Abstract

This paper presents a conceptual framework for process optimization aimed at maximizing gas turbine performance and energy efficiency across the full asset lifecycle. The framework integrates first-principles thermodynamics, data-driven analytics, and control co-design to coordinate component, cycle, and plant objectives under safety, reliability, and emissions constraints. A multiscale modeling layer links compressor and turbine maps, combustion efficiency, blade cooling, and recuperation with Brayton-cycle analysis to expose leverage points for reducing specific fuel consumption and improving power-to-weight. An observability layer synthesizes SCADA streams, high-frequency vibration, and exhaust gas analytics through physics-informed digital twins that estimate unmeasured states, quantify degradation, and track uncertainty. An optimization layer formulates multi-objective problems that balance heat rate, NOx, operability, and lifecycle cost using surrogate models to accelerate search over part-load schedules, variable geometry settings, and fuel compositions, including hydrogen co-firing. A supervisory control layer deploys model predictive control augmented with reinforcement learning for adaptive set-point management, tip clearance control, and combustion dynamics avoidance while enforcing stability margins and hardware limits. A maintenance layer closes the loop via condition-based strategies, remaining-useful-life prediction, and risk-based inspection planning that prioritize actions with the highest efficiency and reliability returns. Governance is provided by a standards and assurance layer that embeds cybersecurity, data quality metrics, and model validation protocols to ensure safe, auditable optimization at fleet scale. Practical implementation proceeds through a staged roadmap: baseline modeling and data readiness; pilot twin deployment on representative units; incremental MPC roll-out; and portfolio-wide optimization with continuous improvement. Case-study templates outline expected benefits, including one to three percent heat-rate reduction, tightened emissions variability at part load, extended maintenance intervals, and improved start reliability in flexible operation. The framework emphasizes human-in-the-loop decision support and interpretable analytics to build trust with operators and regulators. By connecting physics, data, and control within a governed optimization loop, the framework provides a reproducible path to measurable gains in efficiency, availability, and environmental performance for modern and legacy gas turbine assets operating under increasingly dynamic grid and industrial duty cycles. It is adaptable to aeroderivative and heavy-duty frames, simple-cycle and combined-cycle plants, and diverse fuels, enabling resilient decarbonization pathways worldwide, affordably.