Studies in the heat integration of chemical process plants

Viswanathan, Murali; Evans, Lawrence B.

doi:10.1002/aic.690331104

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…Our solution involved the concept of hot-to-hot and cold-to-cold matches (heat exchange between streams of the same type). Such solutions were apparently first proposed on heuristic grounds by Grimes et al (1 982) and have since been found algorithmically by us and by Viswanathan and Evans (1987), who refer to such matches as dual stream solutions, since a hot or cold stream plays a role of both a hot and a cold stream in different parts of the network.…”

Section: 'Grand Canonical' Optimization: Heat Exchanger Networkmentioning

confidence: 99%

“…Capital investment is clearly greater using this approach since a single exchanger in the constraint-free MER design is replaced by two exchangers and the additional piping necessary for the intermediate stream. By using hot-to-hot and cold-to-cold matches, the designs found algorithmically for forbidden match problems by GCSA and by the method of Viswanathan and Evans (1987) circumvent the need for intermediate streams, essentially by using existing process streams for the same purpose.…”

Section: 'Grand Canonical' Optimization: Heat Exchanger Networkmentioning

confidence: 99%

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Process optimization via simulated annealing: Application to network design

1989

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Simulated annealing is a multivariable optimization technique based on the Monte Carlo method used in statistical mechanical studies of condensed systems and follows by drawing an analogy between energy minimization in physical systems and costs minimization in design applications. In this paper, simulated annealing is introduced and reviewed. The utility of the method for optimization of chemical processes is illustrated by applying it to the design of pressure relief header networks and heat exchanger networks. IntroductionComputer-aided chemical process design and optimization increasingly play an important role in reducing the time lag between process innovation and commercial implementation, in ensuring the efficiency and flexibility of new chemical plants, and in improving the operating efficiencies of existing plants.This paper applies the simulated annealing algorithm to design problems in chemical engineering. Simulated annealing is a general method for treating a broad class of large, multivariable optimization problems. It has found wide application in the physical sciences and engineering, but evidently not previously to problems in chemical engineering.The simulated annealing algorithm was proposed by Kirkpatrick et al.(1 983), who drew an analogy between the cooling of a fluid and the optimization of a complex system. In order for a pure substance to be 'frozen' into a perfect or nearly perfect crystal, it must be annealed by first melting and then cooling very slowly. If the substance is cooled too quickly, a crystal with many defects or a glass will be formed; the resulting structure will be far from the highly ordered, minimum energy, crystalline state. To illustrate the effectiveness of simulated annealing, Kirkpatrick et al. (1983) applied the method to two combinatorially large optimization problems: the traveling salesman problem and the layout of very-large-scale integrated (VLSI) circuit computer chips. For the latter, the placement and wiring of several thousand discrete circuit elements are principal concerns. In general, the shorter the connections are between the elements, the more optimal is the design; however, the circuit elements cannot be placed too close together since they can interfere with each other. Simulated annealing has now become a standard technique for designing VLSI chips. In particular, it is often used to assess whether designs obtained by other, perhaps heuristic, methods can be improved upon by starting the anneal- W. B. Dolan P. T. Cummings M. D. LeVanDepartment of Chemical Engineering University of Virginia Charlottesville, VA 22901 ing a t relatively low temperature using the proposed design as the initial configuration.Simulated annealing is based on the theory of Markov chains, as described below. It accepts and rejects randomly generated 'moves' on the basis of a probability related to an 'annealing temperature'. It can accept moves which change the value of an objective function in the direction opposite to that of the desired long-term trend. Thus, for a gl...

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Section: 'Grand Canonical' Optimization: Heat Exchanger Networkmentioning

confidence: 99%

Section: 'Grand Canonical' Optimization: Heat Exchanger Networkmentioning

confidence: 99%

Process optimization via simulated annealing: Application to network design

1989

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“…Furthermore, heat exchange is not permitted between streams of same type (hotehot/coldecold). However, it has been proven that the utility requirement can also be reduced by such forbidden matches from several former works [36,37,41], which may reduce the TAC simultaneously. In a recent work, Pariyani et al [28] proposed a randomized algorithm to generate networks by random sampling with a series of structure and thermodynamics bounds, which permits to arrange utilities not only at the end of each stream.…”

Section: Hen Structure Representationmentioning

confidence: 95%

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

Peng

Cui

2015

Applied Thermal Engineering

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“…From Dolan et al (1987) and Wiswanathan and Evans (1987) the utility requirement can be reduced in a forbidden-match problem. The method consists of matching one of the streams (hot/cold) of the forbidden match with another same stream (hot/cold) by utilizing the heat content of the second stream; as a result, the utility consumption could be considerably reduced.…”

Section: Problem Statementmentioning

confidence: 96%

Synthesis of heat‐exchanger network by simulated annealing and NLP procedures

et al. 1997

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A two-level procedure for heat-exchanger network synthesis problems, which is related to structural optimization, is carried out by a simulated-annealing procedure. The main control parameters, that is, the initial annealing temperature, the temperature length, and the temperature-decreasing scheme, are extensively studied on the well-known lOSPl problem, and their values are used for solving a 5SPl problem with forbidden matches and a larger scale problem, the 20SPl. In all cases, the structural search is initialized with the network involving only utilities. For each generated network, the operating conditions (temperatures and split rates) are optimized by an NLP procedure to minimize the sum of investment and operating costs. Particular attention is given to the size reduction of the NLP problem. The proposed procedure leads to a reduction by a factor of 3 of the problem size in terms of number of variables and constraints. This slave problem is solved by an SQP procedure from the IMSL Library.

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Studies in the heat integration of chemical process plants

Cited by 20 publications

References 13 publications

Process optimization via simulated annealing: Application to network design

Process optimization via simulated annealing: Application to network design

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

Synthesis of heat‐exchanger network by simulated annealing and NLP procedures

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