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Moving horizon approach of integrating scheduling and control for sequential batch processes

AICHE JOURNAL 2014年第5期60卷 1654-1671页

作者： Chu, Yunfei You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

Online integration of scheduling and control is crucial to cope with process uncertainties. We propose a new online integrated method for sequential batch processes, where the integrated problem is solved to determine controller references rather than process inputs. Under a two-level feedback loop structure, the integrated problem is solved in a frequency lower than that of the control loops. To achieve the goal of computational efficiency and rescheduling stability, a moving horizon approach is developed. A reduced integrated problem in a resolving horizon is formulated, which can be solved efficiently online. Solving the reduced problem only changes a small part of the initial solution, guaranteeing rescheduling stability. The integrated method is demonstrated in a simulated case study. Under uncertainties of the control system disruption and the processing unit breakdown, the integrated method prevents a large loss in the production profit compared with the simple shifted rescheduling solution. (c) 2014 American Institute of Chemical Engineers AIChE J, 60: 1654-1671, 2014

关键词： scheduling advanced manufacturing mixed-integer dynamic optimization model predictive control batch processes

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Integrated Scheduling and dynamic optimization of Sequential Batch Processes with Online Implementation

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AICHE JOURNAL 2013年第7期59卷 2379-2406页

作者： Chu, Yunfei You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed-integer dynamic optimization problem or a large-scale mixed-integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed-integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 2379-2406, 2013

关键词： integrated scheduling and dynamic optimization decomposition mixed-integer dynamic optimization mixed-integer nonlinear programming multiobjective dynamic optimization

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State-of-the-art and progress in the optimization-based simultaneous design and control for chemical processes

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AICHE JOURNAL 2012年第6期58卷 1640-1659页

作者： Yuan, Zhihong Chen, Bingzhen Sin, Gurkan Gani, Rafiqul Tsinghua Univ Dept Chem Engn Inst Proc Syst Engn Beijing 100084 Peoples R China Tech Univ Denmark Dept Chem & Biochem Engn CAPEC DK-2800 Lyngby Denmark

Significant progress in the area of simultaneous design and control for chemical processes has been achieved and various methodologies have been put forward to address this issue over the last several decades. These methods can be classified in two categories (1) controllability indicator-based frameworks that are capable of screening alternative designs, and (2) optimization-based frameworks that integrate the process design and control system design. The major objective is to give an up-to-date review of the state-of-the-art and progress in the challenging area of optimization-based simultaneous design and control. First, motivations and significances of simultaneous design and control are illustrated. Second, a general classification of existing methodologies of optimization-based simultaneous design and control is outlined. Subsequently, the mathematical formulations and relevant theoretical solution algorithms, their merits, strengths and shortcomings are highlighted. Last, based on the recent advances in this field, challenges and future research directions are discussed briefly. An attempt is made with the help of this review article to stimulate further research and disseminate the simultaneous design methods to challenging problem areas. In particular, the application of optimization-based simultaneous design and control methods to large-scale systems with highly inherent nonlinear dynamics often the case in industrial chemical processes remains a challenging task and yet to be solved. (C) 2012 American Institute of Chemical Engineers AIChE J, 58: 16401659, 2012

关键词： simultaneous design and control controllability index mixed-integer dynamic optimization robust theory black-box optimization

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Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

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COMPUTERS & CHEMICAL ENGINEERING 2012年 37卷 125-135页

作者： Brunet, Robert Guillen-Gosalbez, Gonzalo Ricardo Perez-Correa, J. Antonio Caballero, Jose Jimenez, Laureano Univ Rovira & Virgili Dept Engn Quim Escola Tecn Super Engn Quim Tarragona 43007 Spain Pontificia Univ Catolica Chile Dept Ingn Quim & Bioproc Santiago Chile Univ Alicante Dept Ingn Quim E-03080 Alicante Spain

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer (R) with an external NLP solver implemented in Matlab (R). The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Hybrid simulation-optimization mixed-integer dynamic optimization Biotechnological processes L-Lysine

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Decentralized Control System Design under Uncertainty Using mixed-integer optimization

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CHEMICAL ENGINEERING & TECHNOLOGY 2012年第2期35卷 261-271页

作者： Paramasivan, Ganesh Kienle, Achim Max Planck Inst Dynam Komplexer Tech Syst D-39106 Magdeburg Germany Univ Magdeburg Lehrstuhl Automatisierungstech Modellbildung D-39106 Magdeburg Germany

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. In this contribution, the optimal control structure and the optimal controller parameters are determined simultaneously using mixed-integer dynamic optimization (MIDO) under uncertainty, to account for nonlinear process dynamics and various disturbance scenarios. Application of the sigma point method is proposed in order to approximate the expectation and the variance of a chosen performance index with a minimum number of points to solve the MIDO problem under uncertainty. The proposed methodology is demonstrated with a benchmark problem of an inferential control for a reactive distillation column. The results are compared with established heuristic design methods and with previous deterministic approaches.

关键词： Control structure selection Decentralized control system mixed-integer dynamic optimization Process control

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Inferential Control of Reactive Destillation Columns - An Algorithmic Approach

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CHEMICAL ENGINEERING & TECHNOLOGY 2011年第8期34卷 1235-1244页

作者： Paramasivan, Ganesh Kienle, Achim Max Planck Inst Dynam Komplexer Tech Syst D-39106 Magdeburg Germany Otto von Guericke Univ Lehrstuhl Automatisierungstech Modellbildung Magdeburg Germany

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. Here, mixed integer optimization is used to determine the optimal control structure and the optimal controller parameters simultaneously. The process dynamics is included explicitly into the constraints using a rigorous nonlinear dynamic process model. Depending on the objective function, which is used for the evaluation of competing control systems, two different formulations are proposed which lead to mixed-integer dynamic optimization (MIDO) problems. A MIDO solution strategy based on the sequential approach is adopted in the present paper. Here, the MIDO problem is decomposed into a series of nonlinear programming (NLP) subproblems (dynamic optimization) where the binary variables are fixed, and mixed-integer linear programming (MILP) master problems which determine a new binary configuration for the next NLP subproblem. The proposed methodology is applied to inferential control of reactive distillation columns as a challenging benchmark problem for chemical process control.

关键词： Control structure selection Decentralized control system mixed-integer dynamic optimization Reactive distillation control

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Global mixed-integer dynamic optimization

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AICHE JOURNAL 2005年第8期51卷 2235-2253页

作者： Chachuat, B Singer, AB Barton, PI MIT Dept Chem Engn Proc Syst Engn Lab Cambridge MA 02139 USA

Recent advances in process synthesis, design, operations, and control have created an increasing demand for efficient numerical algorithms for optimizing a dynamic system coupled with discrete decisions;these problems are termed mixed-integer dynamic optimization (MIDO). In this communication, we develop a decomposition approach for a quite general class of MIDO problems that is capable of guaranteeing finding a global solution despite the nonconvexities inherent in the dynamic optimization subproblems. Two distinct algorithms are considered. On finite termination, the first algorithm guarantees finding a global solution of the MIDO within nonzero tolerance;the second algorithm finds rigorous bounds bracketing the global solution value, with a substantial reduction in computational expense relative to the first algorithm. A case study is presented in connection with the optimal design and operation of a batch process consisting of a series reaction followed by a separation with no intermediate storage. The developed algorithms demonstrate efficiency and applicability in solving this problem. Several heuristics are tested to enhance convergence of the algorithms;in particular, the use of bounds tightening techniques and the addition of cuts resulting from a screening model of the batch process are considered. (c) 2005 American Institute of Chemical Engineers

关键词： mixed-integer dynamic optimization nonconvex problems global optimization decomposition algorithms outer-approximation batch process design

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Integrated control and process design during optimal polymer grade transition operations

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COMPUTERS & CHEMICAL ENGINEERING 2008年第11期32卷 2823-2837页

作者： Flores-Tlacuahuac, Antonio Biegler, Lorenz T. Univ Iberoamer Dept Ingn & Ciencias Quim Mexico City 01210 DF Mexico Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

In this work we address the simultaneous process control and design problem of polymerization reactors during dynamic grade transition operation. The problem is cast as a mixed-integer dynamic optimization (MIDO) formulation and, by using the full discretization approach for solving dynamic optimization problems [Kameswaran, S., & Biegler, L. T (2006). Simultaneous dynamic optimization strategies: Recent advances and challenges. Computers & Chemical Engineering, 30 (10-12). 1560-1575], is transformed into a mixed-integer Nonlinear Program (MINLP). The resulting MINLP is solved using a full space nonconvex optimization formulation [Flores-Tlacuahuac. A., & Biegler, L. T. (2007). Simultaneous mixed-integer dynamic optimization for integrated design and control. Computers & Chemical Engineering, 31, 588-600]. The control and design formulation has been applied to two polymerization reactors featuring highly nonlinear behavior. In both cases, the proposed MIDO formulation was capable of finding optimal solutions. This amounts to finding optimal steady states. reactor designs. as well as open-loop and closed-loop dynamic optimal trajectories, control structures and controller parameters by specifying either the polymer molecular weight distribution or monomer conversion. Because CPU solution time tends to increase with system complexity, some strategies for lowering CPU time are discussed. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Scheduling Control optimization Polymerization reactors Grade transition mixed-integer dynamic optimization

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dynamic predictive scheduling of operational strategies for continuous processes using mixed-logic dynamic optimization

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COMPUTERS & CHEMICAL ENGINEERING 2007年第5-6期31卷 574-587页

作者： Busch, Jan Oldenburg, Jan Santos, Marcella Cruse, Andreas Marquardt, Wolfgang Univ Aachen Rhein Westfal TH Aachen Lehrstuhl Prozesstech D-52064 Aachen Germany

Industrial processes are usually operated in a highly dynamic environment, e.g. with time-varying market prizes, customer demand, technological development or up- and downstream processes. Due to these disturbances, the operational strategies comprising objectives and constraints are regularly adjusted to reflect a change in the environment in order to achieve or maintain optimal process performance. The related operational objectives need not only be of an economical nature, but can also include flexibility, risk or ecological objectives. In this paper, a novel methodology is presented for the modeling and dynamic predictive scheduling of operational strategies for continuous processes. Optimal control actions are computed on a moving horizon employing discrete-continuous modeling and mixed-logic dynamic optimization as introduced by Oldenburg et al. (2003). The approach is successfully demonstrated considering the operation of a wastewater treatment plant. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： dynamic predictive scheduling plant scheduling mixed-logic dynamic optimization mixed-integer dynamic optimization wastewater treatment

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Optimal design and control of a reactive distillation system

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ENGINEERING optimization 2005年第7期37卷 733-753页

作者： Panjwani, P Schenk, M Georgiadis, MC Pistikopoulos, EN Univ London Imperial Coll Sci & Technol Ctr Proc Syst Engn Dept Chem Engn London England

This article presents the design and control of a reactive distillation system utilizing recent advances in mixed integer dynamic optimization. A high fidelity dynamic model is used to predict the behavior of the process under time-varying disturbances. Design and control decisions, involving both discrete and continuous variables, are simultaneously optimized leading to a more economically attractive and better controlled system than that obtained by following a sequential optimization approach. It is shown that the resulting design and control scheme can guarantee feasible operation under bounded uncertainty at a minimum total average cost, representing similar to 17% savings over the original design.

关键词： mixed-integer dynamic optimization reactive distillation process design and control

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