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A. Khosravi
Department of Mechanical Engineering, School of Engineering, Aalto University, Finland

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Journal article
Published: 22 June 2021 in Energy
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In this study, an energy and economic analysis of a novel hybrid system based on a biomass/solar system equipped with a multi-effect desalination unit is carried out. Part of the steam was directed into a heat exchanger to supply the required energy for the multi-effect desalination unit. The capacity of the system was 100 MWe. The proposed system is compared with a photovoltaic power plant, a solar dish/Stirling power plant, a biomass power plant, and a linear Fresnel Reflector solar power plant. The results demonstrate that a solar thermal collector can be considered a promising solution to prevail the problem of the increasing boiler temperature. It reduces the amount of fuel consumption throughout the year, resulting in reducing the levelized cost of energy. Increasing the solar irradiance augments the energy efficiency gains of a hybrid system. The comparison of the power plants showed that the proposed hybrid system had the minimum levelized cost of energy by 7.865 cents of dollar/kWh but has only slightly higher capital investment cost. Additionally, of the solar power plants studied, a photovoltaic system is more efficient for the study region, Natal-RN Brazil, by a levelized cost of energy of 10.45 cents of dollar/kWh.

ACS Style

A. Khosravi; A. Santasalo-Aarnio; S. Syri. Optimal Technology for a Hybrid Biomass/Solar System for Electricity Generation and Desalination in Brazil. Energy 2021, 234, 121309 .

AMA Style

A. Khosravi, A. Santasalo-Aarnio, S. Syri. Optimal Technology for a Hybrid Biomass/Solar System for Electricity Generation and Desalination in Brazil. Energy. 2021; 234 ():121309.

Chicago/Turabian Style

A. Khosravi; A. Santasalo-Aarnio; S. Syri. 2021. "Optimal Technology for a Hybrid Biomass/Solar System for Electricity Generation and Desalination in Brazil." Energy 234, no. : 121309.

Review
Published: 23 May 2021 in Sustainability
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ORC technology is one of the most promising technologies for the use of residual energy in the generation of electrical energy, offering simple and environmentally friendly alternatives. In this field, the selection of working fluids plays an important role in the operation of the cycle, whether in terms of the energy efficiency or the minimization of environmental impacts. Therefore, in this paper, a comprehensive review is presented on the use of R1234yf refrigerant and its mixtures as working fluids in ORC systems. These fluids are used in low- and medium-temperature applications for the use of residual energy generated from solar energy, geothermal energy, and internal combustion engines. It was concluded that R1234yf and its mixtures are competitive as compared with conventional refrigerants used in ORC.

ACS Style

Juan García-Pabón; Dario Méndez-Méndez; Juan Belman-Flores; Juan Barroso-Maldonado; Ali Khosravi. A Review of Recent Research on the Use of R1234yf as an Environmentally Friendly Fluid in the Organic Rankine Cycle. Sustainability 2021, 13, 5864 .

AMA Style

Juan García-Pabón, Dario Méndez-Méndez, Juan Belman-Flores, Juan Barroso-Maldonado, Ali Khosravi. A Review of Recent Research on the Use of R1234yf as an Environmentally Friendly Fluid in the Organic Rankine Cycle. Sustainability. 2021; 13 (11):5864.

Chicago/Turabian Style

Juan García-Pabón; Dario Méndez-Méndez; Juan Belman-Flores; Juan Barroso-Maldonado; Ali Khosravi. 2021. "A Review of Recent Research on the Use of R1234yf as an Environmentally Friendly Fluid in the Organic Rankine Cycle." Sustainability 13, no. 11: 5864.

Journal article
Published: 30 November 2020 in Energy
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The city of Espoo, Finland is planning to develop Kera as a green suburb with high level of energy efficiency and low CO2 emissions, using a high share of renewable energy and recycled or reused energy. For reaching this target, in this study, renewable energy resources such as solar, wind and waste heat are investigated for the study region. Two different technologies comprising heat pump (HP) and heat-only boiler (HOB) are investigated to retrieve waste heat from a data centre and LuxTurrim5G smart poles to use in a low-temperature district heating network. We investigate various scenarios to supply the required energy for the HP (which receives electricity from the electricity market, photovoltaic (PV) system, wind turbine (WT) and hybrid PV/WT; 4 scenarios) and HOB (which works with electricity, forest fuel wood, biogas, ammonia, wood pellets and industry wood residue; 6 scenarios). We found that the heat pump scenario is an efficient and cost-effective way to retrieve waste heat from the data centre and 5G smart poles with an LCOE of 3.192 ¢/kWh if electricity is produced by the PV system, and 3.516 ¢/kWh when the heat pump receives its electricity only from the electricity market.

ACS Style

A. Khosravi; T. Laukkanen; V. Vuorinen; S. Syri. Waste heat recovery from a data centre and 5G smart poles for low-temperature district heating network. Energy 2020, 218, 119468 .

AMA Style

A. Khosravi, T. Laukkanen, V. Vuorinen, S. Syri. Waste heat recovery from a data centre and 5G smart poles for low-temperature district heating network. Energy. 2020; 218 ():119468.

Chicago/Turabian Style

A. Khosravi; T. Laukkanen; V. Vuorinen; S. Syri. 2020. "Waste heat recovery from a data centre and 5G smart poles for low-temperature district heating network." Energy 218, no. : 119468.

Journal article
Published: 01 October 2020 in Energies
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In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of the TMP process under different operational states, the idea of machine learning algorithms is employed. Complicated processes and prediction problems could be simulated and solved by utilizing artificial intelligence methods inspired by the pattern of brain learning. In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. The applied optimization algorithms are particle swarm optimization algorithm (PSO), differential evolution (DE), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), ant colony (ACO), and teaching learning-based optimization algorithm (TLBO). The simulation predictor variables are site ambient temperature, refining dilution water, refining plate gap, and chip transfer screw speed, while the model outputs are refining motor load and generated steam. Findings confirm the superiority of the PSO algorithm concerning model performance comparing to the other evolutionary algorithms for optimizing ANFIS method parameters, which are utilized for simulating a refiner unit in the TMP process.

ACS Style

Behnam Talebjedi; Ali Khosravi; Timo Laukkanen; Henrik Holmberg; Esa Vakkilainen; Sanna Syri. Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method. Energies 2020, 13, 5113 .

AMA Style

Behnam Talebjedi, Ali Khosravi, Timo Laukkanen, Henrik Holmberg, Esa Vakkilainen, Sanna Syri. Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method. Energies. 2020; 13 (19):5113.

Chicago/Turabian Style

Behnam Talebjedi; Ali Khosravi; Timo Laukkanen; Henrik Holmberg; Esa Vakkilainen; Sanna Syri. 2020. "Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method." Energies 13, no. 19: 5113.

Journal article
Published: 16 September 2020 in Journal of Cleaner Production
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For geothermal energy system, the low geothermal well operating lifetime and temperature is one of the main obstacles. Also, finding the optimum design parameters and operating conditions requires many experimental tests and intricate mathematical models. Besides, improving the energy efficiency and lacking technical feasibility of the combined geothermal systems are other challenges concerning geothermal systems. To cover the mentioned challenges, in the current study, a hybrid geothermal/absorption refrigeration system (ARS) incorporated with solar thermal collector, desalination unit and hydrogen storage system is designed and assessed. The proposed system is investigated by developing two methods of artificial intelligence (AI) as well as thermodynamic model. The intelligent methods are multilayer perceptron (MLP) neural network optimized with imperialist competitive algorithm (ICA), MLP-ICA, and MLP optimized with genetic algorithm (GA), MLP-GA. These methods are manufactured based on the solar irradiance, cooling water temperature difference, ambient temperature, pinch-point temperature, evaporating and condensing temperatures as independent parameters. These parameters are utilized to obtain the power generation, coefficient of performance of the ARS (COPchiller), heat exchanger area of the ARS, and cycle thermal efficiency. The obtained results show that simulation of the system by MLP-ICA was successfully carried out and this model operates substantially better than the MLP-GA for simulating the behavior of the system. Also, the payback time for the proposed system (with the interest rate of 3%) was obtained around 8 years.

ACS Style

A. Khosravi; S. Syri. Modeling of geothermal power system equipped with absorption refrigeration and solar energy using multilayer perceptron neural network optimized with imperialist competitive algorithm. Journal of Cleaner Production 2020, 276, 124216 .

AMA Style

A. Khosravi, S. Syri. Modeling of geothermal power system equipped with absorption refrigeration and solar energy using multilayer perceptron neural network optimized with imperialist competitive algorithm. Journal of Cleaner Production. 2020; 276 ():124216.

Chicago/Turabian Style

A. Khosravi; S. Syri. 2020. "Modeling of geothermal power system equipped with absorption refrigeration and solar energy using multilayer perceptron neural network optimized with imperialist competitive algorithm." Journal of Cleaner Production 276, no. : 124216.

Review article
Published: 20 June 2020 in International Journal of Refrigeration
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HFC refrigerants as R134a, R404A, R407C and R410A are commonly used in heating, air conditioning and refrigeration (HACR) systems since Kyoto protocol. However, they are phased-out due to their high global warming potential (GWP). There are various options to replace high GWP refrigerants, among all hydrofluorolefin (HFO) fluids, such as R1234yf, represents an excellent alternative. With GWP <1, R1234yf is a promising substitute for R134a. This paper presents the most relevant researches concerning the application of R1234yf in the last decade. This review paper regroups experimental and theoretical studies which assess the performance of pure R1234yf as working fluid of the compression systems such as mobile and residential air conditioning, air and water heat pump, domestic refrigerator and freezer. Studies depict that R1234yf can be recommended for small-scale systems instead of R134a, but an optimization process is necessary to achieve the optimum operating conditions.

ACS Style

Juan J.G. Pabon; Ali Khosravi; J.M. Belman-Flores; Luiz Machado; Remi Revellin. Applications of refrigerant R1234yf in heating, air conditioning and refrigeration systems: A decade of researches. International Journal of Refrigeration 2020, 118, 104 -113.

AMA Style

Juan J.G. Pabon, Ali Khosravi, J.M. Belman-Flores, Luiz Machado, Remi Revellin. Applications of refrigerant R1234yf in heating, air conditioning and refrigeration systems: A decade of researches. International Journal of Refrigeration. 2020; 118 ():104-113.

Chicago/Turabian Style

Juan J.G. Pabon; Ali Khosravi; J.M. Belman-Flores; Luiz Machado; Remi Revellin. 2020. "Applications of refrigerant R1234yf in heating, air conditioning and refrigeration systems: A decade of researches." International Journal of Refrigeration 118, no. : 104-113.

Journal article
Published: 20 May 2020 in Energy
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Finland has recently adopted a high profile in climate change mitigation. Finland has declared a national target of achieving carbon neutrality by 2035. As a part of this, the use of coal for energy purposes has been banned from year 2029 onwards. The Finnish electricity system is already very low-carbon, and more wind and nuclear power is being constructed. However, District heating (DH) is a backbone of the Finnish energy system, and it is still quite reliant on fossil fuels and domestic high-emission fuel peat, their share being 51% of DH fuels in 2018. This paper models the impacts of this transition on the electricity markets and DH systems and develops scenarios with a large-scale transition to wind and nuclear power and heat pumps in DH systems. The study finds that large-scale introduction of heat pumps would be profitable in cities Helsinki, Espoo, Turku and Vantaa, especially with the planned decrease of electricity tax. The study indicates that the impacts on Winter time capacity adequacy could be managed, but this requires considerable increases in nuclear and wind capacity.

ACS Style

A. Khosravi; V. Olkkonen; A. Farsaei; S. Syri. Replacing hard coal with wind and nuclear power in Finland- impacts on electricity and district heating markets. Energy 2020, 203, 117884 .

AMA Style

A. Khosravi, V. Olkkonen, A. Farsaei, S. Syri. Replacing hard coal with wind and nuclear power in Finland- impacts on electricity and district heating markets. Energy. 2020; 203 ():117884.

Chicago/Turabian Style

A. Khosravi; V. Olkkonen; A. Farsaei; S. Syri. 2020. "Replacing hard coal with wind and nuclear power in Finland- impacts on electricity and district heating markets." Energy 203, no. : 117884.

Journal article
Published: 14 April 2020 in Energies
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Finland has adopted a high profile in climate change mitigation. A national target of achieving carbon neutrality by 2035 has been declared. As a part of this, the use of coal for energy purposes has been banned from May 2029 onwards. The Nordic electricity market was a world fore-runner in creating a liberalized, multi-national electricity market in the 1990s. At present, the electricity systems of Finland, Sweden, and Norway are already very low-carbon. The Baltic countries Estonia, Latvia, and Lithuania joined the Nordic market about a decade ago. Estonian electricity production is the most carbon-intensive of all the EU countries due to the extensive use of domestic oil shale. Especially Lithuania still suffers from capacity deficit created by the closure of the Soviet time nuclear reactor Ignalina in Lithuania. This paper presents the ambitions of the EU and national level energy and climate policies and models the multi-national impacts of Finland’s forthcoming closure of coal-fired generation. We also take into account Sweden’s planned decrease in nuclear generation. We find that these national-level policies have an impact on the Baltic countries as reduced import possibilities and increasing electricity prices, and the expected rise of the EU CO2 allowance prices amplifies these. We further find that the abandonment of coal and nuclear power plants increases the net import and increases CO2 emissions in neighboring regions.

ACS Style

Anahita Farsaei; Sanna Syri; Ville Olkkonen; Ali Khosravi. Unintended Consequences of National Climate Policy on International Electricity Markets—Case Finland’s Ban on Coal-Fired Generation. Energies 2020, 13, 1930 .

AMA Style

Anahita Farsaei, Sanna Syri, Ville Olkkonen, Ali Khosravi. Unintended Consequences of National Climate Policy on International Electricity Markets—Case Finland’s Ban on Coal-Fired Generation. Energies. 2020; 13 (8):1930.

Chicago/Turabian Style

Anahita Farsaei; Sanna Syri; Ville Olkkonen; Ali Khosravi. 2020. "Unintended Consequences of National Climate Policy on International Electricity Markets—Case Finland’s Ban on Coal-Fired Generation." Energies 13, no. 8: 1930.

Journal article
Published: 01 January 2020 in Energy Engineering
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The energy coming from solar radiation could be harvested and transformed into electricity through the use of solar-thermal power generation and photovoltaic (PV) power generation. Placement of solar collectors (thermal and photovoltaic) affects the amount of incoming radiation and the absorption rate. In this research, new correlations for finding the monthly optimum slope angle (OSA) on flat-plate collectors are proposed. Twelve equations are developed to calculate the monthly OSA by the linear regression model, for the northern and the southern hemisphere stations from 15° to 55° and –20° to –45°, respectively. Also, a new equation for calculating the yearly tilt angle is developed and compared with several other calculation methods from the literature. Results confirm a 20% increase in solar energy absorption by adjusting the collectors’ tilt angle in monthly time periods. This is while the adjusted collectors with the yearly optimum slope angle receive approximately 7% higher solar radiation compared to the horizontal collectors. Furthermore, the proposed equations outperformed the other calculation methods in the literature.

ACS Style

Ali Khosravi; Oscar Ricardo Sandoval Rodriguez; Behnam Talebjedi; Timo Laukkanen; Juan Jose Garcia Pabon; Mamdouh El Haj Assad. New Correlations for Determination of Optimum Slope Angle of Solar Collectors. Energy Engineering 2020, 117, 249 -265.

AMA Style

Ali Khosravi, Oscar Ricardo Sandoval Rodriguez, Behnam Talebjedi, Timo Laukkanen, Juan Jose Garcia Pabon, Mamdouh El Haj Assad. New Correlations for Determination of Optimum Slope Angle of Solar Collectors. Energy Engineering. 2020; 117 (5):249-265.

Chicago/Turabian Style

Ali Khosravi; Oscar Ricardo Sandoval Rodriguez; Behnam Talebjedi; Timo Laukkanen; Juan Jose Garcia Pabon; Mamdouh El Haj Assad. 2020. "New Correlations for Determination of Optimum Slope Angle of Solar Collectors." Energy Engineering 117, no. 5: 249-265.

Journal article
Published: 28 November 2019 in Soil and Tillage Research
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Estimation of soil temperature (ST) as one of the vital parameters of soil, which has an impact on many chemical and physical characteristics of soil, is of great importance in soil science. This study applies a time series-based model, namely fractionally autoregressive integrated moving average (FARIMA), as well as two machine learning-based models consisting of feed-forward back propagation neural networks (FFBPNN) and gene expression programming (GEP) for daily ST estimation. In doing so, the daily ST data of three stations at four depths (5, 10, 50, and 100 cm) in Iran were used for the time period from 1998 to 2017. Studied stations were selected from different climates including arid (Isfahan station), semi-arid (Urmia station), and very humid (Rasht station) to evaluate the performance of models and generalize the outcomes in different climate classes. The performances of the developed models are evaluated via three statistical metrics including the root mean square error (RMSE), mean absolute error (MAE), and relative RMSE (RRMSE). Results obtained demonstrated that the machine learning-based FFBPNN and GEP models performed better than the time series-based FARIMA approach at all depths. As a result, negligible differences were observed between the accuracies of FFBPNN and GEP. In addition, this study developed novel hybrid models through combining the FFBPNN and GEP techniques with the FARIMA to enhance the accuracy of traditional FARIMA, FFBPNN, and GEP. The developed hybrid models named GEP-FARIMA and FFBPNN-FARIMA were found to achieve better estimates of daily ST data at different depths in comparison with the classical models. The daily ST estimates with the highest accuracy were observed at a depth of 50 cm via the GEP-FARIMA at Isfahan station (RMSE = 0.05 °C, MAE = 0.03 °C, RRMSE = 0.25% for the testing phase), the GEP-FARIMA at Urmia station (RMSE = 0.04 °C, MAE = 0.03 °C, RRMSE = 0.26% for the testing phase), and the FFBPNN-FARIMA at Rasht station (RMSE = 0.07 °C, MAE = 0.05 °C, RRMSE = 0.35% for the testing phase).

ACS Style

Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Ali Khosravi. Developing novel hybrid models for estimation of daily soil temperature at various depths. Soil and Tillage Research 2019, 197, 104513 .

AMA Style

Saeid Mehdizadeh, Farshad Fathian, Mir Jafar Sadegh Safari, Ali Khosravi. Developing novel hybrid models for estimation of daily soil temperature at various depths. Soil and Tillage Research. 2019; 197 ():104513.

Chicago/Turabian Style

Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Ali Khosravi. 2019. "Developing novel hybrid models for estimation of daily soil temperature at various depths." Soil and Tillage Research 197, no. : 104513.

Research article
Published: 17 October 2019 in Journal of Cleaner Production
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Determining the optimal sizing of a solar power tower system (SPTS) with a thermal energy storage system is subject to finding the optimum values of design parameters including the solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours. These design parameters are determined for each station separately and have remarkable effects on the thermo-economic performance of the system. This paper aims to demonstrate how artificial intelligence (AI) techniques may play an important role in addressing the above-mentioned need and help determine the optimum design parameters for different stations. For this purpose, we developed a thermo-economic model of a 100 MW SPTS with a molten salt storage system for five stations (two stations in India, and one each in Bangladesh, Pakistan, and Afghanistan). A method-based AI is utilized in this paper to ascertain the design parameters of the system. Additionally, a novel hybrid method based on adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching-learning-based optimization algorithm (ANFIS-GATLBO) is employed. The input parameters are latitude, longitude, design point DNI and SM, while the annual energy produced, levelized cost of energy and capacity factor are the target variables. The results of the study show that although the annual energy produced by SPTS rises by increasing the SM and decreasing design point DNI, optimum design parameters should be determined by the economic factors. In addition, it was found that the ANFIS-GATLBO method used in this study successfully predicted the targets with a correlation coefficient close to 1.

ACS Style

A. Khosravi; Mohammad Malekan; J.J.G. Pabon; X. Zhao; Mamdouh El Haj Assad. Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm. Journal of Cleaner Production 2019, 244, 118904 .

AMA Style

A. Khosravi, Mohammad Malekan, J.J.G. Pabon, X. Zhao, Mamdouh El Haj Assad. Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm. Journal of Cleaner Production. 2019; 244 ():118904.

Chicago/Turabian Style

A. Khosravi; Mohammad Malekan; J.J.G. Pabon; X. Zhao; Mamdouh El Haj Assad. 2019. "Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm." Journal of Cleaner Production 244, no. : 118904.

Journal article
Published: 23 September 2019 in Applied Thermal Engineering
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Solar energy is among the cleanest and most adaptable compared to other renewable energy sources. The major challenge is how to get this energy in efficient way to make it available for industrial applications such as electricity generation. One of the most efficient techniques to harvest solar energy and transform it into electrical energy is parabolic trough solar collector (PTSC), which is a type of concentrating solar power generation systems. This system operates by concentrating solar irradiance onto a tubular receiver in which this centralized energy is absorbed by a heat transfer fluid and transported to the power cycle. Improving the performance of the PTSC can enhance efficiency as well as power generation of a PTS power plant. Hence, this issue has been considered as one of the major challenges for scholars in this field. One promising solution is finding more efficient heat transfer working fluids. Another suggestion is proposing a different geometry for the receiver. In the current research, ferrofluids due to their heat transfer characteristics are proposed as working fluid for a PTSC. Fe3O4/Therminol 66 and CuO/Therminol 66 nanofluids are examined under external magnetic field for this target. Besides, to improve the heat transfer characteristics of the collector, the receiver is designed with internal fins. This work is carried out using computational fluid dynamics (CFD). The assessments are done by considering the different nanoparticle sizes on the friction factor, thermal efficiency, performance evaluation criteria (PEC) and convective heat transfer. The results depict that reducing the particle size and enhancing the nanoparticles volume fraction increase the convective heat transfer coefficient, Nusselt number, PEC and the collector efficiency. In addition, the collector efficiency rises in the attendance of the magnetic field and maximum efficiency of the collector was obtained for 4% Fe3O4/Therminol 66 working fluid.

ACS Style

Mohammad Malekan; Ali Khosravi; Sanna Syri. Heat transfer modeling of a parabolic trough solar collector with working fluid of Fe3O4 and CuO/Therminol 66 nanofluids under magnetic field. Applied Thermal Engineering 2019, 163, 114435 .

AMA Style

Mohammad Malekan, Ali Khosravi, Sanna Syri. Heat transfer modeling of a parabolic trough solar collector with working fluid of Fe3O4 and CuO/Therminol 66 nanofluids under magnetic field. Applied Thermal Engineering. 2019; 163 ():114435.

Chicago/Turabian Style

Mohammad Malekan; Ali Khosravi; Sanna Syri. 2019. "Heat transfer modeling of a parabolic trough solar collector with working fluid of Fe3O4 and CuO/Therminol 66 nanofluids under magnetic field." Applied Thermal Engineering 163, no. : 114435.

Journal article
Published: 10 September 2019 in Energy Conversion and Management
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Solar energy is a renewable energy resources that is available across the world. A solar dish/Stirling system means a parabolic dish concentrator and a Stirling engine combined to generate mechanical and/or electrical output power. In this system, the input energy of Stirling engine is provided by sunlight as a source of heat. This study presents the effect of different variables on the power generation and efficiency of the system. In addition, artificial intelligence approach is employed to model a solar dish/Stirling system. For this target, a huge dataset was provided by considering a wide range of input variables. The intelligent methods are group method of data handling (GMDH) type neural network, adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) neural network (ANN). The MLP and ANFIS are optimized with particle swarm optimization (PSO) and genetic algorithm (GA). The intelligent methods are trained with inputs and targets. The considered input parameters are the ratio of focal point to dish diameter, hour of day, solar radiation, geometric concentration factor and working gas specific constant. The power generation, global efficiency, heat used to run the Stirling cycle, hot Stirling chamber temperature and engine speed are selected to be the targets. The results depict that the intelligent methods operate successfully for energy modeling of the solar dish/Stirling system and the statistical indicators illustrate that the ANFIS-PSO method performs better than the other developed methods.

ACS Style

Ali Khosravi; Sanna Syri; Juan J.G. Pabon; Oscar R. Sandoval; Bryan Castro Caetano; Miguel H. Barrientos. Energy modeling of a solar dish/Stirling by artificial intelligence approach. Energy Conversion and Management 2019, 199, 112021 .

AMA Style

Ali Khosravi, Sanna Syri, Juan J.G. Pabon, Oscar R. Sandoval, Bryan Castro Caetano, Miguel H. Barrientos. Energy modeling of a solar dish/Stirling by artificial intelligence approach. Energy Conversion and Management. 2019; 199 ():112021.

Chicago/Turabian Style

Ali Khosravi; Sanna Syri; Juan J.G. Pabon; Oscar R. Sandoval; Bryan Castro Caetano; Miguel H. Barrientos. 2019. "Energy modeling of a solar dish/Stirling by artificial intelligence approach." Energy Conversion and Management 199, no. : 112021.

Journal article
Published: 28 August 2019 in Applied Thermal Engineering
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Compressed air energy storage (CAES) is a hopeful technology to overcome the intermittency of renewable energy systems and meet the high peak load demand. The objective of this study is to propose a double pipe heat exchanger (DPHX) working with CuO/water nanofluid in order to cool the compressed air before cavern in a small-scale CAES system. A new design of DPHX by considering different internal tube geometry (nine configurations) is proposed. To achieve these targets, a transient model for simulating the technical demeanor of the CAES system is developed. After simulating the behavior of the CAES system, DPHX is modeled by computational fluid dynamics (CFD) to evaluate the outcome of nanofluid as well as geometry design on the DPHX performance. The pressure drop is unchanged for all finned tube at higher Reynolds numbers. The numerical analysis through mathematical modeling of the charging process of the cavern denotes the effect of length and mass flow rate of the secondary fluid in the DPHX. The results illustrate that by enhancing the mass flow of the secondary fluid, the cavern temperature declines. The pressure inside the cavern has a small dependence on its temperature. The cavern pressure is invariant by increasing the secondary fluid flow. For proposed DPHX, the convective heat transfer coefficient increased up to 22% for cold fluid considering tube with four fins (air/nanofluid+ finned tube (w=3.5 mm and H=1.0 mm)) and compared to the smooth tube. In addition, around 17% enhancement in convective heat transfer coefficient was achieved using tube with eight fins and with air/nanofluid as the working fluid (case with w=3.5 mm and H=1.0 mm), compared to tube with four fins. This shows the capability of the proposed finned tube along with the utilization of the nanofluid to increase the heat exchanger performance.

ACS Style

A. Khosravi; H. Campos; M. Malekan; R.O. Nunes; M.E.H. Assad; L. Machado; J.J. Garcia Pabon. Performance improvement of a double pipe heat exchanger proposed in a small-scale CAES system: An innovative design. Applied Thermal Engineering 2019, 162, 114315 .

AMA Style

A. Khosravi, H. Campos, M. Malekan, R.O. Nunes, M.E.H. Assad, L. Machado, J.J. Garcia Pabon. Performance improvement of a double pipe heat exchanger proposed in a small-scale CAES system: An innovative design. Applied Thermal Engineering. 2019; 162 ():114315.

Chicago/Turabian Style

A. Khosravi; H. Campos; M. Malekan; R.O. Nunes; M.E.H. Assad; L. Machado; J.J. Garcia Pabon. 2019. "Performance improvement of a double pipe heat exchanger proposed in a small-scale CAES system: An innovative design." Applied Thermal Engineering 162, no. : 114315.

Journal article
Published: 10 May 2019 in International Journal of Refrigeration
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Void fraction is an important parameter to design and simulate thermal systems involving two-phase flows. In this research, an experimental investigation of the void fraction in adiabatic two-phase flow of R1234yf in horizontal and smooth tubes with an internal diameter of 4.8 mm was carried out. For the experimental tests, the vapor quality ranges from 0.1 to 1 while two saturation temperatures (15 and 25°C) and two mass flow rates (180 and 280 kg.m−2s−1) are investigated. The quick-closing valve(s) method is used to measure the volumetric void fraction. Tests are also undertaken with R134a used as a reference in this study. The results highlight that the void fraction of R1234yf is 5% lower than that for R134a. In addition, the experimental data of R1234yf were compared against seven correlations from the literature: the Baroczy and the Hughmark correlations were shown to provide the best prediction, with a mean absolute error of 2% and 3.2%, respectively.

ACS Style

Juan J. Garcia Pabon; Leandro C. Pereira; Gleberson Humia; Ali Khosravi; Rémi Revellin; Jocelyn Bonjour; Luiz Machado. Experimental study on the void fraction during two-phase flow of R1234yf in smooth horizontal tubes. International Journal of Refrigeration 2019, 104, 103 -112.

AMA Style

Juan J. Garcia Pabon, Leandro C. Pereira, Gleberson Humia, Ali Khosravi, Rémi Revellin, Jocelyn Bonjour, Luiz Machado. Experimental study on the void fraction during two-phase flow of R1234yf in smooth horizontal tubes. International Journal of Refrigeration. 2019; 104 ():103-112.

Chicago/Turabian Style

Juan J. Garcia Pabon; Leandro C. Pereira; Gleberson Humia; Ali Khosravi; Rémi Revellin; Jocelyn Bonjour; Luiz Machado. 2019. "Experimental study on the void fraction during two-phase flow of R1234yf in smooth horizontal tubes." International Journal of Refrigeration 104, no. : 103-112.

Journal article
Published: 09 May 2019 in Applied Thermal Engineering
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Heat management by means of two-phase mechanically pumped loop (TMPL) possesses promising advantages such as long-distance heat transport, high heat density and good temperature control. Knowledge of the fluid charge has great importance for TMPL system efficiency. The present work is a theoretical/experimental study on the refrigerant charge in TMPL system using R134a and R1234yf refrigerants. The mass of refrigerant in the TMPL system was measured for several cooling capacities. The effects of vapor quality in the evaporator (varying from 0.1 to 1), mass flux (300kg.s-1m-2 and 400kg.s-1m-2), and saturation temperature (25°C and 30°C) on refrigerant charge in the TMPL were experimentally investigated. About 70 experimental points were analyzed and compared with a theoretical model employing eight different prediction tools for the determination of the void fraction. The best results were obtained by Zivi and Hughmark correlations with mean absolute errors of 12% and 13%, respectively.

ACS Style

Leandro Pereira; Gleberson Humia; Ali Khosravi; Rémi Revellin; Jocelyn Bonjour; Luiz Machado; Juan J. Garcia Pabon. A study on the fluid refrigerant charge in a two-phase mechanically pumped loop system using R134a and R1234yf. Applied Thermal Engineering 2019, 158, 113727 .

AMA Style

Leandro Pereira, Gleberson Humia, Ali Khosravi, Rémi Revellin, Jocelyn Bonjour, Luiz Machado, Juan J. Garcia Pabon. A study on the fluid refrigerant charge in a two-phase mechanically pumped loop system using R134a and R1234yf. Applied Thermal Engineering. 2019; 158 ():113727.

Chicago/Turabian Style

Leandro Pereira; Gleberson Humia; Ali Khosravi; Rémi Revellin; Jocelyn Bonjour; Luiz Machado; Juan J. Garcia Pabon. 2019. "A study on the fluid refrigerant charge in a two-phase mechanically pumped loop system using R134a and R1234yf." Applied Thermal Engineering 158, no. : 113727.

Journal article
Published: 18 March 2019 in Geothermics
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Geothermal energy is a renewable resource that is constantly available. The low geothermal well operating lifetime is the main challenge in using this type of renewable energy. This problem can be covered by the aid of solar system (hybrid system). For complicated renewable energy systems, finding the optimum design parameters and operating conditions require to develop experimental apparatus or sophisticated thermodynamic models. Hence, in this study, artificial intelligence (AI) approach is proposed for modeling the geothermal organic Rankin cycle (GORC) equipped with solar thermal unit. Indeed, the current study depicts how AI methods can meticulously simulate the operation of a complicated renewable energy system. The developed intelligent methods are adaptive neuro-fuzzy inference system (ANFIS) optimized with particle swarm optimization (PSO) algorithm (ANFIS-PSO) and multilayer perceptron (MLP) neural network optimized with PSO algorithm (MLP-PSO). The models are composed based on the main design parameters of the geothermal system that are solar radiation, well temperature, working fluid mass flow rate, turbine output pressure, surface area of the solar collector and preheater inlet pressure. The intelligent models use the mentioned input variables to predict the net power output, energy efficiency, exergy efficiency and levelized cost of energy (LCOE) of the GORC. Energy, exergy and economic analyses are carried out for the low global warming potential (GWP) refrigerants. It was found out that although the intelligent models can meticulously predict the targets, ANFIS-PSO performs better than MLP-PSO for modeling the GORC with solar system. Root mean square error of this model for prediction of power generation, energy efficiency, exergy efficiency and LCOE was 12.023 (kW), 3.587 ×10-4, 3.278 ×10-4 and 1.332 ×10-4, respectively.

ACS Style

A. Khosravi; S. Syri; X. Zhao; Mamdouh El Haj Assad. An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system. Geothermics 2019, 80, 138 -154.

AMA Style

A. Khosravi, S. Syri, X. Zhao, Mamdouh El Haj Assad. An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system. Geothermics. 2019; 80 ():138-154.

Chicago/Turabian Style

A. Khosravi; S. Syri; X. Zhao; Mamdouh El Haj Assad. 2019. "An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system." Geothermics 80, no. : 138-154.

Journal article
Published: 07 November 2018 in Renewable Energy
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A parabolic trough is defined as a type of solar thermal collector that is straight in one dimension and curved as a parabola in the other two, lined with a polished metal mirror. Enhancing the thermal efficiency of this collectors is one of the major challenges of developing and growing of parabolic trough solar thermal power plants. Ferrofluids were proposed as a novel working fluid for industrial applications, due to their thermal performances. In this study, the convective heat transfer of Fe3O4-Therminol 66 ferrofluid under magnetic field (0-500 G) is evaluated using computational fluid dynamics. The ferrofluid with different volume fraction (1-4%) and the Therminol 66 (as the base fluid) are considered as the working fluids for a parabolic trough solar collector. Numerical analysis first validated using theoretical results, and then a detailed study is conducted in order to analyze the effect of the magnetic field on different parameters. The result demonstrated that using magnetic field can increase the local heat transfer coefficient of the collector tube, thermal efficiency as well as output temperature of the collector. In addition, increasing the volume fraction of nanoparticle in the base fluid and intensity of magnetic field increased the collector performance.

ACS Style

Ali Khosravi; Mohammad Malekan; Mamdouh El Haj Assad. Numerical analysis of magnetic field effects on the heat transfer enhancement in ferrofluids for a parabolic trough solar collector. Renewable Energy 2018, 134, 54 -63.

AMA Style

Ali Khosravi, Mohammad Malekan, Mamdouh El Haj Assad. Numerical analysis of magnetic field effects on the heat transfer enhancement in ferrofluids for a parabolic trough solar collector. Renewable Energy. 2018; 134 ():54-63.

Chicago/Turabian Style

Ali Khosravi; Mohammad Malekan; Mamdouh El Haj Assad. 2018. "Numerical analysis of magnetic field effects on the heat transfer enhancement in ferrofluids for a parabolic trough solar collector." Renewable Energy 134, no. : 54-63.

Journal article
Published: 01 October 2018 in Applied Thermal Engineering
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Globally, the integration of renewable energy (which has an intermittent nature) into the power system requires the system operators to improve the system performance to be able to effectively handle the variations of the power production in order to balance the supply and demand. This problem is seen as a major obstacle to the expansion of renewable energy if it is not handled in a suitable way. Efficient electricity storage technology is one of the feasible solutions. The current study proposes Fe3O4/water nanofluid under magnetic field as the secondary fluid in the proposed double pipe heat exchanger before the cavern. The heat of compressed air is absorbed by the secondary fluid and it is stored in an isolation tank. This stored fluid is used to warm up the air that leaves the cavern for expanding in the turbine. The results demonstrated that increasing the mass flow rate of secondary fluid decreases the cavern temperature. Also, the value of convective heat transfer of ferrofluid increases when the volume fraction of nanoparticle as well as magnetic field increases. Furthermore, increasing the volume fraction and magnetic field increases the pressure drop and friction factor of ferrofluid.

ACS Style

Mohammad Malekan; Ali Khosravi; Xiaowei Zhao. The influence of magnetic field on heat transfer of magnetic nanofluid in a double pipe heat exchanger proposed in a small-scale CAES system. Applied Thermal Engineering 2018, 146, 146 -159.

AMA Style

Mohammad Malekan, Ali Khosravi, Xiaowei Zhao. The influence of magnetic field on heat transfer of magnetic nanofluid in a double pipe heat exchanger proposed in a small-scale CAES system. Applied Thermal Engineering. 2018; 146 ():146-159.

Chicago/Turabian Style

Mohammad Malekan; Ali Khosravi; Xiaowei Zhao. 2018. "The influence of magnetic field on heat transfer of magnetic nanofluid in a double pipe heat exchanger proposed in a small-scale CAES system." Applied Thermal Engineering 146, no. : 146-159.

Journal article
Published: 15 May 2018 in International Journal of Refrigeration
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This study is developed to evaluate the possibility of R1234yf to be a drop-in replacement for a pre-designed system with R134a, including the characterization of control system in a vapor compression system. The control algorithms are required for regulating the refrigerant flow rate into the evaporator and their control parameters depend intrinsically on refrigerant fluid. This paper presents a comparison of the effects of R134a and R1234yf refrigerants on the transient response of an evaporator operating with water as secondary fluid (counter-flow). The dynamic heat exchanger distributed model combines principles of thermodynamics, heat and mass transfer with empirical correlations, which are proposed in the literature, in order to compute the void fraction, pressure drop and heat transfer in two-phase flow. Experimental tests are carried out on a fixed refrigeration system for cooling capacity less than 3 kW, with constant condensation conditions, variable evaporating temperature and compressor speed to validate the model. The results demonstrated that a good agreement between simulation results and experimental data was achieved. The evaporation temperature data were within ± 1°C error band. Furthermore, comparative simulations between the refrigerants R-134a and R-1234yf exhibited a similar dynamic behavior during a step at the inlet mass flow rate, and the gain and time constant for superheat control were not considerably modified. Therefore, the refrigerant changing does not demand many changes in the expansion device control strategy.

ACS Style

Juan Garcia; Thiago Ali; Willian Moreira Duarte; Ali Khosravi; Luiz Machado. Comparison of transient response of an evaporator model for water refrigeration system working with R1234yf as a drop-in replacement for R134a. International Journal of Refrigeration 2018, 91, 211 -222.

AMA Style

Juan Garcia, Thiago Ali, Willian Moreira Duarte, Ali Khosravi, Luiz Machado. Comparison of transient response of an evaporator model for water refrigeration system working with R1234yf as a drop-in replacement for R134a. International Journal of Refrigeration. 2018; 91 ():211-222.

Chicago/Turabian Style

Juan Garcia; Thiago Ali; Willian Moreira Duarte; Ali Khosravi; Luiz Machado. 2018. "Comparison of transient response of an evaporator model for water refrigeration system working with R1234yf as a drop-in replacement for R134a." International Journal of Refrigeration 91, no. : 211-222.