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Optimal operation of water distribution networks by predictive control using MINLP

WATER SA 2003年第4期29卷 393-404页

作者： Biscos, C Mulholland, M Le Lann, MV Buckley, CA Brouckaert, CJ Univ KwaZulu Natal Sch Chem Engn Pollut Res Grp ZA-4041 Durban South Africa Lab Anal & Architecture Syst F-31077 Toulouse 4 France Inst Natl Sci Appl Dept GEI F-31077 Toulouse 4 France

This paper presents an approach for the operational optimisation of potable water distribution networks. The maximum of the use of low-cost power (e.g. overnight pumping) and the maintenance of a target chlorine concentration at final delivery points were defined as important optimisation objectives. The first objective is constrained by the maintenance of minimum emergency volumes in all reservoirs, while the second objective would include the minimisation of chlorine dosage and re-dosage requirements. The combination of dynamic elements (e.g. reservoirs) and discrete elements (pumps, valves, routing) makes this a challenging predictive control and constrained optimisation problem, which is being solved by MINLP (mixed integer non-linear programming). Initial experimental results show the performance of this algorithm and its ability to control the water distribution process.

关键词： drinking water mixed integer non-linear programming model predictive control

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Numerical and environmental, considerations on a complex industrial mixed integer non-linear programming (MINLP) problem

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COMPUTERS & CHEMICAL ENGINEERING 1999年第10期23卷 1545-1561页

作者： Harjunkoski, I Westerlund, T Pörn, R Abo Akad Univ Proc Design Lab FIN-20500 Turku Finland Abo Akad Univ Dept Math FIN-20500 Turku Finland

In the present paper an essential problem in the process industry, the trim-loss problem, is considered. The problem can be identified in many different industries (for instance in the paper and metal industry) but here, the main focus is on the paper industry or more precisely, the paper-converting industry. In the trim-loss problem at a paper-converting mill, an optimal strategy is sought for cutting a wide raw-paper reel into narrower, customer-specified product reels in such a way that the appearance of waste, the trim loss, is minimized. Besides being a numerically challenging non-convex mixed integer non-linear programming problem, the choice of objective is of great importance and a non-trivial task in order to alter sustainable and environmentally benign solutions. Therefore, in the following some transformation techniques for overcoming bilinearity and solving the original problem into its global optimality are presented. The transformations are followed by an analysis and comparison of different ways to formulate the objective function. Finally, a set of example problems are solved in order to project the theoretical considerations to more practical level. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： optimization mixed integer non-linear programming trim loss problems waste minimization

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Integrating reliability optimization into chemical process synthesis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2002年第3期78卷 247-258页

作者： Goel, HD Grievink, J Herder, PM Weijnen, MPC Delft Univ Technol Fac Technol NL-2600 GA Delft Netherlands Delft Univ Technol Fac Sci Appl NL-2600 GA Delft Netherlands

The growing need to achieve high availability for large integrated chemical process systems demands higher levels of system reliability at the operational stage. In these circumstances, it has become critical to consider the reliability aspects of a system and its components at the design stage. Traditional reliability/availability analysis methods and maintenance optimization frameworks, commonly applied at the design stage, are limited in their application, as in most of these methods the designer is required to specify the process system components, their connectivity and their reliabilities a priori. As a result, these traditional methods do not provide the flexibility to reconfigure a process or select initial reliabilities of equipment in a way that maximizes the inherent plant availability at the design stage. In this paper, we developed an optimization framework by combining the reliability optimization and process synthesis challenges and the combined optimization problem is posed as a mixed integer non-linear programming optimization problem. The proposed optimization framework features an expected profit objective function, which takes into account the trade-off between initial capital investment and the annual operational costs by supporting appropriate estimation of revenues, investment cost, raw material and utilities cost, and maintenance cost as a function of the system and its component availability. The effectiveness and usefulness of the proposed optimization framework is demonstrated for the synthesis of the hydrodealkylation process (HDA) process. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： reliability optimization process synthesis mixed integer non-linear programming conceptual design

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Parallel solution of disjunctive MINLP problems

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CHEMICAL ENGINEERING COMMUNICATIONS 2001年第1期185卷 115-124页

作者： Björkqvist, J Westerlund, T Abo Akad Univ Proc Design Lab Dept Chem Engn FIN-20500 Turku Finland

In this paper, a system for parallel solution of large scale disjunctive optimization problems is presented. Disjunctive optimization problems occur in process engineering;both in production scheduling and in process synthesis. In general these problems are computationally very hard to solve. This system combines the demands of process engineering optimization problems with the potential CPU-power of the computers connected to the Internet. The system is capable of using an arbitrary number of computers connected to the Internet for distributed solution of rigorous industrial optimization problems.

关键词： optimization disjunctive constraints mixed integer non-linear programming transformations scheduling problems

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A cutting plane method for minimizing pseudo-convex functions in the mixed integer case

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COMPUTERS & CHEMICAL ENGINEERING 2000年第12期24卷 2655-2665页

作者： Pörn, R Westerlund, T Abo Akad Univ Proc Design Lab FIN-20500 Turku Finland Abo Akad Univ Dept Math FIN-20500 Turku Finland

In this paper, a mixed integer non linear programming (MINLP) algorithm for minimizing pseudo-convex functions under pseudo-convex constraints is proposed and illustrated. The solution procedure is iterative and relies on successive linear approximation of the objective function and on a line-search technique. The whole procedure is then embedded within the framework of a existing cutting plane method for mixed integer non-linear programs. This enables us to solve general MINLPs with pseudo-convex objective and pseudo-convex inequality constraints to global optimality. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： mixed integer non-linear programming pseudo-convex functions cutting plane method

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On the use of a mixed integer non-linear programming model for refrigerant design

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International Transactions in Operational Research 1997年第1期4卷 45-54页

作者： Churi, N. Achenie, L.E.K. University of Connecticut United States

In this paper, a novel mixed integer non-linear programming model for single component refrigerant design is presented. At the heart of the approach is a new formulation for structural feasibility that allows multiple bonds, connectivity and isomers. The strategy defines a set of structural groups (consisting of atoms), subsets of which are combined to form refrigerant molecules. Molecules formed this way must obey structural and stability constraints. The design objective is to build a refrigerant molecule that has desired physical properties and performance characteristics. These attributes are formulated as mathematical programming constraints and performance objectives which involve both continuous and integer variables. With the current renewed interest in the environment, the suggested approach is applied to refrigerant design with an environmental constraint. The results indicate the viability and the flexibility of the approach. © 1997 Wiley Periodicals, Inc.

关键词： Computer aided molecular design mixed integer non-linear programming Refrigerant Structural feasibility

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Convexification of different classes of non-convex MINLP problems

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COMPUTERS & CHEMICAL ENGINEERING 1999年第3期23卷 439-448页

作者： Pörn, R Harjunkoski, I Westerlund, T Abo Akad Univ Dept Math FIN-20500 Turku Finland Abo Akad Univ Proc Design Lab FIN-20500 Turku Finland

In the present paper, convexification strategies for certain kinds of discrete and integer non-convex optimization problems are introduced and discussed. We show how to solve problems with both posynomial and negative binomial terms in the constraints. The convexification technique may in some cases be generalized to include continuous variables. Posynomial functions are non-convex and for such functions no straightforward methods for finding the optimal solution exist. Such functions appear frequently in different kinds of chemical engineering problems. The different transformation techniques are illustrated in the form of short examples. The techniques are finally applied to a large, bilinear, trim loss problem regularly encountered at paper-converting mills. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： binomials convexification discrete and integer variables mixed integer non-linear programming optimization posynomials

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Automated reformulation of disjunctive constraints in MINLP optimization

Automated reformulation of disjunctive constraints in MINLP ...

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European Symposium on Computer Aided Process Engineering-9 (Escape-9)

作者： Björkqvist, J Westerlund, T Abo Akad Univ Proc Design Lab Dept Chem Engn FIN-20500 Turku Finland

In this paper, a general method for handling disjunctive constraints in a MINLP optimization problem is presented. This method automates the reformulation of an, in a abstract modeling language given, optimization problem into a mathematical problem that is solvable with existing optimization tools. This implementation can use common MILP solvers for linear problems and nonlinear methods for quasi-convex optimization problems. It also includes the possibility to use the logics in the system and solve the system logically using subproblems.

关键词： disjunctive constraints mixed integer non-linear programming transformations scheduling problems process synthesis

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Studies in adaptive random search optimization for MINLP problems

Studies in adaptive random search optimization for MINLP pro...

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European Symposium on Computer Aided Process Engineering-9 (Escape-9)

作者： Bochenek, R Jezowski, J Poplewski, G Jezowska, A Rzeszow Univ Technol Dept Chem Engn & Proc Control Rzeszow Poland

Stochastic optimization approaches are achieving growing interest in application to difficult optimization problems common in chemical and process engineering. They are easy to use especially in cases where there are disjunctive functions and logical conditions. M-LI optimization algorithm from adaptive random search (ARS) strategy has been developed for most advantageous optimization problems, i.e. MINLP ones. This method is an extension of LJ algorithm developed by Jaakola and Luus (1974) for problems with continuous variables. The main modification in M-LJ method is the change of distribution function for integer variables. The results of tests are presented and the comparison with the performance of other stochastic methods as e.g. MSIMPSA algorithm from Cardoso et al. (1997). M-LJ has shown high robustness and can be seen easy to use optimization tool for certain MINLP problems. Also, the re-formulation of difficult optimization MINLP problem for batch processes from Kocis and Grossmann (1988) has been developed which allows for solving it as NLP task.

关键词： mixed integer non-linear programming adaptive random search distribution function

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Studies in adaptive random search optimization for MINLP problems

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COMPUTERS & CHEMICAL ENGINEERING 1999年第sup1期23卷 S483-S486页

作者： Bochenek, R Jezowski, J Poplewski, G Jezowska, A Rzeszow Univ Technol Dept Chem Engn & Proc Control Rzeszow Poland

关键词： mixed integer non-linear programming adaptive random search distribution function

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