Targeting compression work in hydrogen allocation network with parametric uncertainties

Hydrogen management in an uncertain environment is an important aspect considering environmental and economic perspectives. In this paper, a novel framework and analysis for minimizing compression work in hydrogen allocation network (HAN) with parametric uncertainties are presented which includes stochastic and robust optimization approaches. The applicability of the proposed approaches is applied to a case study in a HAN of the refinery. In stochastic approaches, uncertainty can be converted into deterministic equivalents. The resultant deterministic problem is solved through pinch analysis. In normal distribution rise of 29% in resource requirement and almost 4% in energy, the requirement is calculated. Further, applying Chebyshev's one-sided inequality to the case study 120% more resource requirement and 12% increase in energy requirement is calculated. In robust optimization, three different robust optimization approaches are adapted to deal with bounded and known uncertainty. From the simulated result, it can be concluded that Bertsimas and Sim's approach is the most appropriate approach for such a problem because it provides a range of solutions i. e. least conservative to worst-case, and also the main benefit of this approach over the other two approaches that it preserves the linearity and provides a mechanism to control the degree of conservatism.