Operational optimization of cyclic gas pipeline network with consideration of thermal hydraulics

Taicheng Zheng, Huixia Feng, Bohong Wang, Jianqin Zheng, Yongtu Liang, Yingjie Ma, Thomas Keene, Jie Li

Research output: Contribution to journalArticlepeer-review


Following the rapidly increasing global demand for natural gas, many countries are launching projects to expand gas pipeline networks (GPNs). As a result, more cyclic GPNs are under construction with more rigorous physical constraints required, bringing new challenges to GPNs optimization. This paper proposes a novel nonconvex mixed-integer nonlinear programming (MINLP) formulation for operational optimization of the cyclic GPN with simultaneous consideration of thermal hydraulics and flow direction reversibility, which has not been explored in the literature. To solve the proposed MINLP model, a three-level decomposition algorithm is proposed to generate an approximate solution, from which the flow direction is extracted and used to fix all discrete variables in the original MINLP model to construct two-stage NLP models. The NLP models are then solved to improve solution feasibility and quality. The computational results show that the proposed approach outweighs several state-of-the-art commercial MINLP solvers with better solutions and shorter computational time.
Original languageEnglish
JournalIndustiral and Engineering Chemistry Research
Publication statusPublished - 2021


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