The simultaneous optimization problem of the heat exchanger network and organic Rankine cycle (HEN-ORC) poses significant challenges due to its highly nonconvex and nonlinear equations. We develop an adaptive partition linearization global optimization algorithm which is suitable for a wide range of mixed integer nonlinear programming (MINLP) problems and specially customized for HEN-ORC. The algorithm identifies convex equations of the logarithmic mean temperature function and the power function within the HEN-ORC model, which are relaxed by the first Taylor expansion and piecewise linearization. A multilevel McCormick relaxation is applied for the bilinear/multilinear functions derived from the HEN-ORC energy balance equations. The algorithm achieves global optimality by solving mixed integer linear programming and NLP submodels iteratively, enhancing the lower bound adaptively. Tested on seven heat exchanger networks and waste heat power generation cases, it outperforms two mainstream MINLP global optimization solvers (Baron and Couenne). The current best solutions are obtained for both a HEN and a HEN-ORC case, respectively.

中文翻译：

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自适应分区线性化全局优化算法及其在同步换热器网络和有机朗肯循环优化中的应用


由于其高度非凸和非线性方程，换热器网络和有机朗肯循环 （HEN-ORC） 的同步优化问题带来了重大挑战。我们开发了一种自适应分区线性化全局优化算法，该算法适用于各种混合整数非线性规划 （MINLP） 问题，并专门为 HEN-ORC 定制。该算法确定了 HEN-ORC 模型中对数平均温度函数和幂函数的凸方程，这些方程通过第一次泰勒展开和分段线性化而松弛。多级 McCormick 弛豫应用于从 HEN-ORC 能量平衡方程推导出的双线性/多线性函数。该算法通过迭代求解混合整数线性规划和 NLP 子模型来实现全局最优性，并自适应地增强下限。在七个换热器网络和余热发电案例中进行了测试，它的性能优于两个主流的 MINLP 全局优化求解器（Baron 和 Couenne）。当前的最佳解分别针对 HEN 和 HEN-ORC 情况获得。