Multi-objective design optimization of a natural gas-combined cycle with carbon dioxide capture in a life cycle perspective

The use of multi-objective optimization techniques is attractive to incorporate environmental objectives into the design of energy conversion systems. A method to locally optimize a given process while considering its global environmental impact by using life cycle assessment (LCA) to account for avoidable and unavoidable off-site emissions for each independent material input is presented. It is applied to study the integration of a CO₂-capture process using monoethanolamine in a natural gas-combined cycle power plant, simultaneously optimizing column dimensions, heat exchange, and absorbent flow configuration with respect to two objectives: the levelized cost of electricity and its life cycle global-warming potential. The model combines a process flow-sheeting model and a separate process-integration model. After optimization using an evolutionary algorithm, the results showed that widening the absorber and generating near-atmospheric pressure steam are cost-effective options but that increasing stripper complexity is less so. With $7.80/GJ natural gas and $20/ton CO₂ handling, the minimum on-site CO₂ abatement cost reaches $62.43/ton on a life cycle basis, achieved with a capture rate of over 90%. Of this, $2.13/ton is related to off-site emissions – a specific advantage of LCA that could help industries and governments anticipate the actual future costs of CO₂ capture.     

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.     

Design and optimization of a non-TEMA type tubular recuperative heat exchanger used in a regenerative gas turbine cycle

A special non-TEMA type tubular recuperative heat exchanger used as a regenerator of a gas turbine cycle is considered for multi-criteria optimization. It is assumed that the recuperator is designed for an existing gas turbine cycle to be retrofitted. Three scenarios for optimization of the proposed system have been considered. In one scenario, the objective is minimizing the cost of recuperator; while in another scenario maximizing the cycle exergetic efficiency is considered. In third scenario, both objectives are optimized simultaneously in a multi-objective optimization approach. Geometric specification of the recuperator including tubes length, tubes outside/inside diameters, tube pitch in the tube bundle, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles are considered as decision variables. Combination of these objectives and decision variables with suitable engineering and physical constraints (including NO_x and CO emission limitations) makes a set of MINLP optimization problem. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms namely NSGA-II. This approach which is based on the Genetic Algorithm is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained and a final optimal solution is selected in a decision-making process. It is shown that the multi-objective optimization scenario can be considered as a generalized optimization approach in which balances between economical viewpoints of both heat exchanger manufacturer and end user of recuperator.     

Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models

The definition of an efficient optimization methodology for internal combustion engine design using 1D fluid dynamic simulation models is presented. This work aims at discussing the fundamental numerical and fluid dynamic aspects which can lead to the definition of a best practice technique, depending on the complexity of the problem to be dealt with, on the number of design parameters, objective variables and constrains. For these reasons, both single-and multi-objective problems will be addressed, where the former are still of relevant interest (i.e. optimization of engine performances), while the latter have a much wider range of applications and are often characterized by conflicting objectives.     

Multi-objective optimization of a solid oxide fuel cell-based integrated system to select the optimal closed thermodynamic cycle and heat coupling scheme simultaneously

Integrating thermodynamic cycles with SOFCs is an effective technology to make full use of energy, but the difficulty lies in selecting the optimum thermodynamic cycles and integration scheme. The performance of a thermodynamic cycle is directly determined by the working fluid and operating parameters, and the heat exchange network (HEN) synthesis scheme directly affects the heat coupling degree and the economic performance. Therefore, a multi-objective optimization model is proposed in this paper, where the working fluid and operating parameters of the closed cycle, as well as the HEN synthesis scheme are selected as decision variables, while the total exergy destruction (TED) and the total annual cost (TAC) are adopted as the objectives. To solve this complex problem, a solving strategy based on NSGA-II with two levels is proposed to obtain the Pareto Frontier of TED and TAC. Finally, a case is solved and three Pareto optimality are analyzed and compared.     

Multi-objective optimization of an advanced combined cycle power plant including CO2 separation options

This paper illustrates a methodology developed to facilitate the analysis of complex systems characterized by a large number of technical, economical and environmental parameters. Thermo-economic modeling of a natural gas combined cycle including CO₂ separation options has been coupled within a multi-objective evolutionary algorithm to characterize the economic and environmental performances of such complex systems within various contexts.

The method has been applied to a case of power generation in Germany. The optimum options for system integration under different boundary conditions are revealed by the Pareto Optimal Frontiers. Results show the influence of the configuration and technical parameters on the electrical efficiencies of the Pareto optimal plants and their sub-systems. The results provide information on the relationship between power generation cost and CO₂ emissions, and allow sensitivity analyses of important economical parameters like natural gas and electricity prices. Such a tool is of interest for power generation technology suppliers, for utility owners or for project investors, and for policy makers in the context of CO₂ mitigation schemes including emission trading.     

Multi-objective optimization of a hydrogen production through the HyS process powered by solar energy in different scenarios

Thermochemical or hybrid cycles powered by concentrated solar energy are a very promising way to produce an effective clean hydrogen through the water splitting, in terms of greenhouse gas (GHG) emissions and power production sustainability. SOL2HY2 is an European project focused on this goal. It deepens the so-called HyS process in a closed or partially open version using a proper SO₂ depolarized electrolyser, and moreover, it investigates key materials and process solutions, along the entire production chain. However, the identification of the best solution to obtain a suitable hydrogen in terms of cost, efficiency, availability of energy and material, sharing of renewable energy source, continuity of operation in different locations and plant sizes, poses many challenges in terms of flexibility and complexity of the system. In fact, it involves various chemical equipment, different solar and thermal storage technologies, and variable operative conditions with different reaction temperatures and mixture concentrations. Hence it arises the importance to have a tool for the investigation of this system.In this paper, data analysis and multi-objective techniques are used to study and optimize the process under consideration. Several mathematical methods have been exploited to make the best use of the available data, such as Design of Experiments techniques, meta-modeling strategies and genetic algorithms. All these methods have been implemented in the open source environments Scilab and R.     

Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output,thermal efficiency and ecological performance

Solar-dish Brayton system driven by the hybrid of fossil fuel and solar energy is characterized by continuously stable operation, simplified hybridization, low system costs and high thermal efficiency. In order to enable the system to operate with its highest capabilities, a thermodynamic multi-objective optimization was performed in this study based on maximum power output, thermal efficiency and ecological performance. A thermodynamic model was developed to obtain the dimensionless power output, thermal efficiency and ecological performance, in which the imperfect performance of parabolic dish solar collector, the external irreversibility of Brayton heat engine and the conductive thermal bridging loss were considered. The combination of NSGA-II algorithm and decision makings was used to realize multi-objective optimization, where the temperatures of absorber, cooling water and working fluid, the effectiveness of hot-side heat exchanger, cold-side heat exchanger and regenerator were considered as optimization variables. Using the decision makings of Shannon Entropy, LINMAP and TOPSIS, the final optimal solutions were chosen from the Pareto frontier obtained by NSGA-II. By comparing the deviation index of each final optimal solution from the ideal solution, it is shown that the multi-objective optimization can lead to a more desirable design compared to the single-objective optimizations, and the final optimal solution selected by TOPSIS decision making presents superior performance. Moreover, the fitted curve between the optimal power output, thermal efficiency and ecological performance derived from Pareto frontier is obtained for better insight into the optimal design of the system. The sensitivity analysis shows that the optimal system performance is strongly dependent on the temperatures of absorber, cooling water and working fluid, and the effectiveness of regenerator. The results of this work offer benefits for related theoretic research and basis for solar energy industry.     

液化天然气及其车用技术

本文对液化天然气LNG的性质和特点进行了全面的分析阐明LNG是天然气有效的应用形式介绍了国内外车用LNG及LNG汽车的发展状况同时对LNG加LNG车用技术分别作了较详细论述     

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 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.     

Multi-objective optimized operation of integrated energy system with hydrogen storage

In this paper, an integrated energy system (IES) consisting of wind turbine unit, photovoltaic cell unit, electrolytic hydrogen unit, fuel cell unit, and hydrogen storage unit is proposed, and the construction of multi objectives for day-ahead power dispatching of the IES considering both operation and environment cost is discussed. By adopting piecewise linearization method, the optimization variables are divided into 24 periods, and the day-ahead power dispatching optimization problem is transformed into a 24-h piecewise optimization problem. On the basis, a complete non-linear mixed integer dynamic scheduling optimization model is established. An improved non-dominated sorting genetic algorithm (NSGA-II) is applied to solving the model. In optimization process, an interactive strategy is adopted to solve the coordination between discretization of variables and restriction of switching times of electrolyzer. Optimization results show that, compared with the single objective of minimizing operating costs, the multi-objective optimization scheme can reduce carbon emissions by 3.5% with 2.8% increase of operating cost. Compared with the single objective of minimizing environmental, the multi-objective optimization scheme can reduce operating cost carbon by 5.12% with 2.6% increase of environmental cost.     

Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied.     

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 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.     

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.     

Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (E_excess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.     

Performance assessment and optimization of a biomass-based solid oxide fuel cell and micro gas turbine system integrated with an organic Rankine cycle

Rice straw is a potential energy source for power generation. Here, a biomass-based combined heat and power plant integrating a downdraft gasifier, a solid oxide fuel cell, a micro gas turbine and an organic Rankine cycle is investigated. Energy, exergy, and economic analyses and multi-objective optimization of the proposed system are performed. A parametric analysis is carried out to understand the effects on system performance and cost of varying key parameters: current density, fuel utilization factor, operating pressure, pinch point temperature, recuperator effectiveness and compressors isentropic efficiency. The results show that current density plays the most important role in achieving a tradeoff between system exergy efficiency and cost rate. Also, it is observed that the highest exergy destruction occurs in the gasifier, so improving the performance of this component can considerably reduce the system irreversibility. At the optimum point, the system generates 329 kW of electricity and 56 kW of heating with an exergy efficiency of 35.1% and a cost rate of 10.2 $/h. The capability of this system for using Iran rice straw produced in one year is evaluated as a case study, and it is shown that the proposed system can generate 6660 GWh electrical energy and 1140 GWh thermal energy.     

缸内直喷液化天然气发动机喷嘴布置方案研究

应用商用STAR-CD软件,建立了喷嘴-气缸的三维模型,模拟不同喷嘴布置方案下缸内燃气分布情况,结果显示:采用喷嘴竖直布置方案,会造成燃气分布不均,发动机性能恶化;采用喷嘴倾斜布置方案,可以使缸内燃气分布均匀,实现稀薄分层燃烧。