Optimization of Power Plant Design: Stochastic and Adaptive Solution Concepts

Hillermeier, Claus; Hüster, Steffen; Märker, Wolfgang; Sturm, Thomas F.

doi:10.1007/978-1-4471-0519-0_1

Cited by 2 publications

(5 citation statements)

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“…However, it may encompass also a large number of meaningless or even infeasible alternatives, which potentially lead to the forbiddingly large computational effort, as the computation complexity and difficulty of the optimal synthesis problems almost always increase exponentially with the number of components considered in the superstructure [185,203,212]. In addition, for realistic problems, the number of structural alternatives tends to be very large, e.g., over 10 9 structural enumerations for the feed-water preheating train of thermal power plants [213]. Considering the current computation capability of mathematical programming, it is basically not possible to take all possible alternatives into account.…”

Section: Superstructure Generationmentioning

confidence: 99%

“…To cope with some of these fundamental problems, many systematic or even algorithmic generation of superstructure have been developed. Toward systematic generation, there are stage-wise synheat superstructure for HEN [214], multi-level hierarchical aggregation [175,215,216], state-task and state-equipment network [217] for process systems, or decision tree [213,218] for power plants.…”

Section: Superstructure Generationmentioning

confidence: 99%

“…Regarding the use of decision tree, Hellermeier et al [213] investigated the design synthesis of feedwater preheater train of thermal power plants via genetic and stochastic optimization techniques. The structural alternatives are represented by (modified) decision trees (as a type of superstructure) to consider the hierarchical parameter dependencies, and a set of rules are defined to find a feasible layout (a tuple of values of decision variables).…”

Section: Superstructure-based Synthesismentioning

confidence: 99%

“…Unfortunately, practical constraints have not been compressively considered yet in most research papers. Specifically to thermal power plants, the steam-extraction pressure for de-aerator should be limited within the range from 5 to 15 bar [213] due to technical reasons; the pressures of steam extractions are not completely continuous but are constrained by the turbine design; the secondary turbine which supplies only one steam extractions is less likely to be implemented in real power plants; the cost functions of all components should be developed with more available industrial data or with the participation of industrial partners. Only when well-established objectives are optimized under reasonable real-world constraints could the number, size and operation mode of each component be well-constrained.…”

Section: Real-world Optimal Designs and Retrofitsmentioning

confidence: 99%

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A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

et al. 2018

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To reach optimal/better conceptual designs of energy systems, key design variables should be optimized/adapted with system layouts, which may contribute significantly to system improvement. Layout improvement can be proposed by combining system analysis with engineers’ judgments; however, optimal flowsheet synthesis is not trivial and can be best addressed by mathematical programming. In addition, multiple objectives are always involved for decision makers. Therefore, this paper reviews progressively the methodologies of system evaluation, optimization, and synthesis for the conceptual design of energy systems, and highlights the applications to thermal power plants, which are still supposed to play a significant role in the near future. For system evaluation, both conventional and advanced exergy-based analysis methods, including (advanced) exergoeconomics are deeply discussed and compared methodologically with recent developments. The advanced analysis is highlighted for further revealing the source, avoidability, and interactions among exergy destruction or cost of different components. For optimization and layout synthesis, after a general description of typical optimization problems and the solving methods, the superstructure-based and -free concepts are introduced and intensively compared by emphasizing the automatic generation and identification of structural alternatives. The theoretical basis of the most commonly-used multi-objective techniques and recent developments are given to offer high-quality Pareto front for decision makers, with an emphasis on evolutionary algorithms. Finally, the selected analysis and synthesis methods for layout improvement are compared and future perspectives are concluded with the emphasis on considering additional constraints for real-world designs and retrofits, possible methodology development for evaluation and synthesis, and the importance of good modeling practice.

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Section: Superstructure Generationmentioning

confidence: 99%

Section: Superstructure Generationmentioning

confidence: 99%

Section: Superstructure-based Synthesismentioning

confidence: 99%

Section: Real-world Optimal Designs and Retrofitsmentioning

confidence: 99%

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A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

et al. 2018

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“…Such applications tend to be domain specific and generally relate to the optimization of a particular component where welldefined mathematical models of the component provide an evaluation function~e.g., Parmee & Vekeria, 1997;Elby et al, 1998;Hajela, 1998;Koza et al, 1998;Olhoffer et al, 2000!. More rarely, application has related to whole system design or optimization~e.g., Emmerich et al, 2000;Hillermeier et al, 2000!. Significantly less research effort has concentrated on the higher conceptual levels of the design process, where problem representation and uncertainty can cause significant difficulty in terms of the design of an appropriate evaluation function.…”

Section: Introductionmentioning

confidence: 99%

Improving problem definition through interactive evolutionary computation

Parmee

2002

AIEDAM

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Interactive Evolutionary Design Systems and illustrates these via extensive results from research re various system component interaction with each other and with the user. Identified areas requiring further research set the scene for more recent ACDDM work expanding both utility of approach whilst investigating and implementing novel user?centric computational intelligence systems for early stage design. AIEDAM is a leading engineering journal specialising in Intelligent Systems for design and manufacture. Long standing journal with an excellent international reputation. Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. AbstractPoor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human-computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established.

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Optimization of Power Plant Design: Stochastic and Adaptive Solution Concepts

Cited by 2 publications

References 8 publications

A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

Improving problem definition through interactive evolutionary computation

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