Power-to-X processes depend on an intermittent renewable electricity supply and therefore operate continuously across a wide load range rather than at a single design point. Established plantwide control procedures were developed around a single steady state, and existing extensions address bounded turndown, discrete production transitions, or supervisory model predictive control; the design of the regulatory control layer itself for continuous, wide-range, load-flexible operation remains open. We propose a systematic, simulation-based procedure structured in three stages: defining control objectives and constraints across the load range, designing a basic control system using only single-loop proportional–integral controllers, and adding advanced control elements only where validation reveals a specific need. Load flexibility is addressed by identifying every control loop at multiple operating loads, pairing manipulated and controlled variables using a relative gain array aggregated across loads, and tuning from the load-dependent process parameters. We demonstrate the procedure on a high-fidelity, pressure-driven Power-to-Ammonia model of a 500,000t/yr plant implemented in a commercial process simulator, yielding a regulatory layer of 15 control loops, extended by a feedforward-cascade element for the steam temperature and a ratio-cascade element for the reactor-inlet ratio. The resulting control system operates stably across the full 10%–100% load range at ramping rates of 3%/min, using a single fixed tuning per loop. A decentralized regulatory layer of simple, well-understood controllers is therefore sufficient for highly load-flexible operation of the presented process, and acts as the basis on which scheduling and real-time optimization must subsequently build.
Multi-objective optimization in chemical engineering requires balancing conflicting objectives within highly nonlinear, simulation-driven design spaces. Traditional evolutionary algorithms can struggle with maintaining convergence, diversity, and computational efficiency when applied to such problems. This work presents NEMOPSO, a graph-theoretic multi-objective particle swarm optimization framework that enhances search performance through structured leader selection, topology-aware information flow, and adaptive exploration. By modeling the swarm as a dynamic graph and selecting leaders using centrality metrics, NEMOPSO prioritizes structurally influential solutions rather than relying solely on geometric diversity. Benchmarking on the ZDT suite demonstrates that closeness-based leader selection with caching achieves superior hypervolume and convergence on smooth and moderately complex landscapes, while scale-free topologies excel in deceptive problems. Furthermore, a three-objective scalability assessment using DTLZ2 reveals limitations associated with the baseline single-leader strategy, motivating the implementation of an adaptive neighbor-leader mechanism. The modified approach demonstrates the feasibility of extending NEMOPSO to three-objective optimization. Four chemical-engineering case studies: a low-density polyethylene reactor, a distillation column, a methane tri-reforming reactor, and a continuous-flow SNAr reaction, further validate the approach. NEMOPSO consistently reproduces or improves upon reference Pareto fronts, achieving lower spacing, competitive hypervolume, and strong IGD+ performance, while maintaining robust execution times even when coupled to high-fidelity simulators. Results show that the selection method of leaders’ influence, not the size or diversity of the stored solutions, is the primary driver of optimization quality. Overall, NEMOPSO provides a flexible, scalable, and computationally efficient framework for multi-objective optimization in chemical-engineering applications.
