ABSTRACT Natural gas scheduling on offshore platforms is essential for ensuring safe and economically efficient production. Traditional control methods, such as PID, often exhibit limited robustness and slow response under nonlinear dynamics, disturbances and fluctuating demand. This paper proposes a hierarchical scheduling optimisation framework that integrates an improved genetic algorithm (GA) with model predictive control (MPC), aiming to achieve long‐term global optimisation together with real‐time dynamic control. The improved GA incorporates adaptive crossover and mutation rates, simulated binary crossover (SBX), polynomial mutation and an elitism strategy to enhance search efficiency and avoid premature convergence. A simulation‐driven digital‐twin prototype is constructed using representative operating conditions—normal, high‐demand, low‐demand and fault—to evaluate flow and pressure regulation performance. The optimisation objective minimises total operating cost subject to physical feasibility, safety limits and equipment performance constraints, with decision variables including valve openings and compressor speeds. The GA generates hourly reference trajectories for global scheduling, whereas MPC ensures minute‐level tracking under disturbances and operational variability. Comparative results demonstrate that the hierarchical GA + MPC approach outperforms standalone MPC, reducing operating cost by 9.4%, accelerating convergence by 34.2% and lowering steady‐state tracking error by 18.6%. Relative to PID control, convergence speed improves by 43.5% and steady‐state error decreases by 35.1%. In addition, the improved GA achieves a 65.6% reduction in convergence generations compared with the traditional GA, confirming its superior efficiency and robustness. These results, validated within the simulation‐driven digital‐twin prototype, highlight the hierarchical architecture's ability to combine global optimisation with real‐time dynamic control, demonstrating its practicality, robustness and potential for broader application in intelligent offshore energy systems.
Zhang et al. (Thu,) studied this question.