A comparison between two methods of stochastic optimization for a dynamic hydrogen consuming plant

• Two-stage and chance constrained optimization applied in a desulphurisation process.
• Both methods are formulated using a sequential approach for dynamic optimization.
• Implementation of two-stage results in open loop is made interpolating on scenarios.
• Chance constraints are solved with inverse mapping, formulated as nested estimation.
• Results of both methods are tested using Monte Carlo simulations.

The following work shows the application of two methods of stochastic economic optimization in a hydrogen consuming plant: two-stage programming and chance constrained optimization. The system presents two main sources of uncertainty described with a binormal probability distribution function (PDF). Both methods are formulated in the continuous domain. For calculating the probabilistic constraints the inverse mapping method was written as a nested parameter estimation problem. On the other hand, to solve the two stage optimization, a discretization of the PDF in scenarios was applied with a scenario aggregation formulation to take into account the nonanticipativity constraints. Finally, a framework generalizing this solution based on interpolation was proposed. Both optimization methods, two-stage programming and chance constrained optimization, were tested using Monte Carlo simulation in terms of feasibility and optimality for the application considered. The main problem appears to be the large computation times associated.