Issues in Local Chemical Process Optimization

理论上合理、合理可靠和有效 寻找当地解决小化学品的计算机工具 工艺优化问题是当前可用的。 的 选择合适的方法是基于以下问题： 手，它是如何制定的，施加的限制，等等。 逐次二次规划（SQP）是一种很好的迭代算法， 一种求解小非线性约束优化问题的方法 问题 对于大型问题，使用SQP需要 分解或巧妙地利用问题结构。 在本项目中，PI计划开发可推广的 使用SQP方法解决大型和小型 非线性化工过程优化问题 约束（此类问题的一个示例是分离单元 例如蒸馏塔）。 研究将分为 分为以下几个方面： (1)二次规划新方法的发展 (QP)基于活动集策略和对称， 不定因子分解 (2)非对称信赖域方法的发展 提高牛顿方法的可靠性。 (3)对现有方法的比较评价 拉格朗日函数的Hessian矩阵的近似 函数，任何新的拟牛顿方法的发展， 在混合方法中的使用，以及对不确定的影响的研究 投影Hessian矩阵 (4)自动生成的可行性研究 对未知变量的可靠初始估计， 使用一般问题信息的乘数，物理 洞察力、捷径技术、分析和/或模拟 工具. (5)的数值实现和彻底的测试， 上述概念对各种各样的相关小和大- 规模化工过程优化问题。

Theoretically sound and reasonably reliable and efficient computer tools for finding local solutions to small chemical process optimization problems are currently available. The selection of a suitable method is based on the problem at hand, how it is formulated, the imposed constraints, etc. Successive quadratic programming (SQP) is a good iterative method for solving small nonlinearly constrained optimization problems. For large problems, use of SQP requires either decomposition or clever exploitation of the problem structure. In this project, the PI plans to develop generalizable techniques for using SQP methods to solve large and small chemical process optimization problems with nonlinear constraints (an example of such problems are separation units such as distillation columns). The research will be divided into the following areas: (1) The development of a new method for quadratic programming (QP) based on an active set strategy and a symmetric, indefinite factorization. (2) The development of asymmetric trust region methods for improving the reliability of Newton-like methods. (3) The comparative evaluation of current methods for approximating the Hessian matrix of the Lagrangian function, the development of any new quasi-Newton methods for use in hybrid methods, and a study of the impact of indefinite projected Hessian matrices. (4) The study of the feasibility of automatically generating reliable initial estimates of the unknown variables and multipliers using generic problem information, physical insight, short cut techniques, analysis and/or simulation tools. (5) The numerical implementation and thorough testing of above concepts on a wide variety of relevant small and large- scale chemical process optimization problems.