Optimal design and operation of modular production systems for renewable methanol considering startup/shutdown dynamics

The startup/shutdown dynamics of production lines in modular production systems for renewable methanol is often neglected in system-level optimization, which may lead to overestimated operational flexibility and underestimated capacity requirements and operating costs. To address this issue, we developed a modular production system for renewable methanol with a distributed synthesis-centralized refining configuration and formulated an integrated design–operation optimization model. In this modular production system for renewable methanol, water electrolysis, battery storage, hydrogen storage, crude methanol buffering, modular methanol synthesis, and centralized distillation are involved. Three formulations for startup/shutdown dynamics are introduced, i.e. an ideal startup/shutdown model, a minimum startup/shutdown duration-constraint model, and an individual production-line state-tracking model. A typical 5 t h⁻¹ methanol synthesis reactor is used to quantify the dynamic response during load transitions. The dynamic simulation results show that approximately 10 h is required for the reactor bed temperature to approach a new steady state when the inlet flow rate decreases from the rated load to a low-load condition, whereas about 2.5 h is needed for recovery when the flow rate is restored. The results indicate that startup/shutdown dynamics constraints reduce the immediate response capability of modular production systems to renewable energy fluctuations and significantly increase the levelized cost of methanol. The costs under the no-constraint, minimum time-constraint, and individual state-tracking scenarios are 2.31, 2.50, and 3.38 ＄ per kg, respectively. Under the minimum time constraint, hydrogen storage is expanded to buffer supply–demand mismatches, whereas individual state tracking requires more production lines to compensate for non-productive transition periods. These results demonstrate that startup/shutdown dynamics are key factors reshaping system economics, storage configuration, and scheduling strategies.