A Second-Law-Based Optimization: Part 1—Methodology

1996 ◽  
Vol 118 (4) ◽  
pp. 693-697 ◽  
Author(s):  
Y. M. El-Sayed
Keyword(s):  
Objective Function ◽  
Energy Systems ◽  
Second Law Of Thermodynamics ◽  
Energy System ◽  
Combined Cycle ◽  
Second Law ◽  
Complex Energy ◽  
System A ◽  
Decision Variables ◽  
Further Development

This paper deals with the optimization of complex energy systems given a cost objective function. The optimization uses a decomposition strategy based on the second law of thermodynamics and a concept for costing the components of a system. A large number of nonlinear decision variables can be optimized with enhanced convergence to an optimum. The paper is in two parts. In this part, the methodology is described. In Part 2, the methodology is applied to a simple energy system of 10 components and 19 manipulated decision parameters. The system is treated once as a single purpose combined cycle and once as a power-heat cogenerating system. The results of the application are summarized and evaluated. Further development is encouraged.

Generation of Complex Energy Systems by Combination of Elementary Processes

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
A. Toffolo ◽  
S. Rech ◽  
A. Lazzaretto
Keyword(s):  
Design Optimization ◽  
Ad Hoc ◽  
Energy Systems ◽  
Optimization Procedure ◽  
Thermodynamic Cycle ◽  
Energy System ◽  
Design Parameters ◽  
System Configuration ◽  
Complex Energy ◽  
Synthesis Design

The fundamental challenge in the synthesis/design optimization of energy systems is the definition of system configuration and design parameters. The traditional way to operate is to follow the previous experience, starting from the existing design solutions. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top–down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are feasible. To solve the general problem of the synthesis/design of complex energy systems, a new bottom–up methodology has been recently proposed by the authors, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle, composed only by the compression, heat transfer with hot and cold sources and expansion processes. So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure. In this paper, the main concepts and features of the methodology are deeply investigated to show, through different applications, how an artificial intelligence can generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.

Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems: Part 1 — Detection and Localization of Anomalies

2002 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Inverse Problem ◽  
Complex Systems ◽  
Reference Conditions ◽  
Energy Systems ◽  
Combined Cycle ◽  
Operating Conditions ◽  
The Other ◽  
Complex Energy ◽  
Operational Diagnosis ◽  
Detection And Localization

This paper presents a summary of our most recent advances in Thermoeconomic Diagnosis, developed during the last three years [1–3], and how they can be integrated in a zooming strategy oriented towards the operational diagnosis of complex systems. In fact, this paper can be considered a continuation of the work presented at the International Conference ECOS’99 [4–6] in which the concepts of malfunction (intrinsic and induced) and dysfunction [7] were analyzed in detail. These concepts greatly facilitate and simplify the analysis, the understanding and the quantification of how the presence of an anomaly, or malfunction, affects the behavior of the other plant devices and of the whole system. However, what remains unresolved is the so-called inverse problem of diagnosing [3], i.e. given two states of the plant (actual and reference operating conditions), find the causes of deviation of the actual conditions with respect to the reference conditions. The present paper tackles this problem and describes significant advances in addressing how to locate the actual causes of malfunctions, based on the application of procedures for filtering induced effects that hide the real causes of degradation. In this paper a progressive zooming thermoeconomic diagnosis procedure, which allows one to concentrate the analysis in an ever more specific zone is described and applied to a combined cycle. In an accompanying paper (part 2 [8]) the accuracy of the diagnosis results is discussed, depending on choice of the thermoeconomic model.

INDUSTRIAL APPLICATION OF A SECOND-LAW MODELLING PROCEDURE

1988 ◽  
Vol 12 (3) ◽  
pp. 153-157
Author(s):  
JOHN W. CHINNECK
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Energy Systems ◽  
Energy System ◽  
Second Law ◽  
Additional Energy ◽  
Industrial Plants ◽  
Plant Manager ◽  
Modelling Procedure ◽  
Modelling Techniques

The energy systems in large industrial plants are often very complex involving hundreds of items of equipment such as furnaces, turbines, boilers, generators, etc., and numerous energy forms such as oil, natural gas, steam, electricity and so on. It is usually not obvious how to operate the system to minimize energy consumption, thereby minimizing fuel expenditures. Computer models can be effective tools for the plant manager in tackling this problem. This paper presents the results of the application of a new modelling procedure to the energy system in an existing Canadian petrochemicals plant. The new procedure identified an estimated $600,000 per annum in additional energy savings over other modelling techniques that had been applied to the plant. The procedure includes second-law measures in a convenient and easily-applied form.

scholarly journals Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

2014 ◽  
Vol 2 (2) ◽  
pp. 26-40 ◽  
Author(s):  
Miltiadis Alamaniotis ◽  
Vivek Agarwal
Keyword(s):  
Support Vector Regression ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Energy Systems ◽  
Energy System ◽  
Support Vector ◽  
Complex Energy ◽  
Anticipatory Control ◽  
Certainty Factor ◽  
Inference System

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. The proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially, a set of support vector regressors is adopted for making predictions over critical system parameters. The predicted values are used as input to a two-stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real-world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.

scholarly journals EDITORIAL

2014 ◽  
Vol 13 (2) ◽  
pp. 02
Author(s):  
J. C. Ordonez
Keyword(s):  
Dynamic Models ◽  
Energy Systems ◽  
Second Law Of Thermodynamics ◽  
Heat Transfer Fluid ◽  
Second Law ◽  
Operational Parameters ◽  
Temperature Levels ◽  
System Configurations ◽  
And Control ◽  
Physical Phenomena

I would like to direct the attention of the reader to Second Law of thermodynamics approaches to the control and optimization of dynamic energy systems. Second Law approaches to optimization and control have emerged as an enhancement beyond First Law efforts. Second Law-based metrics have the inherent advantage of being applicable to a wide variety of systems under a variety of physical phenomena (e.g., heat transfer, fluid flow, electromagnetic effects, radiation). Exergy and Second Law efficiencies have been utilized as metrics in optimization of energy systems and in the formulation of control schemes, however, most efforts have emphasized on steady-state systems and have addressed mainly operational parameters (e.g., flow rates, pressure and operating temperature levels). Second Law based metrics are expected to be useful not only for operational studies, but more importantly in leading to the discovery of optimal system configurations. Dynamic models in conjunction with Second Law metrics can lead to the optimization of important system design features beyond operational parameters.

On the Teaching of Performance Evaluation and Assessment of a Combined Cycle Cogeneration System

2008 ◽  
Author(s):  
A. O¨zer Arnas ◽  
Daisie D. Boettner ◽  
Seth A. Norberg ◽  
Gunnar Tamm ◽  
Jason R. Whipple
Keyword(s):  
Performance Evaluation ◽  
Second Law Of Thermodynamics ◽  
Combined Cycle ◽  
Second Law ◽  
First Law Of Thermodynamics ◽  
Systematic Fashion ◽  
Cogeneration System ◽  
The Subject ◽  
Alternative Designs ◽  
Evaluation And Assessment

Performance evaluation and assessment of combined cycle cogeneration systems are not taught well in academia. One reason is these parameters are scattered in the literature with each publication starting and ending at different stages. In many institutions professors do not discuss or even mention these topics, particularly from a second law perspective. When teaching combined cycle cogeneration systems to undergraduates, the professor should introduce pertinent parameters in a systematic fashion and discuss the usefulness and limitations of each parameter. Ultimately for a given situation, the student should be able to determine which parameters form the most appropriate basis for comparison when considering alternative designs. This paper provides two approaches, one based on energy (the First Law of Thermodynamics) and the other based on exergy (the Second Law of Thermodynamics). These approaches are discussed with emphasis on the “precise” teaching of the subject matter to undergraduates. The intent is to make coverage of the combined cycle cogeneration systems manageable so that professors can appropriately incorporate the topic into the curricula with relative ease.

Progressive Filtration of the Induced Effects in Thermoeconomic Diagnosis of Energy Systems

2005 ◽  
Author(s):  
V. Verda ◽  
R. Borchiellini
Keyword(s):  
Heat Recovery ◽  
Energy Systems ◽  
Energy System ◽  
Operating Conditions ◽  
Physical Models ◽  
Linear Functions ◽  
Heat Recovery Steam Generator ◽  
System A ◽  
History Of ◽  
Simple Models

In this paper, the thermoeconomic diagnosis of an energy system is discussed. Several important contributions that make the diagnosis more reliable and practical are introduced. This is obtained through an initial filtration of the effects caused by the dependence of the efficiencies of components on their operating condition. With respect to some previously proposed approaches, simple models are used to achieve this objective. These models are productive models, relating resources and products through linear functions. The drawbacks associated with the use of these simple models are overcome through the use of a technique called the anamnesis, which is the analysis of the case history of a system. A second contribution introduced in this paper is constituted by the analysis of four significant cases of anomalies that can occur in a heat recovery steam generator. Two of them have been obtained by simulating the presence of a single anomaly, each time in a different component. In the other cases, two anomalies have been produced at the same time in two different components. The operating conditions have been obtained by using a simulator, but the effects caused by errors in measurements are taken into account. An analysis has been also performed in order to present the advantages connected with the use of simple productive models, instead of physical models, when measured data are processed.

Second Law Analysis of Integrated Solar Combined Cycle Power Plants

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
S. Can Gülen
Keyword(s):  
Power Generation ◽  
Power Plants ◽  
Thermal Power ◽  
Second Law Of Thermodynamics ◽  
Combined Cycle ◽  
Solar Thermal ◽  
Second Law ◽  
Seamless Integration ◽  
Gas Combustion ◽  
Use Of Time

Integrated solar combined cycle (ISCC) is an operationally simple, clean electric power generation system that is economically more attractive vis-à-vis stand-alone concentrating solar power (CSP) technology. The ISCC can be designed to achieve two primary goals: (1) replace natural gas combustion with solar thermal power at the same output rating to reduce fuel consumption and stack emissions and/or (2) replace supplementary (duct) firing in the heat recovery steam generator (HRSG) with “solar firing” to boost power generation on hot days. Optimal ISCC design requires a seamless integration of the solar thermal and fossil-thermal technologies to maximize the solar contribution to the overall system performance at the lowest possible size and cost. The current paper uses the exergy concept of the second law of thermodynamics to distill the quite complex optimization problem to its bare essentials. The goal is to provide the practitioners with physics-based, user-friendly guidelines to understand the key drivers and the interaction among them. Ultimately, such understanding is expected to help direct studies involving heavy use of time consuming system models in a focused manner and evaluate the results critically to arrive at feasible ISCC designs.

scholarly journals Modeling multimodal energy systems

2019 ◽  
Vol 67 (11) ◽  
pp. 893-903
Author(s):  
Arash Shahbakhsh ◽  
Astrid Nieße
Keyword(s):  
Phase Transition ◽  
Energy Systems ◽  
Energy System ◽  
Integral Methods ◽  
System A ◽  
Transition Functions ◽  
Energy Flows ◽  
Information And Communication ◽  
Power To Gas

Abstract Information and communication technology (ICT) and the technology of coupling points including power-to-gas (PtG), power-to-heat (PtH) and combined heat and power (CHP) reshape future energy systems fundamentally. To study the resulting multimodal smart energy system, a proposed method is to separate the behavior of the component layer from the control layer. The component layer includes pipelines, power-lines, generators, loads, coupling points and generally all components through which energy flows. In the work at hand, a model is presented to analyze the operational behavior of the component layer. The modeling problem is formulated as state and phase transition functions, which present the external commands and internal dynamics of system. Phase transition functions are approximated by ordinary differential equations, which are solved with integral methods. State transition functions are nonlinear algebraic functions, which are solved numerically and iteratively with a modified Newton–Raphson method. In a proof-of-concept case study, a scenario shows the expected multi-sector effects based on evaluated models.

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