A systematic approach for flexible cost-efficient hydrogen network design for hydrogen management in refineries

Abstract

The study of a better use of hydrogen in refineries is essential due to its increasing use in hydrotreating fractions obtained from petroleum. Since several factors affect this process, it is essential not only that the hydrogen network must be able to operate feasibly in various conditions but also accomplish it with minimum costs. In this work, a systematic approach is proposed considering a multi-scenario optimization problem formulation for the network design coupled with the flexibility evaluation of the proposed design to verify the flexibility and identify critical scenarios that are used to update the previous set of scenarios for the design problem. As a result, it is obtained a cost-efficient flexible design. The proposed approach can be used for new designs or for the retrofit case. For the design a superstructure-based MILP and MINLP multi-scenario models were developed and completely described, to optimize hydrogen networks through uncertainties in hydrogen consumption in consumer units. The flexibility index problem formulated for hydrogen networks is presented. All the optimization models were implemented in the modeling system GAMS. The initialization strategy consist of using the network obtained from linear optimization as a starting point for nonlinear optimization. In addition, it was also used the proposed technique of virtual compressors, able to reduce the cost of capital even further. Two case studies were used to validate the proposed approach. A case study from the literature was used and also a second case using real data of a Brazilian refinery. Compared to the initially proposed network, the model through optimization achieved flexible design with a reduction of more than 13.8% (example 1) and 16% (example 2) in the operating cost. For both cases, the procedure could find a cost-efficient flexible design that can be coupled with the refinery production planning for the whole process economy.