基于Gibbs反应器代理模型与隐式公式的工艺流程优化
Process flowsheet optimization with surrogate and implicit formulations of a Gibbs reactor
AI总结：
本文提出用代理模型和隐式函数替代Gibbs反应器方程，提高化工流程优化收敛可靠性，并比较四种公式在求解时间、精度与收敛性上的权衡。
摘要：
本文提出了化学工艺流程优化问题的替代公式，利用代理模型和隐式函数分别替换和移除描述难以收敛的Gibbs反应器单元操作的代数方程。在全空间、ALAMO代理、神经网络代理和隐式函数四种公式之间，比较了优化问题的收敛可靠性、求解时间和解质量。代理和隐式公式均带来更好的收敛可靠性，且对过程参数敏感性低。代理公式求解更快，但代价是存在轻微解误差；隐式公式则提供精确解，且求解时间相近。在对自热重整器工艺流程优化问题的参数扫描中，全空间公式解决了64个实例中的33个，隐式函数公式解决了64个中的52个，ALAMO多项式公式解决了64个中的64个，神经网络公式解决了64个中的48个。这项工作展示了当前提高化学工艺流程优化问题收敛可靠性的方法中，精度与求解时间之间的权衡。
英文摘要：
Alternative formulations for the optimization of chemical process flowsheets are presented that leverage surrogate models and implicit functions to replace and remove, respectively, the algebraic equations that describe a difficult-to-converge Gibbs reactor unit operation. Convergence reliability, solve time, and solution quality of an optimization problem are compared among full-space, ALAMO surrogate, neural network surrogate, and implicit function formulations. Both surrogate and implicit formulations lead to better convergence reliability, with low sensitivity to process parameters. The surrogate formulations are faster at the cost of minor solution error, while the implicit formulation provides exact solutions with similar solve time. In a parameter sweep on an autothermal reformer flowsheet optimization problem, the full space formulation solves 33 out of 64 instances, while the implicit function formulation solves 52 out of 64 instances, the ALAMO polynomial formulation solves 64 out of 64 instances, and the neural network formulation solves 48 out of 64 instances. This work demonstrates the trade-off between accuracy and solve time that exists in current methods for improving convergence reliability of chemical process flowsheet optimization problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
