摘要：
This thesis addresses the design and synthesis of multiperiod and flexible chemical processes within a mathematical programming framework. Given the model of a process, multiperiod operation requires that the design withstands changes in a set of operating parameters, retaining a feasible operation. The multiperiod design and synthesis problems are formulated as large scale nonlinear programming (NLP), and mixed-integer nonlinear programming (MINLP) problems, respectively, whose solution determines the optimal design, configuration and operation of the process flowsheet. Important aspects of these problems, including computational efficiency, modeling and analysis issues, are investigated. Firstly, an efficient methodology that includes an NLP and an MINLP algorithm for convex versions of the multiperiod problem is developed. The proposed Outer-Approximation based decomposition is applied to multiperiod multiproduct batch plant problems operating with single product campaigns. Multiperiod models are developed for the design and future capacity expansions of such plants. Numerical results show the method to be advantageous in terms of both robustness and time efficiency. For the case of nonlinear and non-convex multiperiod design problems an SQP-based, decomposition and projection scheme is developed. Using a quadratic programming subproblem as the main coordination step, the problem in the full space is solved as a stream of independent single period problems. The key property of this method is that the computational effort scales linearly to the number of periods. The ability to solve large scale multiperiod problems is demonstrated on a variety of problems, with superior performance in terms of computational demands, number of function evaluations and solution robustness. To address uncertainty in the duration of individual periods, a minimax formulation of the objective function is derived. For the analysis of multiperiod problems, a formal definition of bottlene
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