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• Study of PV/Biomass/Battery system for a remote village in Bangladesh. • Probabilistic modeling to address the uncertainties of resources and load demand. • Optimization with three different algorithms for effective global minima finding. • Performance superiority of Dandelion Optimizer (DO). • Obtained results provide technical and economic margins for stakeholders. Hybrid Renewable Energy System (HRES) has become a popular alternative for locations restricted to national grid connection due to geographical limitations. The study investigates the available renewable resources of a remote village in Mymensingh district of Bangladesh to propose and evaluate optimal sizing and cost of a grid independent HRES. The intermittency of solar irradiance and uncertain variation in load demand are taken into account by adopting probabilistic scenario-based analysis (SBA). Beta distribution and Gaussian distribution are considered for solar irradiance and load uncertainties, respectively, and Roulette Wheel mechanism generates multiple probabilistic scenarios. Objective function is formulated to minimize the total system cost (TSC) under defined constraints. Dandelion optimizer (DO), a relatively recent and unexplored metaheuristic algorithm along with two other popular optimization algorithms, Slime Mould Algorithm (SMA) and Real Coded Genetic Algorithm (RCGA) are applied to size the components for different probabilistic scenarios generated by Roulette Wheel. DO outperformed SMA and RCGA providing the solution set with 18.4 % and 3.2 % reduction in simulation time and system cost, respectively, in comparison with RCGA, whereas, 11.3 % and 16.7 % reduction in simulation time and system cost, respectively, in contrast to SMA. DO's computation of cost margin (1.17 million dollars to 1.46 million dollars), PV allocation (minimum 7 modules to maximum 218 modules), biomass power allocation (maximum of 70.34215 kW) and optimized battery allocation computed by the algorithms (24 units) provide authorities a margin of cost-efficient estimation with conservative preparedness for developing HRES in the selected region. The study also provides a scientific framework to account for the uncertain parameters while proposing and analyzing HRES for off-grid localities, where inadequate literary exploration is evident in the context of Bangladesh. [ABSTRACT FROM AUTHOR]

• The gradient distributed Tesla cold plate is proposed. • Hydro-thermal performance is optimized using kinging model and NSGA-II. • Mass flow rate poses a most significance on energy efficiency. • A maximum reduction of 75.7% for pressure drop is obtained. • Optimal case achieves module maximum temperature difference of 3.6 °C. To address energy-efficient cooling of battery module at a high discharge rate, this work presents a novel gradient distributed Tesla cold plate. Multi-objective optimization is performed to achieve optimal thermal performance and energy efficiency using coupled battery-cold plate simulations, kriging surrogate model and second non-dominated sorting genetic algorithm. Both cold plate structure and operating parameters are considered with objectives of maximum temperature, temperature uniformity and pressure drop. The results demonstrate that the mass flow rate poses a most significance on hydro-thermal performance, followed by channel depth and Tesla valve distance. A conflicting demand for the mass flow rate and channel depth is observed to achieve best hydro-thermal performance. In accordance with Pareto frontier solution, the improvement of pressure drop is most significant with a maximum reduction of 75.7% compared to base case. A moderate mass flow rate with increasing channel depth is recommended as the optimal strategy, yielding moderate liquid convection percentage (51.8%), cooling efficiency (2 3 3) and the highest Nu (6.7). Moreover, present gradient distribution of fractal inlet and Tesla unit contributes to high heat transfer coefficient zones along flow direction, achieving favorable temperature uniformity with maximum temperature difference of module middle section lower than 4 °C. [ABSTRACT FROM AUTHOR]

Partial Shading Condition (PSC) is a common issue when implementing the Photovoltaic (PV) system. Conventional Maximum Power Point Tracking (MPPT) algorithms are not able to track the Global Maximum Power Point (GMPP) under PSC as the Power–Voltage (P–V) characteristic curve has multiple Local Maximum Power Points (LMPPs). In the literature, meta-heuristic algorithms have been implemented in the Global Maximum Power Point Tracking (GMPPT) applications. However, meta-heuristic algorithms often face initialization and parameter tuning issues. In this paper, a novel evolutionary-based method called Artificial Location Selection Optimization (ALSO) is proposed for GMMPT under PSC. The proposed ALSO replicates the artificial selection activity widely used in agriculture. Compared to the conventional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), it does not involve random numbers so that the PSC issue can be solved effectively. In addition, our proposed algorithm is able to vary its tracking speed according to the complexity of the PSC. The performance of the ALSO has been tested by simulations and experiments by exploiting 252 non-repeating PSC test cases. Both simulations and experiments demonstrate that the ALSO algorithm performs better than the PSO and GA algorithms in terms of speed, efficiency, accuracy, power convergence, and duty convergence. • A novel maximum power point tracking algorithm called ALSO is proposed. • ALSO adeptly adjusts minimal tracking speeds to match PSC complexity. • ALSO outperforms PSO and GA in terms of accuracy, efficiency, and speed. • Particle powers and positions converge with increased accuracy and efficiency. [ABSTRACT FROM AUTHOR]

• Novel daytime radiative-cooled solar-driven ejector refrigeration system is proposed. • System performance is investigated based on energy, exergy and economic perspectives. • System is optimised by genetic algorithm and impact of operating variables is explored. • Effects of using various refrigerants and Indian seasonal climate change are evaluated. • Proposed device offers a sustainable cooling technology to mitigate heat island effect. Solar-driven ejector refrigeration is an emerging technology, but not sustainable due to huge heat rejection to the environment, leading to the heat island effect. However, this can be made sustainable by utilizing a daytime radiative condenser, but not yet been explored. Hence, a novel solar-driven ejector refrigeration system assisted with a daytime radiative condenser is investigated based on energy, exergy and economic perspectives. This system is powered by a parabolic trough collector along with thermal energy storage and double two-phase ejectors. The system performance is optimized by employing the genetic algorithm. The impact of several operating scenarios and seasonal climatic conditions is explored for different refrigerants. The best optimum coefficient of performance and exergy efficiency are obtained with the R123, but the total cost and radiative condenser area are lowest with the R1234ze(E). The performance is higher with lower ambient and radiative condenser temperatures but with higher solar irradiation for all three refrigerants. The performance is maximum during December for Jaisalmer and January for Varanasi and Kolkata, while lowest in May for Jaisalmer and June for Varanasi and Kolkata. The specific total cost and radiative condenser area are maximum from July to August for all considered locations. This study reveals the proposed system as a promising option, but the large space requirement may be the main hurdle behind the practical implementation. [ABSTRACT FROM AUTHOR]

• • Two novel multi-pressure evaporation Brayton combined cycles are proposed. • • Detailed modeling is conducted based on a one-dimensional radial turbine. • • Machine learning is employed to streamline the cycle simulations and improve speed. • • Genetic algorithm is utilized for multi-objective cycle optimization. • • Detailed analysis of the proposed cycles, based on thermal and exergy, is conducted. The Supercritical Carbon Dioxide Brayton combined cycle is one of the most important ways to enhance the performance of the cycle. Since the considerable exergy loss resulting from large temperature differences in the heat exchangers of combined cycles, the multi-pressure evaporation concept was adopted from Organic Rankine cycle research. Two novel multi-pressure evaporation configurations, namely parallel evaporation Brayton combined cycle and series evaporation Brayton combined cycle, are designed with the purpose of enhancing SCO 2 Brayton cycle performance. The performance of the two new cycles was compared with that of the baseline cycle. Moreover, Printed Circuit Heat Exchangers were employed to improve the overall compactness of the cycles. The results from model calculations were utilized to train an Artificial intelligence neural network model using machine learning techniques which allowed to simplify the cycle and improves the computational speed. Subsequently, genetic algorithm was employed to conduct multi-objective optimization on the three cycles. Finally, a detailed exploration was conducted from the perspective of thermal and exergy to elucidate the reasons for the advantages and differences between the two new cycles compared to the baseline cycle. The results indicate that the series Brayton combined cycle exhibits the highest performance, and the combined cycle of multi-pressure evaporation helps to improve the performance of the combined cycle. Compared to the baseline cycle, the combined cycle efficiency of both series and parallel combined cycles has increased from 45.54% to 46.18% and 46.29%, respectively, while the bottom cycle efficiency has improved from 3.81% to 4.93% and 5.03%. The ratio of heat exchange area to net output power increased from 0.6051 to 0.6614 and 0.6740 for the parallel and series cycles, respectively. Thermal analysis shows that, the fundamental purpose of changing the layout is to improve the specific power per unit of working medium in the cycle. Exergy analysis shows that, the two new combined cycles improve cycle efficiency by reducing exergy losses in the intermediate heat exchanger. Compared to the parallel version, the series version's staged compression allows for a lower temperature difference in intermediate heat exchanger 1, resulting in smaller exergy losses and higher cycle efficiency. This work will contribute to an enhanced understanding of Supercritical Carbon Dioxide combined cycles within the academic community and the introduction of multi-pressure evaporation will further improve the performance of these important cycles. [ABSTRACT FROM AUTHOR]

• Combined cycle capable of offering three switchable energy supply modes was studied. • The set of the evaporator ensures the effective switching of different cycle modes. • Heat source in low temperature grade helps to desorb the work or basic solution. • Analysis was conducted from both thermodynamic and economic perspectives. • All cycle modes can maintain high comprehensive performance. The emergence of energy-related issues and the growing need for year-round energy supply has created a pressing demand for efficient cogeneration combined cycles. To this end, a new combined cycle using ammonia-water as the working fluid is proposed and studied from both thermodynamic and economic standpoints in this paper. The proposed cycle can effectively operate in power generation, power/refrigeration cogeneration and power/heating cogeneration modes. Parametric analysis and performance optimization of the proposed cycle are carried out, and the genetic algorithm method is employed for the optimization. Under the same initial conditions, the proposed cycle exhibits comprehensive power recovery efficiencies that are 58.11 %, 23.85 %, and 7 % relatively higher than those of the compared cogeneration cycles and Kalina cycle reported in the literature, respectively. The optimization results show that the maximum comprehensive power recovery efficiencies for the power/refrigeration, power/heating and power modes with heat source/cooling water temperature of 250℃/30℃, are 0.2271, 0.2658, and 0.1789, respectively. The associated power/refrigeration, power/heating, and power outputs are 221.1 kW/417.8 kW, 248.5 kW/650.9 kW, and 292.1 kW, respectively. Under these conditions, the overall annual unit cost of comprehensive power and the payback period of the cycle are 0.03947 $/kWh and 3.898 years. Results of the comprehensive analysis indicate that the proposed cycle can achieve satisfactory comprehensive performance for each mode, while also sufficiently recovering sensible heat source. [ABSTRACT FROM AUTHOR]

• An enhanced thermal network model with added thermal resistances and capacities. • Thermal resistances and heat capacities are determined via genetic algorithm. • The impact of added resistances and capacities on thermal response is discussed. • Temperature prediction error is reduced from 4.24 to 0.95 ℃. Battery thermal management is crucial for preventing the safety issues of lithium-ion batteries. Due to the simple modeling and fast calculation speed, the thermal resistance-capacity (RC) network model is broadly applied in the design of battery thermal management systems. However, the simplification of heat flow paths and the lumped definition of thermal capacity in traditional RC models result in large temperature prediction errors, which fail to reflect the thermal response in complex and diverse heat transfer situations. To improve the prediction accuracy, a decentralized centroid multi-path RC network model is constructed for a typical prismatic lithium-ion battery. This novel model incorporates multiple heat flow paths with additional thermal resistances and legitimately decentralizes the lumped heat capacity to other surface center points, resulting in a more realistic thermal response. A genetic algorithm is employed to determine the unknown thermal resistances and heat capacities at the attributed nodes. Results show that compared to the traditional RC network model, the multi-path decentralized RC network model can reduce the temperature prediction error from 4.24 to 0.95 ℃. This more refined modeling approach extends the application scope of thermal resistance network models to more complex scenarios while maintaining efficient simulation speed, which enables the attainment of more accurate and reliable onboard temperature predictions for lithium-ion power systems. [ABSTRACT FROM AUTHOR]

• A novel solar-assisted vapor injection autocascade heat pump is proposed. • The effect of key operation parameters on the novel system performance is analyzed. • 4E performance of the novel system and other two systems are compared based on GA. • The novel system owns obvious 4E performance advantage over other two systems. Insufficient heating performance is one of the main obstacles that inhibit the widespread application of autocascade heat pump. Therefore, a novel solar-assisted vapor injection autocascade high-temperature heat pump is put forward, which owns two working modes to take full use of solar energy and air energy. At first, the performance variations of the novel system with key operating parameters are theoretically investigated to assess the system characteristics. Thereinto, under considered condition, SV mode is more appropriate to the novel system than VA mode under over 200 W m−2 solar radiation intensity. And then, the energy, exergy, economic and environmental performance of the novel system, vapor injection autocascade heat pump and convenient autocascade heat pump is conducted based on genetic algorithm. The novel system respectively increases the maximum heating COP and corresponding heating capacity by averages of 16.5 % and 18.1 %, in comparison with the vapor injection autocascade heat pump. The irreversibility distributions in three systems are also analyzed. Moreover, the novel system produces the lowest full life cycle cost and air pollutant emissions among three systems. Under given application scenario, the payback period is 2.06 years and 2.95 years when the novel system is adopted to replace other two systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel design optimization framework for supercritical radial turbomachinery is proposed. • The framework uses both sequential quadratic programming method and genetic algorithm to find the optimum turbomachinery geometry. • The framework capabilities are demonstrated for 100 kW, 200 kW and 250 kW energy systems. • The maximum total-to-static efficiencies are 86.99% for radial inflow turbine and 77.06% for centrifugal compressor. The application of supercritical CO 2 (sCO 2) in power generation systems is getting attractive recently owing to high energy efficiency and compact component size, but there are big design challenges for turbines and compressors due to the supercritical fluid characteristics. In this paper, a novel turbomachinery design optimization framework, "Turbine-Δh-Compressor" (TΔhC) tool, is proposed to generate the optimal flow feature and geometry. The TΔhC tool based on sequential quadratic programming formulation extends the design parameter search spaces and compares the optimized turbomachinery geometries by coupling with genetic algorithm, its capability is demonstrated by three sCO 2 systems with 100, 200 and 250 kW turbine output power under different operating conditions. It is found that the maximum turbine total-to-static efficiency of these systems is 86.99 % and the maximum compressor isentropic efficiency is 77.06 %. In the optimal design case, the system thermal efficiency could reach 33.43 %. [ABSTRACT FROM AUTHOR]

• Support vector regression guided genetic algorithm is presented. • The landowners' behavior of participation is considered in the model. • The proposed method can achieve higher energy conversion efficiency. • Representative irregular wind farms are studied, and the impacts are investigated. • The Python code will be made available online. Due to the existence of wake effect that causes the reduction of intake wind speed among wind turbines in the downwind direction, the wind farm efficiency is substantially discounted. An integral question to ask is how to find the optimal wind turbine layout given a wind farm. Inspired by the self-adjustment capability among individuals in the natural evolution process, a new algorithm called support vector regression guided genetic algorithm is proposed to solve the wind warm layout optimization problem which integrates the capability in each individual to adjust itself for a better "fitness" with guiding information sampled from a response surface approximated by support vector regression. It is also interesting to use the new proposed algorithm to evaluate the impact of the constraints imposed by landowners' willingness whether to rent their land to the wind farm company. Extensive numerical experiments under different settings of wind distribution and wind farms with unusable cells are conducted to validate the proposed algorithm, shedding insights on the impact of landowners' participation on the overall efficiency. The experiment showcases that the proposed algorithm outperforms two baseline algorithms under different conditions with improved efficiency. The proposed framework with consideration of landowners' participation decision provide insights for wind farm planner on the different values of farm lands from the landowners. [ABSTRACT FROM AUTHOR]

• Battery transport and logistics to increase renewable use and system flexibility. • Full train transport and carpooling to improve the economics of battery use. • Levelized cost of energy of battery transport & logistics is $0.045/kWh averagely. High share of variable renewable energy is challenging to the traditional power system technically and economically. This calls for a significant increase to the system flexibility, which might result in the costs associated with energy storage and costly upgrades to the traditional transmission and distribution system. This paper presents a multi-stage battery transportation and logistics optimization method to increase the renewable energy consumptions, economics, and mobilities of the battery utilization. A new approach is proposed in which the batteries are charged in the renewable power plants and transported back and forth by railways between the renewable power plants and cities. Based on the forecasts of battery supplies/demands, multiple optimization stages (full train transport and carpooling) are designed by the branch-and-bound algorithm and genetic algorithm respectively. The proposed battery transportation and logistics concept and model are performed using the Beijing-Tianjin-Hebei region in China as an example. The results show that the levelized cost of energy of the battery transportation and logistics model is $0.045/kWh averagely. Also, by the use of mobilized batteries, the proposed battery transportation and logistics model increases the system flexibilities and renewable energy deliveries to the end users without the reinforcement of transmission and distribution system and any constraint from a highly penetrated power system. [ABSTRACT FROM AUTHOR]

• An innovative energy efficient CO 2 pressurization and utilization system is proposed. • The proposed design involves a vapor compression cycle for CO 2 pressurization. • sCO 2 power cycle is employed to reduce the power requirement for CO 2 pressurization. • The integrated system results in 13.88% reduction of power consumption. An innovative CO 2 pressurization system combined with supercritical CO 2 (sCO 2) open power cycle is proposed in this study. The combined system reduced the power demand associated with CO 2 pressurization in the CO 2 capture and storage (CCS) process as well as utilized the captured CO 2 in a sCO 2 power cycle to generate power. As the first step, conventional multi-stage compression was complemented with CO 2 liquefaction and pumping to reduce the compression power. Later, a waste heat-powered recuperative sCO 2 power cycle was employed to generate additional electric power. The vapor compression cycle (VCC) was first modeled, validated, and explored for CO 2 liquefaction and pumping. Refrigerants R717, R134a, R290, and R32 were analyzed as the VCC working fluid. An initial thermodynamic analysis was performed to identify the most influential liquefaction parameters. Then, a genetic algorithm optimization module in MATLAB was used to minimize the overall power consumption in the VCC. The VCC was integrated with a sCO 2 cycle to utilize the high pressure CO 2 , and after optimizing the VCC, the performance of the sCO 2 cycle was evaluated. Results of our study revealed that integrating the sCO 2 cycle with a CO 2 liquefaction and pumping cycle reduced power consumption by 13.88% compared to conventional multi-stage compression. Finally, sensitivity analysis with respect to the crucial thermodynamic parameter was also performed. [ABSTRACT FROM AUTHOR]

• Exergy analysis for both double effect series and parallel configurations of ARS. • Effect of temperatures of all the components of ARS & distribution ratio was evaluated. • Study of crystallization & effect of various parameters on the crystallization limit. • Exergy efficiency of parallel configuration was 15% higher than series configuration. • Implementation of global optimization algorithm to ensure the global solution. World energy demand is continuously increasing due to population growth, extensive urbanization and rapid industrialization as growth in global energy demand will rise by 30% in 2040. This demand can be met by utilizing traditional and alternative energy resources and by enhancing energy efficiency. The energy efficiency of an energy intensive process may be improved by utilization of waste or low grade energy, design and operation optimization, and running the plant in flexible mode with improved control strategies. Waste heat of an industrial plant can be utilized by employing the so called absorption refrigeration systems (ARS). ARS is capable of utilizing the low quality energy or waste heat which otherwise may have been lost as compared to conventional compression refrigeration system (CRS) where huge quantity of electricity is required for compression of the refrigerant. One of the major concerns associated with ARS is its low efficiency. Efficiency improvement of ARS can be achieved by analyzing and optimizing the various configurations of ARS using any mathematical technique. In the present work, the aim is to analyze and maximize the performance of ARS using exergy efficiency instead of conventional coefficient of performance (COP) approach. Both the series and parallel flow configurations have been considered for the analysis and optimization. Thermodynamic model has been implemented in MATLAB® using the best available correlations for phase equilibria and thermodynamic properties. Operating conditions including temperatures of all the units of both configurations of ARS and distribution ratio for parallel configuration have been optimized. Genetic Algorithm (GA) was used to optimize the operating conditions. The effect of distribution ratio and effectiveness of heat exchanger on the maximum allowable temperature of low pressure generator (LPG) and high pressure generator (HPG) to avoid crystallization has also been evaluated. Exergy efficiency of parallel flow configuration came out to be 6.45% more than that of series flow configuration at nominal operating conditions. At optimal operating conditions, exergy efficiency improved by 11.6% and 20.81% for series and parallel flow configurations, respectively. Furthermore, exergy efficiency for parallel flow configuration is 15.1% higher than series flow configuration at optimal operating conditions. The effect of variation of effectiveness of solution heat exchangers on the optimization results has also been evaluated. Effect of weather conditions, type of energy source, application for solvent cooling and variable cooling load also needs to be considered in future. [ABSTRACT FROM AUTHOR]

Highlights • An advanced trigenerative micro compressed air energy storage system is proposed. • The mathematical model of the proposed system has been set up. • A multi-objective optimization by the aid of NSGA-Ⅱis employed. • The design parameters of the proposed system are determined. • A sensitivity analysis is performed to assess the proposed system performance. Abstract This paper proposes an advanced trigenerative micro compressed air energy storage (CAES) system, which acts as combined cooling, heating and power system by recovering cooling, heating and power energy during or after expansion. The proposed CAES system can be integrated with grid and placed close to users' side. Based on the users' demand, the proposed CAES system can produce different kinds of energy by adjusting the turbine inlet temperature in different seasons. The mathematical model of the proposed system including energy analysis and economic analysis has been set up. A multi-objective optimization by the aid of Non-dominated sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ) is employed to determine the design parameters of the proposed system considering the round trip efficiency and the annual total cost saving. A case application reveals the effectiveness of the above method. At the final optimal point, the round trip efficiency and the annual total cost saving are 0.762 and 147,820.0 $/year, respectively. Then, the design parameters of the proposed system are as follow: the capacity of each compressor is 256.0 kW, the capacity of turbine is 830.0 kW, and the volume of compressed air reservoir is 502.0 m3. In addition, a sensitivity analysis is performed mainly to assess the influence of key parameters variation on the proposed CAES system performance. This research will provide a guiding principle for design and practical application of trigenerative CAES system to mitigate the power shortage and improve economy for users. [ABSTRACT FROM AUTHOR]

Highlights • Surrogate models of range-extender system are built with artificial neural network. • Method of optimization and matching for range-extender is proposed. • Effect and sensitivity analysis based on surrogate models is conducted. • High efficiency for the range-extender is achieved. • Theoretical and method guidance for development of a range-extender. Abstract The primary issues for the popularization of electric vehicles are low energy density, short life, high cost, and long charging time of battery. In an extended-range electric vehicle, a range-extender is applied to realize the on-board electricity generation avoiding the range anxiety; and a large capacity battery and public charging facilities are not necessary. A primary issue is lack of an efficient range-extender that is light, compact and silent. The main reasons are that the efficiencies of range-extended engines now available are low; and the efficiency optimum operating points of the range-extended engines and generators are not matched. A 3-cylidner gasoline spark-ignition engine for an application in a range-extender has been investigated. Atkinson cycle, exhaust gas recirculation and gasoline direct injection are applied to suppress the knocking. At most cases, a range-extender engine has only a most frequent operating point. All design parameters and operating variables of the range-extender engine can be optimized around the single operating point to maximize the efficiency while matching to the highest efficiency point of a range-extender generator. For this purpose, an optimization and matching method by combing artificial neural network and genetic algorithm has been investigated. The optimization and matching for a range-extender engine, with no generator constraint and with three different constraints of generator efficiency maps, have been conducted. The results show that both the operating points and the optimal parameters of the range-extender engine are different under the different generator constraints. A higher maximum thermal efficiency of the range-extender engine can be achieved as the power requirement decreases. The maximum efficiency of the range-extender engine can reach 40.2%, which is because of the application of high geometrical compression ratio, exhaust gas recirculation, Atkinson cycle and single-point optimization of all parameters. [ABSTRACT FROM AUTHOR]

Highlights • A cascade absorption refrigeration system is proposed. • The proposed system is used to recover low-grade waste heat. • Thermodynamic models of the proposed system have been built. • Energy, exergy and economic analyses have been conducted. • The multi-objective optimization scheme is optimized by NSGA-II. Abstract In the present study, an innovative cascade absorption refrigeration (CAR) system for recovering low-grade waste heat is proposed. Based on the simulation results, the energy, exergy and economic (3E) analyses of the cascade refrigeration system have been conducted. The analysis results show that there is a contradiction between economic performance and thermodynamic performance. Then, the optimization scheme aiming at total exergy destruction (E ̇ D t), the optimization scheme aiming at total annual cost (C t) and multi-objective optimization scheme (C t and E ̇ D t) are optimized by non-dominated sort genetic algorithm-II (NSGA-II) technology. E ̇ D t is 11% more than its minimum result in the optimization scheme aiming at total annual cost, which shows that the thermal performance of the scheme is poor. C t is 14.8% more than its minimum result in the optimization scheme aiming at total exergy destruction, which shows that the scheme is not economical. C t and E ̇ D t are only 5.9% and 4.5% respectively higher than their minimum results in the multi-objective optimization scheme. Therefore, multi-objective optimization scheme is the most comprehensive and optimal scheme of the CAR system. [ABSTRACT FROM AUTHOR]

Highlights • Organic Rankine cycle coupled with a radial inflow turbine is modeled. • System is optimized with two objective functions. • Optimum design parameters of the radial inflow turbine are determined. • Groups of ideal working fluids are suggested for three heat source temperatures. Abstract Organic Rankine cycles (ORCs) are used to generate power from low temperature heat sources. In this study, 28 hydro carbon (HC), hydro fluorocarbon (HFC) and hydro fluoroolefin (HFO) working fluids are used for three different heat source temperatures (90, 120 and 150 °C). A 1-D radial-inflow turbine (RIT) design with loss calculations is incorporated into the model to add dynamic turbine stage efficiency. The turbine model is run together with the ORC, and real gas properties are used for the entire system. A genetic algorithm multi-objective optimization is used to design an ORC with minimum thermal conductance per net power output and maximum performance factor (PF). The decision variables are specific speed, condensation pressure, pressure ratio at the turbine stage, degree of superheating and pinch point temperature difference (PPTD) in the evaporator. Pareto frontiers are obtained at the end of the study, and a decision-making method (TOPSIS) is used to select an optimum solution for each working fluid. R1234yf, R1234ze(e) and isobutane are found to be the optimum working fluids at 90, 120 and 150 °C heat source temperatures respectively. For each solution, turbine geometry, fluid velocities and impeller rotational speed are calculated. [ABSTRACT FROM AUTHOR]

Highlights • Techno-economic optimization and environmental analysis (LCA) of microgrid was performed. • Found optimum microgrid configuration with lowest LCOE ($/kWh) using Genetic algorithm. • Analyzed microgrid size and location for 1, 10, and 50 home capacities in 3 different US locations. • Varying capacity revealed LCOE reduced by 7–12% depicting economies of scale effect. • LCOE (no subsidies) was $0.32–0.42/kWh and CO 2 emissions were 90% less than conventional grid. Abstract A methodology was developed that assessed the techno-economic and environmental performance of a small scale microgrid located in US cities of Tucson, Lubbock and Dickinson. Providing uninterrupted power the microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H 2 tanks. Firstly, detailed mathematical models that predicted the hourly energy generation for each of the components were developed and validated. Secondly, based on an electricity dispatch strategy, configurations having lowest LCOE (Levelized Cost of Energy) were determined using an evolutionary optimization technique - Genetic Algorithm (GA). Results for a single home microgrid were verified using an exhaustive search technique that scanned the entire design space to find the lowest LCOE configuration. The microgrid size was subsequently increased to satisfy power requirements for 10 and 50 homes and new lowest LCOE configurations were determined using GA to examine the economies of scale effect on sustainability. The LCOEs obtained were in the range of $0.32–0.42/kWh and were also compared with similar economic analyses available in the literature. The carbon footprint (LCA GHG emissions – CO 2 eq.) was extremely low and was approximately 1/10th as that of an equivalent conventional electric grid. [ABSTRACT FROM AUTHOR]

Highlights • A triple cycle is proposed consisting of a SOFC and gas and steam cycles with an auxiliary burner. • Energy, exergy, and economic analyses of the triple cycle are performed. • The triple cycle is optimized with a multi-objective genetic algorithm. • Sensitivity analyses are performed of key variables in the optimization. Abstract The current study is directed at the optimization and energy, exergy, and economic analyse of a solid oxide fuel cell, integrated with gas and steam trigeneration systems. The variables considered are the fuel cell's temperature and pressure, air-to-fuel molar ratio, the compressor and gas turbine pressure ratio, combustion chamber temperature, steam cycle pinch point. The objective functions are the exergy efficiency and the electricity cost. The results show that by selection of the optimal values of the fuel cell's temperature and pressure, air-to-fuel molar ratio, compressor and gas turbine pressure ratio, and the cycle pinch point as 809.8 K, 360 kPa, 2, 2.05, 11.6, and 34.9, the exergy efficiency is increased by 8%, and the produced electricity cost is reduced by 9.7%. The sensitivity analysis shows that an increase in compressor pressure ratio leads to a decrease in the energy and exergy efficiencies and entropy production rates. A change in air-to-fuel molar ratio from 2 to 3 causes the triple cycle electricity cost first to increase and then to decrease. [ABSTRACT FROM AUTHOR]

Highlights • Optimal design of desiccant cooling with energy generation and storage is presented. • The optimization was achieved using a neural network and a genetic algorithm. • Specific net electricity generation was used as the optimization objective. • A dimension reduction method was used to determine key design parameters. • Design solutions identified by this method outperformed a baseline design. Abstract A design optimization strategy for rotary desiccant cooling (RDC) systems integrated with a photovoltaic thermal collector-solar air heater (PVT-SAH) and a phase change material based thermal energy storage (TES) (named RDC-PVT-SAH-TES) is presented in this paper. The optimization method was developed using a multilayer perceptron neural network (MPNN) and a genetic algorithm to maximize the specific net electricity generation (SNEG) of RDC-PVT-SAH-TES systems while maintaining the required cooling demand with the assistance of an electric heater. A dimension reduction method was used to determine the main design parameters of the RDC-PVT-SAH-TES system. An RDC-PVT-SAH-TES system was simulated using TRNSYS and the simulation data were utilized for training and validation of the MPNN model and for dimension reduction analysis. A comparison of the design solution identified by this optimization method with a baseline design showed that the SNEG and the solar thermal contribution of this RDC-PVT-SAH-TES system can be increased from 3.77 kWh/m2 to 10.32 kWh/m2 and from 91.5% to 99.4%, respectively. The optimization method developed could be potentially adapted to facilitate optimal design and size of other engineering systems with onsite energy generation and thermal storage. [ABSTRACT FROM AUTHOR]

Highlights • Multi-objective optimization of double effect vapour absorption systems is presented. • Water–lithium chloride and water–lithium bromide are used as solution pairs for comparison. • Distribution ratio and low and high pressure generator temperatures are the decision variables. • Coefficient of performance, exergy efficiency and system irreversibility are the objective functions. • The optimization is done for four cases of fixed evaporator, absorber and condenser temperatures. Abstract In this study, multi-objective optimization of the series and parallel flow type double effect absorption refrigeration systems is presented. Genetic algorithm is used to find the optimal solutions and the Pareto-optimal fronts. First, a thermodynamic model is developed to simulate the double effect series and parallel flow configurations with water–lithium chloride and water–lithium bromide solution pairs. Energy and exergy balances are applied to evaluate coefficient of performance, exergy efficiency and irreversible losses in the system components separately for the two working fluid pairs. Next, optimization is done considering coefficient of performance, exergy efficiency and the total system irreversibility rate as objective functions. The low and high pressure generator temperatures are taken as decision variables in the series configuration while for the parallel system; additionally the distribution ratio is also taken as a decision parameter. The optimization is done for four different cases of fixed evaporator, absorber and condenser temperatures. For each case, the optimal decision parameter values are determined for both the water–lithium chloride and water–lithium bromide operated double effect series and parallel configurations. The optimized coefficients of performance were found slightly lower for water–lithium chloride. Contrary to this, the optimal exergy efficiencies were more and the total system irreversibility were less for water–lithium chloride. Further, the optimal performances of the double effect water–lithium chloride systems were obtained comparatively at lower generator temperatures than those of the water–lithium bromide systems. [ABSTRACT FROM AUTHOR]

Highlights • The partial shading condition is fully considered in MPPT of PV system. • A dynamic leader based collective intelligence (DLCI) is proposed for MPPT. • DLCI can exploit multiple searching mechanisms from various sub-optimizers. • A dynamic leader is selected for a deeper guidance to other sub-optimizers. • DLCI based MPPT is evaluated by four case studies and a hardware-in-the-loop test. Abstract This study presents a novel maximum power point tracking (MPPT) algorithm via dynamic leader based collective intelligence (DLCI) of PV systems affected by partial shading condition (PSC). Different from the conventional meta-heuristic algorithms, DLCI is consisted of multiple sub-optimizers, which can achieve a much wider exploration by fully collaborating the optimization ability of various searching mechanisms instead of a single searching mechanism. In order to achieve a deeper exploitation, the sub-optimizer with the current best solution is chosen as the dynamic leader for an efficient searching guidance to other sub-optimizers. Although the multiple sub-optimizers of DLCI will result in a higher computational complexity, it can offer an enhanced searching ability and a more stable convergence compared to that of conventional meta-heuristic algorithms. Since it does not reply on the system model, DLCI can be easily applied to other optimization tasks. Four case studies, including start-up test, step change in solar irradiation with constant temperature, gradual change in both solar irradiation and temperature, and daily field data of solar irradiation and temperature in Hong Kong, are undertaken. They attempt to evaluate the effectiveness and advantages of DLCI in comparison to that of conventional incremental conductance (INC) and eight typical meta-heuristic algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bees colony (ABC), Cuckoo search algorithm (CSA), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), and teaching–learning-based optimization (TLBO), respectively. Lastly, a dSpace based hardware-in-the-loop (HIL) test is carried out to validate the implementation feasibility of DLCI based MPPT technique. Both the case studies and HIL test demonstrate that the searching ability of DLCI can be significantly improved via an effective coordination between multiple sub-optimizers, which can make the PV system generate more energy (up to 36.64%) and smaller power fluctuation (up to 21.17%) than other methods with a single searching mechanism. [ABSTRACT FROM AUTHOR]

Highlights • Circuitry arrangement optimization for multi-tube phase change material heat exchanger was proposed. • The optimization approach was based on genetic algorithm coupled with numerical simulation. • The genetic algorithm optimization process was improved by adding domain knowledge. • The arrangement rules of optimal circuitry were not necessarily the same for different flow rates. • Implementing optimization based on the operating conditions maximized the effect. Abstract This study focused on circuitry arrangement optimization for a multi-tube phase change material heat exchanger. The optimization approach was developed based on a genetic algorithm approach coupled with numerical simulation and was aimed at maximizing the equivalent hot water output during the discharging process. Validated two-dimensional and three-dimensional simulations were used to support the genetic algorithm optimization process and to accurately investigate the thermal performance of the optimal circuitry arrangement. The results showed that adding more connections near the outlet side of the phase change material heat exchanger could lengthen the flow path in the high temperature zone, which was beneficial for a high mass flow rate condition of 1.68 kg min−1 with a 5% increment. Under medium and low mass flow rates of 1.2 kg min−1 and 0.5 kg min−1, a 3% increment and a 6% decrement, respectively, were observed. It was found that different mass flow rates require their own optimal circuitry arrangements, and the arranged rules were not necessarily the same. Therefore, circuitry arrangement optimization should be performed according to the operating flow rate or designed range to maximize the optimization effect. The proposed optimization approach could serve as an effective way to improve the energy efficiency of multi-tube latent thermal energy storage systems. [ABSTRACT FROM AUTHOR]

Highlights • Multi-objective optimization of desiccant wheels is investigated. • Response surface method is used for establishing novel regression models. • Effects of operating variables on corresponding responses are comprehensively evaluated. • Optimum values of input variables have been derived to minimize process outlet temperature and humidity ratio. • A valuable equation for determination of Pareto-optimal points has been proposed. Abstract A two-step computational framework based on the combination of response surface methodology and multi-objective optimization is proposed to model the outlet-air state of desiccant wheels and subsequently optimize their operation. Regeneration temperature, surface area ratio, rotational speed, and wheel diameter are considered as decision parameters in the genetic algorithm. The central composite design and response surface methods have been employed to design experiments, establish predictive empirical models, and determine interactive effects of decision variables on response variables—process outlet temperature and humidity ratio. Several experiments have been performed to verify applicability of the proposed methodology and validate obtained results. A value of the coefficient of determination exceeding 0.95 demonstrates high reliability and accuracy of the modeling process involved in the proposed methodology. Results obtained demonstrate greater dominance of the surface area ratio compared to other decision variables in terms of their influence on response variables. After successful validation against experimental data, the developed models have been considered as a combination of two objective functions. A fast and elitist non-dominated sorted genetic algorithm II-based optimization technique has been employed to simultaneously determine optimum values of decision variables. A Pareto-optimum front has been presented to select the best value of each decision parameter from available points of optimum operation, and a valuable equation for Pareto-optimal points has been deduced for each material to assist designers develop an optimum design of desiccant cooling systems. [ABSTRACT FROM AUTHOR]

• An IES framework with CO 2 absorption function is proposed. • CO 2 absorption rate can reach 38.24%. • Heat generated from electrolysis of water and fuel cell power generation is utilized. An integrated energy system, as a system where multiple energy sources coexist, is important for achieving energy conservation and emission reduction and upgrading the energy structure. In this study, a regional integrated energy system capable of absorbing CO 2 emissions is proposed. The system can absorb 38.24% of the CO 2 generated in the system by using the alkaline solution produced by electrolysis of water for hydrogen production. At the same time, the system can recover the waste heat generated by electrolysis of water and fuel cells, which can meet 42.42% of the heat demand. In order to better achieve efficient utilization between energy sources of the integrated energy system, a nonlinear mixed integer dynamic scheduling optimization model is constructed with multiple objectives of minimizing economic operating costs, CO 2 emissions, and energy consumption. The model is solved using a non-dominated ranking genetic algorithm, and the optimal scheduling scheme is determined by combining compromise schemes. Meanwhile, the system can spontaneously select the most appropriate scheduling strategy based on the proposed scheduling model. The results show that it saves 7.23% of operation cost, 33.04% of CO 2 emission, and 9.94% of energy consumption compared with the conventional cooling, heating, and power triple-supply system. The system is not only a stable source of energy such as electricity and hydrogen, but also achieves energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Six feature selection methods were evaluated for wind speed quantiles estimation at ungauged locations. • Feature selection enabled the identification of the most important predictors for various wind speed quantiles. • The most parsimonious features selection methods led to the lowest generalization error. • The location distance from the coast and the surface roughness were the most significant wind speed quantiles predictors. Wind speed estimation at ungauged locations is one of the preliminary steps for wind resource assessment. With the availability of high-resolution Digital Elevation Models (DEM) and remote sensing data, the number of potential wind speed predictors has grown substantially. The adequate spatial scale of these predictors is unknown a priori, leading to the use of multiple spatial scales of predictors in wind speed estimation models. Implementing a feature selection method as a pre-processing step of the analysis is necessary to avoid overfitting and the resulting potential model underperformance. This paper evaluated six feature selection methods (forward stepwise regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, maximum relevance minimum redundancy (MRMR), Genetic algorithm, and recursive feature elimination using support vector regression) for the estimation of different wind speed quantiles across Canada. The selected features were used to fit a regression-kriging model, and the importance of the predictors was evaluated with their associated regression coefficients. The results of the study showed that LASSO and MRMR are the most efficient algorithms with the least number of parameters to tune and good generalization performance. The study found that some predictors were more important for specific exceedance probabilities. The most important predictors were the distance from the coast and surface roughness length, regardless of exceedance probability. [ABSTRACT FROM AUTHOR]

• Combination of air and water injection can be used to minimize pressure fluctuation in part load operation of Francis turbine. • New index is defined for turbine performance considering pressure fluctuation and effective efficiency. • Combination of computational and optimization approach can specify the best operational point of Francis turbine. • Using the combined approach, optimized parameters including air and water injection flow rate, air nozzle diameter and height was determined. Optimal operation of hydro turbine as one of the most important sources of renewable energy is of great importance. In this article, using a combination of experimental and numerical methods, the effect of different water and air injection on the vortex reduction in a Francis turbine draft tube was studied and the most optimal condition is selected in order to minimize pressure fluctuations and maximize effective efficiency. For this purpose, pressure fluctuations of the water turbine of a real hydropower plant were experimentally measured under different loading conditions to find the worst condition need to be improved. It was found with injecting water at 1.5% and 3% of the total volume of inlet flow rate, the performance index (γ) improves by 38% and 10% respectively, compared to the case without injection. By injecting air at 1% and 2%, the performance index shows 36% and 38% improvement respectively. Optimization was done with the help of Nondominated Sorting Genetic Algorithm II. The results showed that the most optimal condition of injections are 1.63% and 0.65% air and water volume flow rates, respectively, using 0.019-meter air nozzle diameter at −2.80-meter injection height, which reduce pressure fluctuations by 33.44%. [ABSTRACT FROM AUTHOR]

In order to accelerate the commercialization of solid oxide fuel cells, optimal process parameters for reliable and efficient electricity generation are of the highest interest. To reduce the number of timely and monetarily expensive experiments used to find suitable operating parameters, while also allowing for only small sacrifices in accuracy, an artificial neural network (ANN) is used in combination with algorithm-based optimization in this study. An ANN was trained with data from a complex multi-physics model and coupled with a genetic algorithm (GA) to find the maximum power output within a range of operational parameters. Instead of time-consuming, manual fine-tuning of the ANN's model architecture and hyperparameters (HP), a Bayesian HP-tuning algorithm is used in this work. To avoid over-fitting and to ensure a high model consistency, nested k-fold cross validation is implemented. Very low error values of the ANN are achieved both in the k-fold cross-validation and in an additionally performed validation by means of experimental data and a computational fluid dynamics simulation. Compared to a multi-physics model used to generate training data, the ANN achieved an increased prediction speed of more than three orders of magnitude, while only minimal decreases to prediction accuracy. Optimization with the GA produced consistent results close to the global optimum and also provided good alternative solutions with significantly different gas compositions at high power. The validity of the solutions found with the GA was underpinned with the help of a sensitivity analysis, which was carried out for the most promising SOFC operating case. • Closing the gap between accuracy and speed in solid oxide fuel cell modelling. • Automated artificial neural network (ANN) architecture and hyperparameter tuning. • Model validation with 2D multi-physics model, CFD simulation and experimental data. • Identified optimal process parameters by combining ANN with a genetic algorithm. [ABSTRACT FROM AUTHOR]

• Two MED-MVC systems with FF and PCF feedings are evaluated. • MED-MVC-FF presents the optimal UCDW and distilled water production separately. • MED-MVC-PCF allows to optimize simultaneously UCDW and distilled water production. • MED-MVC-PCF is more sensitive to part load conditions. This article presents the simulation results of a Multi Effect Desalination system coupled to a Mechanical Vapor Compression system. The system is optimized with the Non-dominated Sorting Genetic Algorithm II genetic algorithm by obtaining the optimal results for the desalination plant operating with 8–12 effects, with Forward Feeding and Parallel Cross Feeding. The best results are obtained for two cases: a) For a Multi Effect Desalination system with Parallel Cross Feeding operating with 12 effects, a Top Brine Temperature of 64 °C and a feed water flow rate of 293 kg/s. This configuration presents a Unit Cost of Distilled Water of 1.94 USD/m3 and 128.1 kg/s of fresh water. b) And for a Multi Effect Desalination system with Forward Feeding with 9 effects, a feed water flow rate of 380 kg/s, a Top Brine Temperature of 64 °C, which allows obtaining a Unit Cost of Distilled Water of 1.79 USD/m3. The same system but with 12 effects, a Top Brine Temperature of 72 °C, a feed water flow rate of 380 kg/s allows obtaining a higher water production of 152.3 kg/s, which shows that there is more than one optimal solution for this kind of systems, which depends on the figure to be optimized. [ABSTRACT FROM AUTHOR]

The paper introduces artificial intelligence (AI) approach for the optimization study of a hydrogen concentrations in syngas via CaO Sorption. The performed model allows estimating the hydrogen content in the syngas produced from biomass in different types of facilities. Bubbling fluidized bed (FB) and circulating fluidized bed (CFB) are taken into account in the study. Comparing with the previous FB gasification results, CFB gasification was capable of producing syngas with higher hydrogen concentration. The model considers and covers a broad range of conditions, influencing the hydrogen rich-gas production. The presented non-iterative approach gives quick and accurate results as an answer to the input data sets. The H 2 concentration in the gas, estimated using the developed model, is in good agreement with the experimental data. Maximum relative error between measured and calculated data is lower than ±8%. The model allows also studying the influence of operating parameters on the hydrogen concentration in the gas. The method constitutes an easy to employ and useful complementary technique in relation to the other ways of data handling, including experimental procedures. The model can be used by scientists and engineers for optimizations purposes and can be applied as a submodel or a separate module in engineering calculations, capable to predict the H 2 concentration in the syngas from biomass via the CaO sorption both in FB and CFB gasifiers. [ABSTRACT FROM AUTHOR]

As an effective strategy to control the combustion of advanced combustion modes, the application of variable valve timing (VVT) in reactivity controlled compression ignition (RCCI) combustion was investigated in this study. By coupling KIVA-3V code with genetic algorithm, the combustion of a heavy-duty engine with RCCI combustion combined with VVT strategy was optimized under a wide load range. At each load, six operating parameters including premix ratio (PR), intake valve closing (IVC) timing, start of injection, exhaust gas recirculation rate, intake pressure, and intake temperature were optimized to realize low-emission and high-efficiency combustion. The optimization results indicate that, at low load, high PR coupled with either late IVC or base IVC can be utilized for the realization of high thermal efficiency. At mid load, the base IVC strategy is integrated with high PR, while the late IVC strategy is coupled with low PR. At high load, only the strategy with late IVC and low PR can be used. The strategy with higher PR and earlier IVC timing exhibits better engine performance on thermal efficiency and soot emissions, while the strategy with lower premix ratio and later IVC timing is superior in ringing intensity. By optimizing the RCCI combustion with the VVT strategy, the Euro 6 NO x limit can be met while maintaining ultra-low soot emissions at low and mid load. However, at least one aftertreatment device is required to further eliminate the NO x or soot emissions at high load. Under the whole load conditions, satisfactory fuel consumption can be obtained. [ABSTRACT FROM AUTHOR]

This paper presents an optimization of a high temperature organic Rankine cycle (ORC) system providing the basis for optimal fluid selection and subsequent design parameters based on the working fluid selected. The working fluids used are m-xylene, propylcyclohexane and decane having high critical temperatures. The proposed system deals with the application of biomass due to the high content heat available during its combustion. The system is optimized through non-dominated sorting genetic algorithm (NSGA-II) by taking the prime operators such as; exergetic efficiency (η ex ) to extract maximum work and total heat transfer requirement (UA) to get a prediction of the heat transfer area and hence the cost of the system. The parameters subjected to constraints for optimization are evaporation pressure, degree of superheating and pinch point conditions at heat exchangers. The optimization results exhibit an increase of 22.9% for propylcyclohexane and 45.5% for decane in UA values, relative to m-xylene. Highest exergetic efficiency values for m-xylene among three working fluids further ensures its use in the system as the most viable option from both thermodynamic and economic aspect. Moreover, optimal evaporation pressure range is evaluated by taking the maximum and minimum of exergetic efficiency and UA value, respectively. Both objective functions show negative trend with increase in degree of superheating, with less significant drop. As the pinch point value increases, the UA value decreases showing significantly smaller areas of heat transfer and less cost, but with low exergetic efficiency, therefore, moderate pinch point condition of 8–10 °C is recommended. [ABSTRACT FROM AUTHOR]

The thermo-economic optimization of a solar organic Rankine cycle (SORC) should consider the features of fluctuations in solar radiation based on local historical solar radiation. However, the use of historical solar radiation is inconvenient for thermo-economic optimization because it involves considerable computational effort for simulation. To overcome this inconvenience, we propose the thermo-economic optimization of SORC by using typical solar radiation year (TSRY). TSRY is a synthesis of typical solar radiation on the basis of historical solar radiation, indicating that TSRY can reflect the typical features of fluctuations in solar radiation in a specific area. Afterward, the multi-objective genetic algorithm (GA) is selected to optimize the dynamic performance of a small-scale SORC by using the TSRY. In GA, the evaporation temperature and capacity of thermal energy storage are taken as optimization parameters, and the power output and fluctuation in power output are optimization goals. Accordingly, Pareto frontiers that optimize the SORC performance can be obtained. The effect of different parameter combinations in the Pareto frontiers and the scale of the SORC on thermo-economic are further analyzed using annual net profit as an indicator. Our analysis shows that a minimum SORC scale for profitability is set for a given location, and the profit growth rate increases as the system scale increases. [ABSTRACT FROM AUTHOR]

The shipping industry has been facing great pressure to become more sustainable, emanating from the increasingly stringent environmental regulations, fuel prices volatility and societal needs. As a result, a variety of technologies have been developed aiming to improve the environmental and economic performance of the modern ship energy systems, however leading to additional challenges for the technology selection during the design process. This study introduces an innovative method that integrates the economic and environmental aspects of sustainability to support decisions on the synthesis of the modern ship energy systems. The method includes a simulation model for predicting the energy systems performance during the ship lifetime. The genetic algorithm NSGA-II, is employed to solve the multi-objective combinatorial optimisation problem of selecting the integrated ship energy systems configuration. The derived results are visualised to reveal the Pareto front and the trade-offs among the objectives. The method is novel in supporting the synthesis of the integrated ship energy systems, as it includes both environmental and economic objectives, as well as evaluates the performance of the systems over an expected operational profile. The developed method is implemented for the case study of an Aframax oil tanker and the derived results analysis indicates that the ship energy systems sustainability can be improved by adopting LNG fuel and dual fuel engines technology, as well as by introducing other emerging technologies like fuel cells and carbon capture, although the latter is associated with a high cost. It is concluded that the inclusion of both environmental and economic objectives highlights the trade-offs between more environmentally friendly or cost efficient configurations, thus supporting the multi-objective decision-making process. [ABSTRACT FROM AUTHOR]

As an essential issue in wind energy industry, wind speed forecasting plays a vital role in optimal scheduling and control of wind energy generation and conversion. In this paper, a novel method called EnsemLSTM is proposed by using nonlinear-learning ensemble of deep learning time series prediction based on LSTMs (Long Short Term Memory neural networks), SVRM (support vector regression machine) and EO (extremal optimization algorithm). First, in order to avert the drawback of weak generalization capability and robustness of a single deep learning approach when facing diversiform data, a cluster of LSTMs with diverse hidden layers and neurons are employed to explore and exploit the implicit information of wind speed time series. Then predictions of LSTMs are aggregated into a nonlinear-learning regression top-layer composed of SVRM and the EO is introduced to optimize the parameters of the top-layer. Lastly, the final ensemble prediction for wind speed is given by the fine-turning top-layer. The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term wind speed forecasting. Statistical tests of experimental results compared with other popular prediction models demonstrated the proposed EnsemLSTM can achieve a better forecasting performance. [ABSTRACT FROM AUTHOR]

Last years have been characterized by a worldwide increasing attention towards the reduction of fuel consumption and carbon dioxide emissions. Several industrial fields, as well as the civil and residential sector, have introduced innovative approaches for the design and the operation of energy systems. These actions are aimed to reach higher values of energy conversion efficiency, also including an increase in the use of renewable resources. In this context, especially in the sector of cruise ships, further efforts are required to improve the energy efficiency of the employed energy systems. The aim of this paper is to propose an optimization framework based on genetic algorithms in order to maximize the energy efficiency and minimize both the fuel consumption and the thermal energy dissipation, by optimizing the load allocation of the ship energy systems. To this purpose, different strategies for the energy systems on board of an existing cruise ship are proposed and analyzed. In particular, two main engines configurations have been defined: standard (current logic of operation maintained) and hybrid configuration. For each proposed strategy – being the ship a particular and interesting application of isolated energy grid ( i.e. a grid without connections with electric and fuel national grids) – an in-house-developed software has been adapted and applied to optimize the load allocation of the various energy systems. Furthermore, an economic and environmental analysis has been carried out, in order to point out the benefits – or the eventual limits – related to the proposed solutions. The considered approach is based on the concept of introducing economically and structurally suitable modifications to the current cruise energy systems configuration, in order to reach the goal of increasing the energy efficiency. The carried out analysis shows that the hybrid strategies allow to reach the best results in terms of energy (fuel consumption and heat dissipation reduction), economic and environmental points of view. [ABSTRACT FROM AUTHOR]

This paper presents performance prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery based on artificial neural network (ANN). An ANN based prediction model of the ORC system is established with consideration of mean squared error and correlation coefficient. A test bench of combined diesel engine and ORC waste heat recovery system is developed, and the experimental data used to train and test the proposed ANN model are collected. A genetic algorithm (GA) is also considered in this study to increase prediction accuracy, and the ANN model is evaluated with different learning rates, train functions and parameter settings. A prediction accuracy comparison of the ANN model with and without using GA is presented. The effects of seven key operating parameters on the power output of the ORC system are investigated. Finally, a performance prediction and parametric optimization for the ORC system are conducted based on the proposed ANN model. The results show that prediction error of the ANN model with using the GA is lower than that without using GA. Therefore, it is recommended to optimize the weights of the ANN model with GA for a high prediction accuracy. The proposed ANN model shows a strong learning ability and good generalization performance. Compared to the experimental data, the maximum relative error is less than 5%. The experimental results after optimizing the operating parameters are very close to ANN’s predictions, indicating one or more operating parameters can be adjusted to obtain a higher power output during the experiment process. [ABSTRACT FROM AUTHOR]

Combined cooling, heating and power (CCHP) system is drawing great attention due to its energy-saving, environmentally friendly and cost-saving characteristics. Conventionally, CCHP system uses the water–LiBr absorption chiller to meet the cooling demand. In this paper, comparison of exhaust-gas-and-hot-water-driven absorption chiller (AC) and another three cooling modes, including AC combined with electric chiller (EC), AC combined with gas-fired absorption chiller (GFC), and AC combined with ground source heat pump (GSHP), is made. Optimization models for the four CCHP systems following the electric load (FEL) and following the thermal load (FTL) are proposed. The evaluation criteria include primary fossil energy saving rate (PFESR), carbon dioxide emission reduction rate (CDERR) and annual total cost saving rate (ATCSR) compared with the separation production (SP) system. Genetic algorithm (GA) is used to achieve the optimal solutions. A case analysis of a hypothetical commercial building in Shanghai is performed. Results show that the CCHP system with GSHP under FEL strategy has the best comprehensive performance, with PFESR of 0.2990, CDERR of 0.5278 and ATCSR of 0.1582. Finally, sensitivity analysis of climate, natural gas price and pool purchase price are performed. [ABSTRACT FROM AUTHOR]

In order to further improve the efficiency of the gas turbine, a novel combined supercritical CO 2 regenerative cycle and organic Rankine cycle using zeotropic mixtures for waste heat recovery of gas turbine is proposed. The zeotropic mixtures used in the present study are cyclopentane/R365mfc. Exergoeconomic analysis is reported for the proposed system and parametric studies have been carried out to investigate the effect of system parameters on the exergy efficiency and the unit cost of electricity. The multi-objective optimization method based on genetic algorithm is chosen to obtain the optimum system parameters. The results show that the overall values of the exergoeconomic factor, the optimal exergy efficiency and the optimal unit cost of electricity of the proposed system are 31.88%, 62.23% and 3.95 cent/kW h, respectively. The obtained result reveals the superiority of the proposed combined regenerative S-CO 2 cycle and ORC system compared to the combined basic S-CO 2 cycle and ORC system, the combined recompression S-CO 2 cycle and ORC system. Therefore, the proposed system is suitable for gas turbine waste heat recovery, and it has advantages of deep utilization of waste heat, high efficiency and low cost. [ABSTRACT FROM AUTHOR]

This paper aims to explore the alternatives for waste heat recovery in a floating production storage and offloading (FPSO) platform to meet the demand for heat (from hot water) and to maximize the electric power generation through the organic Rankine cycle (ORC) with purpose to increase the overall thermal efficiency of the process and reduce CO 2 emissions. Two different cycles’ configurations are explored (simple and regenerative) using exhaust gases from the gas turbines as the heat sources for the ORC and the cogeneration system. The curves of the GE LM2500 and GE LM2000 turbines are modeled together with the water heating systems and the organic Rankine cycle. The model is solved using a genetic algorithm optimization method, whose objective function is set to meet the electric power demand for the FPSO platform. The purchased equipment costs of the ORC, the reduction in fuel consumption and CO 2 avoided are estimated. Waste heat recovery meets the heat demand and contributes up to 21% of the electric energy demand, which increases the overall efficiency of the system, and improves the utilization factor by up to 10.8% and 19.2%, respectively. There is an average reduction of 22.5% in fuel consumption and CO 2 emissions during the lifetime of the FPSO. The economic analysis based on the NPV shows that a US$12.55 million return on investment is possible, in addition to reducing the initial investment cost by US$14.2 million through the exclusion of the GE LM2500 gas turbine at project implementation. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN’s) have been used to optimize the performance of a dry reformer with catalyst sintering taken into account. In particular, we study the effects of temperature, pressure and catalyst diameter on the methane and CO 2 conversions, as well the H 2 to CO ratio and the molar percentage of solid carbon deposited on the catalyst. The design of the ANN was automated using a genetic algorithm (GA) with indirect binary encoding and an objective function that uses the effective number of parameters provided by Bayesian regularization. Results show that an industrially-acceptable catalyst lifespan for a dry reformer can be achieved by periodically optimizing temperatures and pressures to accommodate for the change in catalyst diameter caused by sintering. In particular, it was found that the reactor’s operation favors high temperatures of almost 1000 °C, while the pressure must be gradually increased from 1 to 5 bars to remain as far as possible from carbon limits and ensure acceptable conversions and molar ratios in the syngas. [ABSTRACT FROM AUTHOR]

The potential benefits of Homogenous Charge Compression Ignition (HCCI) engines are higher thermal efficiency, lower Nitrogen Oxides (NOx) and Particulate Matter (PM) emissions in contrast with conventional engines. This mode of combustion faces its own set of challenges, such as lack of a direct means to control timing initiation of combustion together with higher unburned hydrocarbon and Carbon Monoxide emissions. In the present study, a multi-zone thermo-kinetic model developed to investigate the HCCI engine performance and to predict its emissions. In addition, to determine the optimal HCCI combustion phasing, a grey-box control model used to predict major variables. In order to optimize conditions for higher thermal efficiency and lower emissions, the thermo-kinetic model coupled with a Genetic Algorithm. The octane number and equivalence ratio are two qualitative parameters, which determine the optimal conditions. The grey-box model consists of a combination of physical models and Artificial Neural Networks (ANN) models. The steady state and transient validations show that the grey-box model is capable of predicting HCCI engine outputs including combustion phasing, load, THC, CO emissions. By means of coupling the GA and thermos-kinetic model, the optimal engine operating conditions can be derived without the use of test results. The validation results show that the grey-box model is able to predict CA50, IMEP, CO and THC with the average errors of 1.2 CAD, 0.4 bar, 10 PPM, 0.8%, and 394 PPM, respectively. [ABSTRACT FROM AUTHOR]

The organic Rankine cycle (ORC) is regarded as one of the most promising methods to increase the efficiency of diesel engines. Owing to the variability of exhaust energy with engine speed and load, the ORC should be designed for various operating conditions of engines for optimal waste heat recovery (WHR). In this study, an integrated simulation model of the diesel engine–ORC combined system (the combined system) is built by GT-Suite. Based on the model, the MAPs of optimum parameters are obtained by an artificial neural network (ANN) and a genetic algorithm (GA). Subsequently, operation modes of the combined system under various conditions are proposed. Finally, a control strategy of switching operation modes and adjusting parameters that adapts to various conditions of diesel engines is developed by GT-Suite and MATLAB/Simulink. Simulation results show that the optimum pump speed is steady approximately 1000 r/min under the low load region of the engine and increases with the engine load when the engine speed is higher than 1800 r/min. By contrast, the optimum expander speed is 1500 r/min in all selected engine operating conditions. Further investigations indicate that the performance of the combined system presents improvements, with a 3.57% increase in thermal efficiency and a 10.09 g/(kW·h) reduction in brake specific fuel consumption (BSFC) when compared against the original diesel engine. These preliminary results prove that the integrated simulation model can be used for further research. Meanwhile, with regard to the proposed control strategy, more thorough experimental research needs to be conducted. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) has been proven to be an effective and promising technology to convert low-grade heat energy into power, attracting rapidly growing interest in recent years. As the key component of the ORC system, turbine significantly influences the overall cycle performance and its efficiency also varies with different working fluids as well as in different operating conditions. However, turbine efficiency is generally assumed to be constant in the conventional cycle design. Aiming at this issue, this paper couples the ORC system design with the radial-inflow turbine design to investigate the thermodynamic performance of the ORC system and the aerodynamic characteristics of radial-inflow turbine simultaneously. The constrained genetic algorithm (GA) is used to optimize the radial-inflow turbine with attention to six design parameters, including degree of reaction, velocity ratio, loading coefficient, flow coefficient, ratio of wheel diameter, and rotational speed. The influence of heat source outlet temperature on the performance of the radial-inflow turbine and the ORC system with constant mass flow rate of the heat source and constant heat source inlet temperature is investigated for four kinds of working fluids. The net electrical powers achieved are from few tens kWs to one hundred kWs. The results show that the turbine efficiency decreases with increasing heat source outlet temperature and that the decreasing rate of turbine efficiency becomes faster in the high temperature region. The optimized turbine efficiency varies from 88.06% (using pentane at the outlet temperature of 105 °C) to 91.01% (using R245fa at the outlet temperature of 80 °C), which appears much higher compared to common values reported in the literature. Furthermore, the cycle efficiency increases monotonously with the growth of the heat source outlet temperature, whereas the net power output has the opposite trend. R123 achieves the maximum cycle efficiency of 12.21% at the heat source outlet temperature of 110 °C. Based on the optimized results, the recommended ranges of the key design parameters for ORC radial-inflow turbine are presented as well. [ABSTRACT FROM AUTHOR]

Most research published into stand-alone energy systems, hybridised by supplementing PV with combustion-based prime movers, considers meeting an electric load demand. This paper goes further by studying the role of both electric and heating loads on the optimisation of hybridised stand-alone Combined Heating and Power (CHP) systems. The role of both the load following strategy in these systems (electric only FEL, versus electric and thermal FEL/FTL) as well as the relative magnitude of the heating load is analysed on system cost and performance. The conceptual CHP systems modelled also consider waste system derived from either multiple Internal Combustion Engines (ICEs) or Micro Gas Turbines (MGTs). The research uses MATLAB-based Genetic Algorithm (GA) optimisation throughout and features detailed hardware characteristics as well as temporally fluctuating meteorological (solar irradiance, temperature) and load (electric, heating) data. The outcomes are also tested in relation to CHP systems sized whilst optimising either single (Cost of Energy-COE, $/kW h) or multiple functions (COE and overall system efficiency, η CHP ,%). Results indicate that whilst the power management strategy used in CHP systems (FEL or FEL/FTL) has minimal effects on the COE, it can appreciably affect other performance indicators. For example, in CHP systems sized based on FEL/FTL, whilst COE = ∼0.20 $/kW h the resulting η CHP is 66% for PV/Bat/ICE and 44% for PV/Bat/MGT. This is compared to using a PMS of the FEL type which results in similar COE = ∼0.21 $/kW h but with η CHP  = 50% in PV/Bat/ICE systems and 34% in PV/Bat/MGT. In relation to overall environmental impact expressed though Life Cycle Emission-LCE (kg CO 2 -eq/yr) when heating demand is around 50% of the electric (Electric to Thermal Load Ratio = 60:40), a PMS of the FEL/FTL results in up to 30% lower LCE compared to those with FEL in some CHP systems. [ABSTRACT FROM AUTHOR]

Adsorption cycles have a distinct advantage over other systems in the ability to use low grade heat, especially waste heat of near ambient temperature. A Tri-bed twin-evaporator adsorption chiller constitute an innovative design in cooling production which allows more efficient conversion and management of low grade sources of thermal energy due to more effective way of utilization adsorptive abilities of the beds during a single work. Although it is the most effective way in chilled water production the complexity of the Tri-bed twin-evaporator adsorption chiller operation is still not sufficiently recognized and the improvement in cooling capacity (CC) of the cooler is still a challenging task. The paper introduces artificial intelligence approach for the optimization study of a Tri-bed twin-evaporator adsorption chiller using low-temperature heat from cogeneration. Genetic algorithms (GA) and artificial neural networks (ANN) are used to develop the model which allows estimating the behaviour of the adsorption heat pump. Cooling capacity (CC) as one of the main energy efficiency factor in cooling production is examined during the study for different operating sceneries. The presented non-iterative approach gives quick and accurate results as an answer to the input data sets. The CC of the chiller, evaluated using the developed model, is in good agreement with the experimental data. Maximum relative error between measured and calculated data is lower than ±10%. The developed model permits to study the influence of operating parameters on the cooling capacity of the chiller. For the considered range of input parameters the highest cooling capacity which can be obtained by the heat pump is equal 93.3 kW. The method constitutes an alternative, easy-to-apply and useful, complementary technique, comparing to the other techniques of data handling, including the complex of numerical and analytical methods as well as high costs of empirical experiments. The model can be applied for optimizations purposes and can constitute a sub model or a separate module in engineering calculations, capable to predict the CC of the Tri-bed twin-evaporator adsorption cooler, integrated into multigenerative systems. [ABSTRACT FROM AUTHOR]

This study investigates the combined Rankine power and the absorption cooling cycles. The working fluid used in this cycle is the binary liquid mixture of water and ammonia. It produces both refrigeration and power simultaneously via a single heat source. Parametric analysis has been adopted to evaluate the thermodynamic parameters effects on the operation of the combined cycle where the Engineering Equation Solver (EES) is utilized. The obtained results show that environmental temperatures, heat source, refrigeration, inlet pressure, and temperature, and the density of the ammonia-water dilution have major effects on the exergy efficiency, the refrigeration output, and the net power of the system. In order to obtain the maximum exergy and thermal efficiencies, the optimization of the combined cycle has been performed via the genetic algorithm. [ABSTRACT FROM AUTHOR]

High temperature resistance and corrosion resistance make ceramic materials available for heat exchangers operating under high temperature or harsh chemical conditions. And the importance of the effect of flow maldistribution on thermal-hydraulic performance of heat exchangers has been demonstrated by a lot of researches. In this work, the nonuniformity of fluid flow is focused on to improve the performance of a microchannel ceramic heat exchanger. The inlet part of a microchannel ceramic heat exchanger is optimized using surrogate model coupled with genetic algorithm. Specifically, 30 sample points are designed by Latin hypercube sampling method and calculated by computational fluid dynamics. Radial basis neural network is established with the sample data and employed to predict the specific fluid flow distribution within design space as a surrogate model. Specifically, the surrogate model predicts the specific flow distribution instead of a single target value of nonuniformity in previous surrogate model. The results indicate that such a method has a significant advantage over the previous surrogate model. The genetic algorithm is implemented to search for the optimal point. The nonuniformity of fluid flow is reduced by 68.2% and pressure drop is increased by 6.6% by the optimization, which means the uniformity of fluid flow in the heat exchanger is improved significantly with just a little cost of pressure drop. [ABSTRACT FROM AUTHOR]