A bilevel decomposition method for the simultaneous heat integration and synthesis of steam/organic Rankine cycles. (2nd September 2019)

• Record Type: Journal Article
• Authors: Elsido, Cristina; Martelli, Emanuele; Grossmann, Ignacio E.
• Is Part Of: Computers & chemical engineering. Volume 128(2019)
• Page Start: 228
• Page End: 245
• Publication Date: 2019-09-02
• Subjects: Nonconvex MINLP -- Bilevel decomposition -- McCormick relaxation -- Utility systems -- Rankine cycle superstructure; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.05.041
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Rankine cycle and HEN superstructures are integrated and optimized simultaneously. The nonconvex MINLP is solved using a novel bilevel decomposition algorithm. Three different linearization techniques are used for the master level MILP. The algorithm successfully tackled real-world problems with up to 35 streams. Considerable improvement in solution quality and CPU time over state-of-the-art. Abstract: This work tackles the simultaneous synthesis and design of heat exchanger networks (HEN) integrated with complex utility systems, such as Heat Recovery Steam Cycles or Organic Rankine Cycles. Thanks to the combination of two superstructures (Rankine cycles and HEN), all the key heat integration options between process and utility system can be considered, and the trade-off between efficiency and costs is optimized. The superstructure for complex utility systems involves streams with variable flow rate. The resulting MINLP problem is very challenging due to its large number of binary variables and nonconvex terms. We present a novel bilevel decomposition algorithm, combining the outer-approximation linearization technique with McCormick relaxation, valid redundant constraints, piecewise linearization of cost functions and "nested" integer cuts. The algorithm successfully tackled real-world problems with up to 35 streams showing considerable improvements in solution quality and computational time over commercial MINLP solvers and meta-heuristic algorithms.