SyPSE: A Symbolic Computation Toolbox for Process Systems Engineering Part I─Architecture and Algorithm Development
SyPSE: A Symbolic Computation Toolbox for Process Systems Engineering Part I─Architecture and Algorithm Development
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DOI：
10.1021/acs.iecr.1c02151
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发表时间：
2021-11
影响因子：
4.2
通讯作者：
Shuhui Zhang;Chenglin Zheng;Xi Chen
中科院分区：
文献类型：
--
作者：
Shuhui Zhang;Chenglin Zheng;Xi Chen
在过程系统工程领域，求解非线性系统的主要技术途径是基于数值计算。然而，由于数值不稳定，数值方法的收敛和可靠性并不总是得到保证。与数值方法相比，符号计算的优点在于解的准确性和完备性。然而，由于算法开发的复杂性和工具的缺乏，符号计算在PSE领域中受到的关注很少。在这两部分工作中，为PSE开发了一个完整的符号计算工具箱，包括稳态过程模拟、优化和柔性分析。在这个由两部分组成的系列文章的第一部分中，我们将介绍用Python语言开发的符号计算工具箱SyPSE。给出了系统的体系结构和基本算法模块。为了提高算法的效率，还提出了几种策略。给出了一个案例研究，以演示算法模块的功能。
The main technical approaches to solving nonlinear systems are based on numerical computation in the field of process systems engineering (PSE). However, the convergence and reliability of numerical methods are not always guaranteed due to numerical instability. Symbolic computation benefits from the accuracy and completeness of the solution compared with numerical methods. However, symbolic computation has received little attention in the PSE area due to the complexity in the algorithm development and the lack of tools. An integrated symbolic computation toolbox is developed in this two-part work for PSE, including steady-state process simulation, optimization, and flexibility analysis. In part I of the two-part series, SyPSE, the symbolic computation toolbox developed in Python, is presented. The architecture and fundamental algorithm modules are described. Several strategies are also developed to improve the algorithm efficiency. A case study is presented to demonstrate the functionalities of the algorithm modules.