Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed.     

Thermoeconomic operation optimization of the Hellenic Aspropyrgos Refinery combined-cycle cogeneration system

Hellenic Aspropyrgos Refinery (HAR) is a state-owned petroleum refinery with a capacity of 130,000 barrels per day. The electric and part of the thermal loads of HAR are covered by a combined-cycle cogeneration plant of 54 MW_e capacity, which is interconnected with the utility grid. The plant consists of two gas-turbine generators, two exhaust-gas boilers, four fuel-oil boilers and one steam-turbine generator. Steam is produced at four levels; high, medium, low and very low pressure. Low-sulphur fuel oil is burned in the boilers, while the gas turbines can operate in any one or in a combination of (a) diesel oil, (b) process-generated fuel gas. (c) a mixture of propane and propylene (LPG). Connection to the utility network allows for importing additional electricity, if there is need, or for exporting excess electricity. Due to the variety of sources which can be used to cover the loads, the interdependency between sources and the variation of technical and economic conditions with time, questions such as the following arise. Given the technical (e.g. needs in electricity and heat), environmental and economic conditions at any instant of time, what source and at what load should be used? Which is the quantity of electricity bought from or sold to the utility grid? In order to answer these questions, an optimization procedure has been developed which is supported by a thermoeconomic analysis of the system and modelling of the performance of the main components. The minimization of the operation expenses has been selected as the objective function. A computer program has been developed for the numerical solution of the optimization problem. In the present work, the optimization procedure and the computer program are described, numerical results are presented which show the importance of applying such a procedure to real-world complex systems, a sensitivity analysis with respect to important parameters is performed and conclusions are drawn regarding the usefulness and further improvements of the program.     

Multi-objective self-adaptive algorithm for highly constrained problems: Novel method and applications

As a substantive input to resolve the industrial systems and challenging optimization problems, which are multi-objective in nature, the authors introduce an emerging systematic multi-objective optimization methodology for large-scale and highly-constrained industrial production systems. The methodology uses a simulation-based optimization framework built on a novel multi-objective evolutionary algorithm that exhibits several specific innovative features to maintain genetic diversity within the population of solutions and to drive the search towards the Pareto-optimal set/front. This novel algorithm was validated using standard test functions and the results demonstrate undoubtedly that the proposed algorithm computes accurately the Pareto-optimal set for optimization problems of at least two-objective functions. Next, the algorithm was applied on a base case cogeneration optimization problem with three-objective functions named the modified CGAM problem. The modified problem includes concentrations and tax rates of pollutant emissions (i.e. CO₂ and NO_x). The multi-objective optimization of such a problem consists of simultaneously maximizing the exergetic efficiency of the cogeneration plant, minimizing the total cost rate (including pollutant tax rate), and minimizing the specific rate of pollutant emissions. A fuel-to-air equivalence ratio ranging from 0.5 to 1.0, and pollutant tax rates of 0.15 $/kg CO₂, and 7.50 $/kg NO_x were used to compute the surfaces of the Pareto fronts. The results found for the modified CGAM problem clearly demonstrate the applicability of the proposed algorithm for optimization problems of more than two-objective functions with multiple constraints. The results strengthen the fact that there is no single optimal solution but rather a set of optimal solutions that present the best trade-off alternatives from which a decision-maker can select the appropriate final decision. Also, the study emphasizes the key role of both economic and environmental issues in the optimization problem of energy systems.     

Multi-objective optimization of a multi water-to-water heat pump system using evolutionary algorithm

This paper deals with the energy recovery in the dairy industry. Thermodynamic, economic and environmental optimization of three water-to-water heat pumps has been studied in order to replace totally or partially a fuel boiler used to produce heat at different temperature levels in a cheese factory. These heat pumps have their evaporators connected to one effluents source and two of them are equipped by storage tanks at the condenser side. Multi-objective optimization permits optimal repartition of mass flow rates of effluents and optimal choice of electrical power of the compressors and volumes of storage tanks. The thermodynamic objective is based on the exergy destruction in the whole system. The economic objective is based on the investment cost and the operating cost obtained with the heat pump system. The environmental impact objective has been defined and expressed in cost terms by considering a CO₂ taxation (carbon tax) on the GHG emissions. This objective has been integrated with the economic objective. Multi-objective genetic algorithms are used for Pareto approach optimization.     

Multi-objective optimization of the airfoil shape of Wells turbine used for wave energy conversion

Wells turbine is one of the technical systems allowing an efficient use of the power contained in oceans’ and seas’ waves with a relatively low investment level. It converts the pneumatic power of the air stream induced by an Oscillating Water Column into mechanical energy. The standard Wells turbines show several well-known disadvantages: low tangential force, leading to low power output from the turbine; high undesired axial force; usually a low aerodynamic efficiency and a limited range of operation due to stall. In the present work an optimization process is employed in order to increase the tangential force induced by a monoplane Wells turbine using symmetric airfoil blades. The automatic optimization procedure is carried out by coupling an in-house optimization library (OPAL (OPtimization ALgorithms)) with an industrial CFD (Computational Fluid Dynamics) code (ANSYS-Fluent). This multi-objective optimization relying on Evolutionary Algorithms takes into account both tangential force coefficient and turbine efficiency. Detailed comparisons are finally presented between the optimal design and the classical Wells turbine using symmetric airfoils, demonstrating the superiority of the proposed solution. The optimization of the airfoil shape leads to a considerably increased power output (average relative gain of +11.3%) and simultaneously to an increase of efficiency (+1%) throughout the full operating range.     

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

In this paper, a novel cogeneration system integrating Kalina cycle, CO₂ chemical absorption, process, and flash‐binary cycle is proposed to remove acid gases in the exhaust gas of solid oxide fuel cell (SOFC) system, improve the waste heat utilization, and reduce the cold energy consumed during CO₂ capture. In the CO₂ chemical absorption process, the methyldiethanolamine (MDEA) aqueous solution is utilized as a solvent, and feed temperature and absorber pressure are optimized via Aspen Plus software. The single‐objective and multiobjective optimization are carried out for the flash‐binary cycle subsystem. Results show that when the multiobjective optimization is applied to identify the exergoeconomic condition, the cogeneration system can simultaneously satisfy the high thermodynamic cycle efficiency and also the low product unit cost. The optimal results of the exergy efficiency, product unit cost, and normalized CO₂ emissions obtained by Pareto chart were 75.84%, 3.248 $/GJ, and 13.14 kg/MWhr, respectively.     

Multi-objective optimization of HVAC system with an evolutionary computation algorithm

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.     

燃气热电联产系统的节能分析

热电联产是一种热能．电能同时生产的能源利用形式。它将高品位的热能用于发电,低品位的热能用于集中供热,既提高能源的利用效率,又减少了环境污染。燃气热电联产（CHP）系统则是利用燃气锅炉和供热汽轮机的热电联产供应系统。从能量的有效利用方面对CHP系统的各个环节进行了分析计算,并结合实例,将它与燃气热电分产系统进行比较。结果表明,CHP系统的可用能效率高,节能．经济,同时还可以减少环境污染。     

Performance assessment and optimization of an integrated solid oxide fuel cell-gas turbine cogeneration system

The combined solid oxide fuel cells and gas turbine (SOFC/GT) system is known to be a potential alternative for distributed power generation. In this paper, a novel SOFC/GT based cogeneration system, which integrated a transcritical carbon dioxide cycle (TRCC) with a LNG cold energy utilization system is proposed. A mathematical (zero-dimensional) model is developed to analyze the co-generation system performance from the perspective of thermodynamic (energy and exergy) and economic costs. The main parameters of the system are chosen to analyze their effects on thermodynamic performance. The results show that the current system can achieve 64.40% thermal efficiency and 62.13% exergy efficiency under given conditions, and can further improve efficiency through parameter optimization. Finally, the multi-objective optimization program using NSGA-II (Non-dominated Sorting Genetic Algorithm II) is used to obtain the optimal value of the system design parameters. In the multi-objective analysis, the thermodynamic efficiency and economic cost of the system are considered as objective functions. The optimization results show that the final optimized design selected from the Pareto front can achieve 63.08% thermal efficiency and 61.10% exergy efficiency, respectively.     

Thermodynamic and thermoeconomic optimization of a cooling tower-assisted ground source heat pump

Thermodynamic and thermoeconomic optimization of a cooling tower-assisted ground source heat pump (GSHP) in a multi-objective optimization process is performed. A thermodynamic model based on energy and exergy analyses is presented, and an economic model of the hybrid GSHP (HGSHP) system is developed according to the total revenue requirement (TRR) method. The proposed hybrid cooling tower-assisted GSHP system, including 12 decision variables, is considered for optimization. Three optimization scenarios, including thermodynamic single objective, thermoeconomic single objective, and multi-objective optimizations, are performed. In multi-objective optimization, both thermodynamic and thermoeconomic objectives are simultaneously considered. An optimization process is performed using the genetic algorithm (GA). In the case of multi-objective optimization, an example of a decision-making process for selection of the final solution from the Pareto optimal frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, the annual number of operating hours in cooling mode, the electricity price, and the water price are studied in detail. It is shown that the thermodynamic optimization is focused on provision for the limited source of energy, whereas the thermoeconomic optimization only focuses on monetary resources. In contrast, the multi-objective optimization considers both energy and monetary. Further, it is found that thermodynamic optimization is economical when the operating time in cooling mode is long and/or the electricity price is high, and water prices variations have no marked impact on the total product cost.     

Experimental study of a domestic thermoelectric cogeneration system

Thermoelectric application for power generation does not appear to be appealing due to the low conversion efficiency given by the current commercially available thermoelectric module. This drawback inhibits its wide application because of the overall low thermal efficiency delivered by typical thermoelectric applications. This paper presents an innovative domestic thermoelectric cogeneration system (TCS) which overcomes this barrier by using available heat sources in domestic environment to generate electricity and produce preheated water for home use. This system design integrates the thermoelectric cogeneration to the existing domestic boiler using a thermal cycle and enables the system to utilise the unconverted heat, which represents over 95% of the total absorbed heat, to preheat feed water for domestic boiler. The experimental study, based on a model scale prototype which consists of oriented designs of heat exchangers and system construction configurations. An introduction to the design and performance of heat exchangers has been given. A theoretical modelling for analysing the system performance has been established for a good understanding of the system performance at both the practical and theoretical level. Insight has also been shed onto the measurements of the parameters that characterise the system performance under steady heat input. Finally, the system performance including electric performance, thermal energy performance, hydraulic performance and dynamic thermal response are introduced.     

Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger

Thermodynamic and thermoeconomic optimization of a horizontal geothermal air conditioning system has been performed. A model based on energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. An artificial intelligence technique known as evolutionary algorithm has been utilized for optimization. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective optimization, thermoeconomic single objective optimization and multi‐objective optimization (with simultaneous optimization of thermodynamic and thermoeconomic objectives) are performed. In multi‐objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. In the case of multi‐objective optimization, an example of decision‐making process for selection of the final solution from available optimal points on Pareto front is presented here. The results obtained using the various optimization approaches are compared and discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Multi-objective optimization of rotary regenerator using genetic algorithm

The rotary regenerator (heat wheel) is an important heat recovery equipment, which rotates between two cold and hot streams. The pressure drop and effectiveness of rotary regenerator are important parameters in optimal design of this equipment for industrial applications. For optimal design of such a system, it was thermally modeled using -NTU method to estimate its pressure drop and effectiveness. Frontal area, ratio of hot to cold frontal heat transfer area, matrix thickness, matrix rotational speed, matrix rod diameter and porosity were considered as design parameters. Then fast and elitist non-dominated sorting genetic algorithm (NSGA-II) method was applied to find the optimum values of design parameters. In the presented optimal design approach, the effectiveness and the total pressure drop are two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto optimal solutions’. The sensitivity analysis of change in optimum effectiveness and pressure drop with change in design parameters of the regenerator was also performed and the results are reported.     

Evaluation of the performance of a micro gas turbine cogeneration system in a sewage treatment facility: Optimized configuration of a cogeneration system

In this study, efficient configuration of a biogas‐fuelled cogeneration system (CGS) in a sewage treatment facility was investigated. The efficient configuration of the CGS was clarified on the basis of the relationship between exhaust heat recovery efficiency (η_ehr) of the CGS and the ratio of yearly average heat demand to yearly average biogas production of the facility (Q_h.d/Q_b.p). The CGS was assumed to be used under Q_h.d/Q_b.p<η_ehr,Q_h.d/Q_b.p≈η_ehr, and Q_h.d/Q_b.p>η_ehr conditions. It was found that although the CGS was able to cover total heat demand of the facility by only consuming biogas produced, from the point of view of energy utilization, reduction of unutilized biogas and reduction of electricity demand efficiencies, the most efficient CGS was obtained under the Q_h.d/Q_b.p≈η_ehr condition. Under the Q_h.d/Q_b.p≈η_ehr condition, energy utilization, reduction of unutilized biogas, and reduction of electrical demand efficiencies were 0.64, 0.99, and 0.32, respectively, whereas under the Q_h.d/Q_b.p<η_ehr and Q_h.d/Q_b.p>η_ehr conditions, energy utilization, reduction of unutilized biogas, and reduction of electrical demand efficiencies were in ranges of 0.56–0.64, 0.43–0.99, and 0.16–0.20, respectively. A more efficient system can be obtained if a CGS with lower η_ehr such as a fuel cell is used under the Q_h.d/Q_b.p<η_ehr condition and if a CGS with higher η_ehr such as a steam turbine is used under the Q_h.d/Q_b.p>η_ehr condition. © 2011 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.20389     

Cost structure of CGAM cogeneration system

The cost structure of the CGAM system, a predefined cogeneration system suggested to unify the different methodologies of thermoeconomic analysis, was investigated by using a thermoeconomic method called modified productive structure analysis (MOPSA). An emphasis has been specially put on how the cost structure of the system is affected by the chosen level of aggregation that specifies the subsystems. It has been found that the unit cost of products is dependent on the chosen level of aggregation of the system only when one considers the entropy flow as one of the parameters to determine the unit cost of products. Copyright © 2004 John Wiley & Sons, Ltd.     

Multi-objective numerical optimization of the front blade pitch angle distribution in a counter-rotating type horizontal-axis tidal turbine

In order to exploit renewable energies from tidal stream, tandem propellers of a unique counter-rotating type horizontal-axis tidal turbine was firstly designed based on the blade element momentum (BEM) theory. And then a multi-objective numerical optimization method coupled the response surface method (RSM) with the genetic algorithm (GA) was employed to obtain desirable blade profiles. The front blade pitch angle distribution was taken as optimization variable in this paper, as it plays an important role in affecting the inlet conditions of the rear blade. The numerical results show that both optimization objectives of power coefficient and thrust coefficient can be significantly improved. It was verified that the performance of the power unit with the optimized blades increases obviously by optimizing the pitch angle.     

Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling

As the development of China's economy, environmental problems in China become more and more serious. Solar energy and wind energy are considered as ones of the best choices to solve the environmental problems in China and the hybrid wind/solar distributed generation (DG) system has received increasing attention recently. However, the instability and intermittency of the wind and solar energy throw a huge challenge on designing of the hybrid system. In order to ensure the continuous and stable power supply, optimal unit sizing of the hybrid wind/solar DG system should be taken into consideration in the design of the hybrid system. This paper establishes a multi-objective optimization framework based on cost, electricity efficiency and energy supply reliability models of the hybrid DG system, which is composed of wind, solar and fuel cell generation systems. Detailed models of each unit for the hybrid wind/solar/fuel cell system were established. Advanced ε-constraints method based on Hammersley Sequence Sampling was employed in the multi-objective optimization of the hybrid DG system. The approximate Pareto surface of the multi-objective optimization problems with a range of possible design solutions and a logical procedure for searching the global optimum solution for decision makers were presented. In this way, this work provided an efficient method for decision makers in the design of the hybrid wind/solar/fuel cell system.     

Multi-objective topology optimization of end plates of proton exchange membrane fuel cell stacks

End plates of the proton exchange membrane fuel cell (PEMFC) need to be well designed because their strength and rigidity directly affect the clamping pressure distribution and thus affect the performance and lifetime of fuel cell stacks. In this paper, a multi-objective topology optimization model of the end plates in a PEMFC stack with nonlinear contact boundary conditions was established to obtain an optimized structural design. It was found that the design improved with topology optimization is not only light but also meets manufacturability requirements. This provides good guidance for the design of a high-performance end plate.     

An approach to configure optimal cogeneration system between existing steam headers of an industry

Traditionally, steam‐based cogeneration systems have been added to industrial applications as topping cycle or bottoming cycle configurations. Such systems frequently require that pressure or steam flow handling capacity of one or more existing steam headers be enhanced. The aim of this work is to suggest a new type of cogeneration system configuration (referred to as the closed‐loop configuration) that essentially avoids any such requirement of capacity enhancement. Considerable difference in enthalpy between existing steam headers is a feature commonly observed in large industrial setups. Transfer of steam between two such headers can be configured so as to cogenerate significant quantities of electric power, that may either be used in‐plant or may be sold to local utilities at an appropriate price. This paper presents a design approach to optimally configure such cogeneration systems. Case studies illustrate application of the design approach to benchmark oil refinery plants. Copyright © 1999 John Wiley & Sons, Ltd.