Christos A. Frangopoulos

CGAM Problem: Definition and Conventional Solution

Note: (idem 93.31). Reference LENI-ARTICLE-1994-026 Record created on 2005-08-08, modified on 2017-05-10

Thermo-economic functional analysis and optimization

Thermo-economie functional analysis is a method for optimal design or improvement of complex thermal systems. Thermodynamic concepts are combined with economic considerations in a systems approach. Units are the basic elements of the system; each unit has a particular quantified function (purpose or product). The distribution of functions establishes inter-relations between units or between the system and the environment and leads to a functional diagram of the system. The optimization minimizes the total cost of owning and operating the system, subject to constraints revealed by the functional diagram and analysis. The general formulation and a numerical example are presented.

Application of the thermoeconomic functional approach to the CGAM problem

A gas-turbine cogeneration system with a regenerative air preheater and a single-pressure exhaust gas boiler serves as an example for application of three different analysis and optimization procedures: 1.(i) direct use of a nonlinear programming algorithm,2.(ii) thermoeconomic functional approach, and3.(iii) modular simulation and optimization of the system. The results obtained with the three methods are compared with each other. The sensitivity of the optimal solution to certain parameters and of the objective function to the independent variables is studied. Conclusions are drawn regarding the applicability of each procedure to more complicated optimization problems.

Effect of reliability considerations on the optimal synthesis, design and operation of a cogeneration system

In most of the publications on optimization of energy systems, it is considered that the equipment is available for operation at any instant of time (i.e. it is not subject to failure) except, perhaps, of pre-determined periods of maintenance. Thus, it is left to the designer to decide empirically how to provide the system with redundancy, which is necessary in case of equipment failure. However, in this way, the final configuration may not be optimal. In the present work, reliability and availability are introduced in the thermoeconomic model of the system, so that redundancy is embedded in the optimal solution; in addition, more realistic values are obtained for the cost and profit, if any. The state-space method (SSM) of reliability analysis is used. The optimization problem is formulated at two levels: (A) synthesis and design, (B) operation under time-varying conditions. For the solution of the problem at level A and also at level B with no failure, a genetic algorithm coupled with a deterministic one is used. In case of partial failure, the optimization problem is solved by the Intelligent Functional Approach (IFA). The use of IFA combined with SSM is proved to be very efficient for decision making regarding systems under partial failure. It turned out that reliability aspects have a direct and significant impact on the optimal result at each one of the three levels: synthesis, design and operation.

Operation optimization of an industrial cogeneration system by a genetic algorithm

Large process plants need energy in several forms (mechanical energy, electricity, steam, hot water etc.), which very often come from a variety of sources such as gas-turbine generators, steam-turbine generators, exhaust gas boilers, fuel-burning boilers etc. In addition, the utility network serves as a source of supplementary electricity if needed, or as a sink when excess electricity is produced. The cost of energy is one of the major contributors to the total operating cost of a process plant. Consequently, minimization of this cost is of utmost importance. Due to the variety of energy sources, the interdependency between sources and the variation of technical and economic conditions with time (e.g. change of load, deterioration of equipment, change of fuel and electricity prices etc.), the task of minimizing energy cost is far from trivial. Methods and algorithms to solve these types of problems are still a subject of research because of the following reasons: the problems are usually nonlinear with multimodal objective functions that may contain both discrete (e.g. integer) and continuous variables. No single method has been successful with every problem of this type. In the present work, a genetic algorithm (GA) is applied for the operation optimization of a cogeneration system, which supplies a process plant with electricity and steam at various pressure levels. A mathematical simulation model of the system has been developed, taking into consideration the real condition of main equipment, as it is revealed by an appropriate set of measurements. The GA is combined with the simulation model, in order to solve the optimization problem under specified constraints.

A method for taking into account environmental impacts in the economic evaluation of energy systems

Decisions to produce and consume useful forms of energy will better reflect societys desires for environmental quality by internalizing the external costs of production. Internalization of external environmental costs may significantly affect decisions regarding selection of the type of the energy system for a particular application, as well as design and operation of the system. In the present work, main classes of economic approaches for environmental protection are presented and critically reviewed. A procedure is proposed in order to take into consideration the environmental impacts in the economic analysis of energy systems. The analytical formulation of the procedure is followed by a numerical example. Application of the method reveals the critical values of environmental penalties that result in a break-even operation of the particular system. Also, it is shown that the proposed procedure can be used as a proxy method to calculate environmental externalities for a particular system in a given environment. In addition, by assessing the unit cost of reducing pollutants by abatement technologies, the method can turn into a useful tool at the hands of those seeking a sound basis to set environmental charges and incentives. If, on the other hand, the environmental and social cost of a pollutant is known, then the method can be used to reveal the type and level of the incentive that the national economy would be willing to provide.

A Genetic Algorithm for operation optimization of an industrial cogeneration system

Genetic Algorithms (GAs) have been developed in the last three decades in an attempt to imitate the mechanics of the selection process in natural genetics. They also contain many elements of expert systems. In the present work, a GA is applied for the optimization of the operation of a cogeneration system, which supplies a process plant with electricity and steam at various pressure levels. A mathematical simulation model of the system has been developed taking into consideration the real conditions of the main equipment, as determined by an appropriate set of measurements. The GA is combined with the simulation model in order to solve the optimization problem under specified constraints. The capability of GAs to handle objective functions of any complexity with both discrete (e.g., integer) and continuous variables, as well as their capability of optimizing only on the basis of the results of the simulation model, make GAs successful in this type of problems.

Towards synthesis optimization of a fuel-cell based plant

Abstract In a previous work by the authors, the performance of a plant based on solid oxide fuel cells (SOFC) which produces its own fuel by solar energy had been studied. The system consists not only of the fuel/electrolytic cell unit and the gas turbine but also of compressors, pumps, heat exchangers, etc. In the aforementioned work, a “workable” synthesis (structure) of the system had been considered, which is not necessarily the best one. In the meantime, an attempt has been made to improve on the structure and, if possible, to determine the one with the best performance under certain conditions. For this purpose, the Pinch method together with exergy analysis has been applied, while the performance of the system for any configuration has been determined by a simulation model. The results of this work are presented in this article.

Functional decomposition for optimal design of complex thermal systems

Thermoeconomic functional analysis (TFA), a method for optimal design or improvement of complex thermal systems, has an intrinsic adaptability to conditions of decomposition. If specified conditions are satisfied, the system may be decomposed into smaller subsystems and the solution of the optimization problem is facilitated significantly. Complete and partial functional decomposition are derived as special cases of TFA. An example demonstrates the applicability of the methods.

Thermo-economic optimization of synthesis, design and operation of a marine organic Rankine cycle system

The recovery of high temperature thermal energy released by propulsion engines in order to cover thermal loads is commonplace in contemporary ships. However, the medium- and low-temperature thermal energy is only partially exploited or not exploited at all. In the present work, an organic Rankine cycle system driving an electric generator is considered, in addition to the exhaust gas boiler, in order to recover available heat and produce electrical energy. The specifications of the system are determined by an optimization procedure taking economic criteria into consideration, apart from the technical criteria usually used in this kind of studies. More specifically, with the net present value as the objective function and by application of optimization algorithms, the optimal synthesis, design and operation of the organic Rankine cycle system are determined. For the particular vessel considered, the installation of the organic Rankine cycle is technically feasible and economically profitable, with a dynamic payback period of 4 years. The solution of the optimization problem is supplemented with a sensitivity analysis with respect to important parameters.