基于原-对偶内点法的化工过程优化算法

Chemical process optimization approach based on primal-dual interior-point method

在基于积极集SQP的拟序贯算法研究基础上,提出了基于原-对偶内点法的拟序贯化工过程优化算法。拟序贯算法分为模拟层和优化计算层双层。模拟层中使用正交配置法同时离散状态变量和控制变量,变量的边界约束加于配置点上。同时,每次NLP迭代均求解离散DAE系统,消除等式约束和状态变量,从而减小NLP问题的规模。最新研究表明,在大规模优化问题中内点法相对于积极集SQP算法具有明显优势,因此,优化计算层中用原-对偶内点法来求解NLP问题。使用FORTRAN语言独立编写了整个算法程序,并通过热集成精馏系统最优控制的动态优化问题验证了算法的有效性。结果显示,该算法具有求解大规模动态优化问题的能力。

Based on active set SQP quasi-sequential approach,a quasi-sequential approach based on primal-dual interior-point method was presented.Quasi-sequential approach included two layers called simulation layer and optimization layer.In the simulation layer orthogonal collocation method was used to discretize both state variables and control variables,and the discretized DAE system was solved at each NLP iteration to eliminate equality constraints and state variables,so that the optimization problem is reduced to a smaller NLP problem only with inequality constraints and control variables.Recent studies showed that interior-point method took advantage of active set method in large-scale optimization problems,thus in the optimization layer a primal-dual interior-point method is employed.FORTRAN is used to code quasi-sequential approach and a distillation optimal control problem is optimized to demonstrate the efficiency.Results show that this approach has the capacity to solve large-scale dynamic optimization problems.