power, steam, hydrogen and downstream chemical production. Its industrial value, though, relies on keeping within a narrow range of operation even though the reaction kinetics are nonlinear, the measurement conditions are severe, the characteristics of the feed vary and there are strong interactions between oxygen, steam, solids circulation, temperature, pressure and syngas composition. This review brings together the literature published between 2020 and 2025 on smart instrumentation, status evaluation, advanced control, machine learning and digital twins for gasification and then converts the findings as a framework for implementation in Saudi Arabia. A structured, PRISMA-informed integrative review was carried out, with the evidence assessed for methodological clarity, the quality of validation, its relevance to real-time control and its transferability to integrated energy facilities. The synthesis reveals a clear trend moving from conventional regulatory loops towards sensor fusion, Kalman filtering, model predictive control, neural-network-assisted prediction, explainable machine learning and bidirectional digital-twin architectures. Recent pilot-scale studies have shown that constrained model predictive control can regulate the coupled gasification variables, state prediction can recover the poorly measured internal states and online spectroscopy can improve the monitoring of syngas and tar.