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A bilevel decomposition method for the simultaneous heat integration and synthesis of steam/organic Rankine cycles

机译:蒸汽/有机朗肯循环同时热集成和合成的双级分解方法

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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 non-convex 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. (C) 2019 Elsevier Ltd. All rights reserved.
机译:这项工作解决了与复杂的公用事业系统(例如热回收蒸汽循环或有机朗肯循环)集成的热交换器网络(HEN)的同步综合和设计问题。得益于两个上部结构(朗肯循环和HEN)的结合,可以考虑过程和公用事业系统之间所有关键的热集成选项,并且可以在效率和成本之间进行权衡。复杂公用事业系统的上部结构涉及流量可变的流。由于存在大量的二进制变量和非凸项,因此产生的MINLP问题非常具有挑战性。我们提出了一种新颖的双级分解算法,将外部近似线性化技术与McCormick弛豫,有效冗余约束,成本函数的分段线性化和“嵌套”整数割相结合。与商用MINLP求解器和元启发式算法相比,该算法通过多达35个流成功解决了现实世界中的问题,显示出解决方案质量和计算时间的显着改善。 (C)2019 Elsevier Ltd.保留所有权利。

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