Robust simulation-optimization framework for synthesis and design of natural gas downstream Incorporating renewable hydrogen network under uncertainty

Many intrinsic uncertainties are part of natural gas downstream synthesis, design, and operation. This work extends the earlier developed deterministic model to account for new decision variables, unit oper-ations, and uncertainty in the network. The uncertainty includes the feedstock composition and flowrate, utility requirements, by-products, and final product market price. The proposed two-stage stochastic model includes new major processing units (helium recovery units), hydrogen production via renewable technologies (biomass gasification, water electrolysis), and operating modes. The stochastic formulation simultaneously considers uncertainty in 12 different model parameters. The model's first stage aims to find the optimal network's design variables, such as processing units, technologies, and operating modes common to all scenarios. The second stage is concerned with deriving operational variables, for exam-ple, the network's overall inlet feedstock flowrates, individual units' inlet flowrates, and units' capacity. Outcomes obtained for a range of uncertain material/energy feedstock and products/by-products mar-ket prices result in higher expected operating profits than the equivalent deterministic model. The anal-ysis's key takeaways show that the most prevailing network consists of two syngas technologies: (1) auto-thermal reforming to feed a low-temperature Fischer Tropsch synthesis unit and (2) steam methane reforming for hydrogen production. Additionally, renewable hydrogen is produced via electrolysis and biomass gasification. There is an improvement of 2.22% in the objective function value using the stochas-tic approach instead of the deterministic. (c) 2022 Published by Elsevier Ltd.