A novel mathematical model for integrating the hydrogen network of refinery with compressor allocation considered

• Published in: International journal of hydrogen energy Vol. 47; no. 41; pp. 18067 - 18079
• Main Authors: Huang, Lingjun, Li, Dan, Li, Ning, Liu, Guilian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 12.05.2022
• Subjects: Allocation; Compression path; Compressor; Hydrogen network; Optimization; Compression path; Compressor; Hydrogen network; Allocation; Optimization
• ISSN: 0360-3199; 1879-3487
• DOI: 10.1016/j.ijhydene.2022.03.267

The allocation and cost of compressors have significant influence on hydrogen network. A novel mixed integer nonlinear programming method is proposed for optimizing hydrogen network with the allocation of compressors considered. This model considers the streams compressed stage by stage with multiple compressors and all possible compression paths. The compression power cost of multiple compressors is deduced to optimize the number of compressors in each compression process. The trade-off between the power loss and capital cost of compression is analyzed for different compression paths. The superstructure and mathematical model are built to optimize the hydrogen network in terms of minimizing the total annual cost. The proposed model is flexible and efficient. Three literature cases are studied by the proposed method, and the optimal flowsheets are identified. Compared with previous methods, the computing time is significantly reduced and the total cost of compressors is reduced by 3.38%–8.46%. [Display omitted] •A novel method is proposed for optimizing hydrogen networks considering compressor allocation.•The streams compressed multiple compressors and all possible compression paths are considered.•The power cost of multiple compressors is deduced to optimize the number of compressors.•The trade-off between power loss and capital cost is analyzed for different compression paths.•The computing time is reduced, and the total cost of compressors is reduced by 3.38%–8.46%.