复杂流程工业系统的优化综合与柔性分析

MINLP模型在优化综合与柔性分析中起着重要作用。具有两种表示方法：代数法和逻辑法,后者在模型表达与求解方面有很多优点。MINLP模型中还可以集成启发性知识,也就是工程经验,以加快求解速度,并使结果更加合理。对于凸的MINLP问题。目前比较成熟的算法有分支界定法、外近似法和广义Bender函数法。对于非凸NINLP问题,其求解算法目前尚在研究中,已提出的有罚函数法、αBB算法和符号重建算法。此外,一些基于随机搜索的方法也得到了应用,并且在实践中取得了较好的结果。

Optimal Synthesis and Flexibility Analysis of Complex Process Industrial System

The mixed-integer nonlinear programming (MINLP) models are widely used in optim al synthesis and flexibility analysis. There are two types of MINLP model: the alge braic MINLP model and the logic MINLP model, and the latter has many advantages in expressin g and solving.Logic and heuristic knowledge can also be integrated into MINLP model for proces s synthesis.Hard logic knowledge cannot be violated, and heuristic rule can be violated when needed. Every heuristic rule has a penalty factor, when it is broken, a penalty will be added into the object function. There are three solution algorithms for convex MINLP models: the branc h-and-bound algorithm, the outer approximation algorithm and the generalized Benders decompo sition algorithm. All these three methods generate a descending upper-bond sequence an d a increasing lower-bond sequence, and the convergence of the two sequences is the optimal so lution. The branch-and-bound algorithm make a computational-tree, in which every node is a relaxed subproblem. The outer approximation algorithm solves a series of NLP subproblems and MILP main problems, and the main problems are the linearization of the original probl em. The generalized Benders decomposition algorithm divides the variables into complex v ariables and simple variables, and projects the original problem into the complex variable sp ace to get the MILP main problem. The solution algorithms for non-convex MINLP models are unde rdeveloping, including the AP/OA/ER algorithm, the αBB algorithm and the symboli creformulation algorithm. Stochastic optimization based algorithms are also prese nted and are used successfully.