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To reduce energy consumption, a model was developed to optimize a multi-generation system. The system integrates multi-effect distillation through thermal vapor compression (MED-TVC), solar flat plate collectors (FPCs), and photovoltaic panels (PVs). The objective function was to minimize the annual cost of the system. To achieve this, an evolutionary algorithm was employed to determine the optimal values of 34 design parameters. The design parameters considered for optimization included the capacity of the gas turbine as the prime mover, electrical chiller capacity, absorption chiller capacity, size of the boiler, partial loads of the gas turbine for each month of the year (12 values in total), number of FPCs and PVs, number of effects in the desalination unit, driving steam pressure, feed water flow rate, driving steam flow rate, and electric cooling ratio. A novel approach was introduced by incorporating a variable electrical cooling ratio, which represents the proportion of electrical and absorption chiller usage, for each month of the year. The optimization aimed to fulfill the heating, cooling, electricity, and freshwater requirements of a residential complex situated in Bandar Abbas, Hormozgan Province, Iran. The optimization results were compared with a constant electrical cooling ratio system where the electrical cooling ratio was assumed to be fixed throughout the year. The optimal scenario, considering the variable electrical cooling ratio strategy, yielded an annual cost of $0.9780 × 106 $/year, indicating a significant 10.11% improvement compared to the conventional case ($1.0882 × 106 $/year). These findings underscore the potential advantages of the proposed strategy in terms of cost savings and system performance optimization. [ABSTRACT FROM AUTHOR]

A hybrid energy system, comprising a diesel engine as the prime mover, electrical and absorption chillers, a backup boiler, and a multi‐effect distillation through thermal vapor compression (MED‐TVC) unit, has been utilized to meet the requirements of a residential complex. This study focuses on redesigning and optimizing the system to enhance environmental conditions, reduce pollutants, and minimize the use of fossil energy. The feasibility and design of renewable energy systems, including wind turbines (WTs), photovoltaic panels (PVs), and flat plate collectors (FPCs), have been examined. Genetic algorithm (GA) has been employed for optimization. The hybrid system employs 21 design variables, with 24 design variables chosen for optimization alongside renewable energies. The total annual cost (TAC), encompassing investment, operation, and pollution emission fines, has been chosen as the objective function for minimization. The results indicate that the use of WTs has not been cost‐effective, and solar energy can enhance the system's performance in Bandar Abbas, Hormozgan province in Iran. In the case of using a combined system, the objective function value was 2.0472 × 106 $/year, and when using renewable energies, the objective function became 1.6795 × 106 $/year. Thus, the proposed combined‐renewable system has reduced the objective function by 17.96%. [ABSTRACT FROM AUTHOR]

In this study, a cogeneration system of heating, cooling and power used with the solar flat plate collectors (FPCs) and photovoltaic panels (PVs) integrates with a multi‐effect distillation through thermal vapor compression (MEE‐TVC), investigated as the first system to meet the loads of heating, cooling, electricity and fresh water. As an innovation, the combination of the first system with cooling and thermal storage tanks have been investigated. The systems are optimized with MATLAB and single‐objective genetic algorithm. The total annual cost considered as the objective function to compare the two systems and 36 and 61 design variables are obtained for the first and second systems, respectively. The design parameters considered for optimization included the capacity of the diesel engine as the prime mover, electrical chiller capacity, absorption chiller capacity, size of the boiler, partial loads of the diesel engine for each hour of the 2 days (1 day for hot months and 1 day for cold months), partial loads of the chillers for each hour of the first days (second day has no cooling load), number of FPCs and PVs, number of effects in the desalination unit, driving steam pressure, feed water flow rate, driving steam flow rate, and electric cooling ratio. The optimization aimed to fulfill the heating, cooling, electricity, and fresh water requirements of a residential town situated in Bandar Abbas, Hormozgan province, Iran. The second system's optimization results were compared with first system. The results have shown that the objective function for the first system was 2.6549×106$/year$$ 2.6549\times {10}^6\ \$/\mathrm{year} $$ and the initial investment cost was 9.1845×106$$$ 9.1845\times {10}^6\ \$ $$, and for the second system the objective function was 2.3549×106$/year$$ 2.3549\times {10}^6\ \$/\mathrm{year} $$ and the initial investment cost was 10.8790×106$$$ 10.8790\times {10}^6\ \$ $$. Therefore, by using energy storage, the objective function was reduced by 11.30%. [ABSTRACT FROM AUTHOR]

Modeling and optimization of a multi tube heat exchanger (MTHE) network considering the effects of different nanoparticles on the tube side are carried out using Fast and elitist non-dominated sorting genetic algorithm. After thermal modeling in ε − NTU method, optimization is performed by increasing the effectiveness and decreasing total annual cost as two objective functions using eight design parameters such as number of MTHE and particles volumetric concentration. In addition, optimization is performed at three various cold mass flow rates and different nanoparticles including Al2O3, CuO and ZrO2 and results are compared with the base fluid (water). For the reliability of the present code, the modeling results are validated with the results obtained from both the numerical and experimental model. The results show that the optimal Pareto front is improved in nanoparticles case, and the rate of improvement in CuO nanoparticles case, especially in higher effectiveness and lower cold mass flow rate is more significant compared with the other studied cases. In addition, because of improvement in the thermal performance of MTHE network with nanoparticles, the heat transfer surface area and consequently the total volume of MTHE network for the fixed values of effectiveness are noticeably reduced. Finally, the effects of design parameters versus effectiveness are demonstrated and discussed. [ABSTRACT FROM AUTHOR]

There are many works on improving the performance of a cogeneration plant such as the implementation of a recuperator. In previous works, the authors modelled a gas turbine cycle considering the recuperator as a black box. In this paper, a cogeneration plant is modeled and optimized with details of recuperator parameters. For this purpose, 13 design variables for a plant as well as a recuperator are selected. Then, a genetic algorithm is applied to optimize exergy efficiency and total cost rate, simultaneously. This work included Energy, Economy, and Environmental factors which with Exergy provided 4E analysis. A 36% decrease in total cost and a 33% increase in exergy efficiency in comparison with a simple gas turbine system were found. The above results for a gas turbine with a preheater and inlet cooling system reveal a 36% decrease in total cost and 35% increases in exergy efficiency. In addition, the optimum recuperator design parameters reveal that, higher effectiveness is more important than the investment cost. Moreover, a plant with higher exergy efficiency needs a recuperator with a lower pressure drop. Finally sensitivity analysis for variation of objectives functions with a change in fuel cost and interest rate are performed. [ABSTRACT FROM AUTHOR]

Thermal modeling and optimal design of a combined cooling, heating, and power (CCHP) generation system are presented in this paper. A new procedure for simultaneous selection of the type (gas engine, diesel, gas turbine) and number of available prime movers (PMs) in a market, selecting PMs partial load, selecting the heating capacity of backup boiler as well as selecting the cooling capacity of electrical and absorption chillers available in the market are presented. A genetic algorithm (GA) with discrete and continuous decision variables is applied to select the equipment for the CCHP system by maximizing the actual annual benefit (AAB) as the objective function. The optimization problem is carried out for 1000 alternative states for electricity, cooling, and heating (E-Q-H) loads in the range of 500 kW to 5000 kW to investigate the effect of E-Q-H loads. Moreover, the optimization is performed at two SELL and NO-SELL modes. In the former case is the sale of the excess electricity to the network is allowed and in the latter one, it was not allowed to sell the excess electricity to the grid. A correlation in terms of E-Q-H loads is obtained to specify the effect of E-Q-H loads on optimum AAB values in SELL and NO-SELL modes. Using these correlations, designers can predict the maximum accessible AAB for any electricity, cooling, and heating loads in the above specified range. [ABSTRACT FROM AUTHOR]

A combined cooling, heating and power generation (CCHP) system is modeled and optimized. The heat demand in this plant can provide by prime mover, backup boiler, and solar panels. Both the genetic algorithm (GA) and particle swarm optimization (PSO) are used to find the maximum of actual annual benefit (AAB) as an objective function. The design parameters or decision variables are capacity of prime mover, their number as well as their partial load (PL), backup boiler and storage tank heating capacity, the number of solar panels, types of electrical and absorption chiller as well as the electric cooling ratio. Both genetic and PSO algorithms are converged with maximum 0.6% difference. As a result, a diesel engine with nominal power o f350 kW combined with 255 solar panels is selected in the optimum situation. In addition, the optimization results show that the advantage of absorption chiller than the electrical chiller due to the extra availability of heat by the prime mover at the warm season in residential area. Finally, the effect of electric cooling ratio, number of solar panels and solar panels investment cost on objective function are investigated and results are reported, [ABSTRACT FROM AUTHOR]

In the present study, a heat recovery steam generator (HRSG) with a typical geometry and a number of pressure levels used at combined cycle power plants (CCPPs) is modeled. In order to validate the model results, they are compared with data obtained from the actual running power plant located near the Caspian Sea in Iran. The results show a good agreement between the model results and the experimental data. Upon a comprehensive exergy analysis conducted for this HRSG, the results show that an increase in the high and low drum pressures results in an increase in the HRSG exergy efficiency, while an increase in the pinch temperature leads to a decrease in the HRSG exergy efficiency. Also, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables is applied to obtain maximum exergy efficiency with minimum total annual cost per produced steam exergy as a two objective functions. The decision variables (or design parameters) are high and low drum pressures, steam mass flow rates, pinch point temperature differences, and the duct burner fuel consumption flow rate. The first objective function included capital or investment cost and operational cost and is minimized while satisfying a group of constraints, and HRSG exergy efficiency is maximized simultaneously. In addition, a regression analysis for curve fitting is conducted to correlate the data to determine the optimal points from the multi-objective optimization to predict the trend of each objective function. The results show that an increase in high pressure and low pressure drum pressure results in increasing HRSG exergy efficiency and also a smaller pinch temperature corresponding to a larger heat transfer surface area and more costly system, as well as higher exergy efficiency and lower operating cost. [ABSTRACT FROM AUTHOR]

• A new multi-generation system based on geothermal energy is developed. • An exergoeconomic optimization is implemented on the developed system. • Advanced exergy analysis is performed on the developed system at optimal point. • Exergy efficiency and total product unit cost of the developed system are 52.65% and 4.35 $/GJ. This study aims to develop a new multigeneration system based on geothermal energy and liquefied natural gas cold energy. The developed system includes dual fluid organic Rankine cycle and stirling engine to produce power, a desalination unit to produce purified water and a water heater to produce heating. Exergy and exergoeconomic analyses are carried out to determine the irreversibilities and the inefficient costs of each component of the developed process. The results show that the used heat exchanger (number 4), in liquefied natural gas regasification plant (26.72 $/h) and the used pump (number 3), in the desalination system (0.001 $/h), obtain the maximum and the minimum amount of exergy destruction cost. Moreover, multi-objective optimization is performed based on genetic algorithms. Achieved outcomes demonstrate that at the optimal point where exergy efficiency of the whole system as well as product cost rate are optimized, the corresponding values are 52.65% and 4.35 $/GJ, respectively. A sensitivity analysis is provided to assess the effects of design parameters on objective functions. Finally, advanced exergy analysis is performed on the most inefficient system components. Based on the avoidable endogenous part of irreversibility, heat exchangers (number 4) (843 kW), (number 3) (664 kW) and (number 1) (599 kW) should be modified first, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Thermal modeling and optimal design of an underfloor heating system are presented in this paper. Analytical modeling is coupled with some experimental data is used to obtain the rate of heat transfer and temperature distribution in the presented system. After thermal modeling, tube length, tube radius, water mass flow rate and number of panels are considered as four design parameters. Both the Genetic Algorithm and the Particle Swarm Optimization Algorithm are applied to obtain the minimum total annual cost (sum of investment and operational costs) as objective function with discrete and continues variables. Both algorithms are converged with 0.2% differences. The optimization results show that the underfloor heating optimum configuration has 13 panels, each of them with 80m length, 7mm tube inside radius where water passes through the tubes with the rate of 0.503kg/s. The sensitivity analysis of change in the optimum total annual cost, rate of heat transfer and panel temperature with change in design parameters of the underfloor heating are also performed. The results show that by increase of design parameters, both the total annual cost and the rate of heat transfer increase. In addition, the tube length and water mass flow rate were found as the most important parameters in the design and optimization of underfloor heating system. Finally, a closed form equation is presented between the rate of heat transfer and five important variables with acceptable precision by using Artificial Neural Network. [Copyright &y& Elsevier]

This paper comprehensively investigates performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) method are implemented for finding the optimal values for seven key design variables of the STHX model. ∊ -NTU method and Bell-Delaware procedure are used for thermal modeling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA and CS always lead to maximum STHX efficiency. Also computational requirements of CS method are significantly less than FA method. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behavior of the majority of decision variables remains consistent in different runs of the FA and CS optimization processes. [ABSTRACT FROM AUTHOR]