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• The optimization of 10 important design parameters of a GCPV plant is introduced. • The IRR objective function includes both technical and economic aspects. • Genetic algorithm is suitable and effective to find the global optimum. • Optimal configurations are presented for six locations worldwide. • The effects of decreasing PV module costs on optimal plant design are analysed. The increasing penetration of photovoltaic (PV) technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. This paper presents a simultaneous optimization method of ten important design parameters of a PV plant, including the module power, inverter sizing, support structure dimensions, cable losses, module orientation and row spacing. A mathematical PV performance model taking into account the important effects and losses and an economic cost model were developed and presented in detail. The objective function is the internal rate of return and the optimization is performed by a genetic algorithm. The results show that the proposed models and method are capable to optimize the grid-connected PV plant and provide reliable results after a 6–7 min calculation time. The method was demonstrated in detail for a Hungarian location, including the losses and cost structure of the optimal plant configuration. The optimization was also performed for 5 additional sites around the world to assess the effect of location and meteorology. The impact of the decreasing PV module prices on the optimal design is calculated to identify the expected future trends in PV plant design. The presented optimization method can be utilized to facilitate the optimal design of commercial PV plants and for research purposes. [ABSTRACT FROM AUTHOR]

• Environmental footprint methodology is applied to a utility-scale solar energy plant. • A case study of a grid-connected 20 MWp photovoltaic system in Hungary is presented. • Impacts on climate, human health, ecosystem and natural resources is assessed. • The environmental footprint of electricity is reduced by 75% compared to the grid mix. • A simplified life-cycle model linked to plant design parameters is validated. Balance-of-system components represent a growing share of environmental impacts in photovoltaic utility-scale solar energy systems. There are, however, no effective methods to carry out screening type life-cycle assessment during the planning phase of the power plant to minimize these impacts. This paper uses the European Commission's novel product environmental footprint methodology to explore the life-cycle impacts of a case study in Hungary and applies two life-cycle models with different system boundaries and assumptions. The first model represents a full-fledged assessment with a detailed inventory, while the other, simplified model can be directly linked to the main plant design parameters. Our results show that: a) the production of photovoltaic modules account for around half of the total aggregated environmental impacts, b) compared to the Hungarian grid mix, the new power plant reduces the environmental footprint by 75%. It is also shown that the simplified model captures the overwhelming majority of impacts, thus, it is applicable during the eco-design of similar systems. [ABSTRACT FROM AUTHOR]

Hydrogenation of CO 2 to hydrocarbons in fixed-bed and annular reactors (AR) can be limited via problems associated with high water production as the main by-product. Selective in-situ water removal using a hydrophilic membrane can be a promising solution for enhancing reactor performance. To this aim, a one-dimensional heterogeneous model comprising mass and heat transfers in the shell and tube of a membrane reactor (MR) is proposed. Firstly, the performance of different rector configurations exhibiting similar cross sectional areas and volumes are compared. Afterwards, influential factors affecting the MR performance such as shell/tube temperature, sweep ratio (θ) and pressure ratio (φ) are investigated thoroughly. Results show that increasing initial tube/shell temperature has positive effect on total hydrocarbons yield. However, sharp and sudden temperature elevation (hot spot) due to large extent of water removal, may have detrimental effects on catalyst performance. Moreover, it is observed that increasing θ and φ alter products distribution due to the equilibrium displacement and results in the lack of H 2 for further reactions. In addition, kinetic parameters corresponding to the inhibiting effect of water are indicated to have significant roles in hydrocarbons distribution. Therefore, water removal impose various changes, which cannot be considered independently in analyzing the MR performance. Image 1 • Modeling of CO 2 hydrogenation in membrane reactor is presented. • Effect of in-situ water removal on the reactor performance is analyzed. • Hydrocarbons distribution and reactor temperature are assessed in AR and MR. • Effect of sweep ratio and sweep pressure on products yield are studied. [ABSTRACT FROM AUTHOR]

Abstract The thermal performance of the evacuated tube solar collector with WO 3 /Water Nanofluid was studied in this paper. The WO 3 nanoparticles were spherical with 90 nm diameter Three different volume fraction of WO 3 nanoparticles of 0.014%, 0.028%, and0.042% were examined at several mass flux rates of 0.013 kg/s.m2, 0.015 kg/s.m2 and 0.017 kg/s.m2. The stability of the nanofluid was checked. Experiments were performed in Budapest, Hungary on the latitude of 47°28′N and longitude of 19°03′E. The results showed that the temperature difference of the fluid increased up to 21% with adding WO 3 nanoparticles. The maximum heat gain at the solar irradiance of 900 W/m2 was raised up to 23% when WO 3 nanoparticles were used. The proportion of the growth of the heat removable factor for nanofluids comparing to water at the same mass flux rate is between 1.05 and 1.16. The results indicated that the efficiency of the evacuated tube solar collector enhanced with more nanoparticles added. The thermal-optical efficiency of the evacuated tube solar collector reached 72.8%. Highlights • A good stability WO 3 /water nanofluid was prepared at different volume fraction. • The collector outlet temperature and heat gain enhanced with 21% and 23%, respectively. • The ratio of the increase of the heat removable factor for nanofluids was between 1.05 and 1.16%. • The thermal-optical efficiency of the collector reached 72.83%. [ABSTRACT FROM AUTHOR]

Abstract Global warming, climate change, fossil fuel depletion and steep hikes in the price of environmentally friendly hydrocarbons motivate researchers to investigate CO 2 hydrogenation for hydrocarbons production. However, due to the reaction complexities and varieties of produced species, the process mechanism and subsequently estimation of the kinetic parameters have been controversial yet. Therefore, estimating the kinetic parameters using Artificial Bee Colony (ABC) and Differential Evolution (DE) optimization algorithms based on Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism is proposed as a possible remedy to fulfil the requirements. To this end, a one-dimensional heterogeneous model comprising detailed reaction rates of reverse water gas shift (RWGS), Fisher-Tropsch (FT) reactions and direct hydrogenation (DH) of CO 2 is developed. It is observed that ABC exhibiting 6.3% error in predicting total hydrocarbons selectivity is superior to DE algorithm with 32.9% error. Therefore, the model employed the estimated kinetic parameters obtained via ABC algorithm, is exploited for products distribution analysis. Results reveal that maximum 73.21% hydrocarbons (C 1 C 4) selectivity can be achieved at 573 K and 1 MPa with 0.85% error compared to the experimental value of 72.59%. Accordingly, the proposed model can be exploited as a powerful tool for evaluating and predicting the performance of CO 2 hydrogenation to hydrocarbons process. Graphical abstract Image 1 Highlights • Studying kinetic parameters of CO 2 hydrogenation has remained controversial. • One-dimensional heterogeneous model including detailed reaction rates is developed. • Kinetic parameters are estimated via ABC and DE algorithms using experimental data. • Components distribution along reactor length are presented based on ABC algorithm. • Performance of the theoretical reactor is compared with available experimental data. [ABSTRACT FROM AUTHOR]

Nanofluids are the most attractive mean to enhance the performance of heat transfer devices. Several types of nanoparticles were utilized as they have high thermal conductivity. Renewable energy makes use of these nanofluids special in solar collectors. Evacuated tube solar collector is one of the most utilized solar collectors in thermal applications. CeO 2 nanoparticles were used in the presented study. The mean diameter of CeO 2 was 25 nm. A stable CeO 2 /water was made. The stability was checked using Zeta potential machine. Experiments were carried out using three different volume concentration of CeO 2 nanoparticles of 0.015%, 0.025%, and 0.035%. The thermal performance of the evacuated tube solar collector was examined at different mass flux rates. Results showed that the temperature difference between inlet and outlet flow and absorbed energy increase when nanofluids are used. The volume fraction flow rate of 0.035% at the mass flux rate of 0.017 kg/s.m 2 had the maximum heat removable factor, the thermo-optical characteristic of the collector, and the thermal loss coefficient. The thermo-optical characteristic of the collector of tube solar corrector is raised up to 34%. [ABSTRACT FROM AUTHOR]

New materials and structures are applied in order to maintain the engineering products more and more efficient. Different types of thermal analysis/modelling of such materials and structures are also needed for the proper planning or design. The thermal analysis/modelling processes are supported by many commercial codes but in some cases, the experts might have applied a self-developed method also. The success of the analysis/modelling depends on many factors, but the basis is the correct application of the heat conduction theory which is more important in case of transient thermal processes. As the used tools are more and more powerful, the users pay less attention to the theory background, according to the author experience. It seems that the long tradition of the inconsistent application of the temperature-dependent thermal diffusivity is continuing, even strengthened by the increased availability of data of the temperature-dependent thermal properties. Regarding the origination of the thermal diffusivity, this might lead to the misinterpretation or misunderstanding of the nonlinear heat conduction problem solution procedures. This paper calls attention to avoid this, shows what kind of mistakes might occur related to this widely observable practice, summarizes the proper application and interprets where and how one could find room for the temperature-dependent thermal diffusivity. [ABSTRACT FROM AUTHOR]

Using nanofluids in thermal energy devices, such as flat-plate solar collectors, is gradually making progress, and getting awareness in the scientific community. Experiments were performed to study the effect of using CeO 2 -water on the efficiency of flat-plate solar collector by three different volume fractions of CeO 2 nanoparticles of 0.0167%, 0.0333% and 0.0666%, while the mean particle dimension was kept constant at 25 nm. An ultrasonic process was used for maintaining the stability of the CO 2 -water nanofluid. The working fluid mass flux rates were 0.015, 0.018 and 0.019 kg/s m 2 . The experiments were carried out in Budapest, Hungary on the latitude of 47°28′N and longitude of 19°03′E. Higher collector efficiency was achieved when using CeO 2 -water nanofluid compared to results achieved with water application. Based on present data, the efficiency of the collector is directly proportional with the mass flux rate and with the volume fraction in the ranges of the present study. Experiments indicate that the highest rise in efficiency of the collector at zero value of [(T i  – T a )/G T ] is 10.74%, for volume fraction ( φ ) 0.066%, and for mass flux rate of 0.019 kg/s m 2 compared to water. [ABSTRACT FROM AUTHOR]

The investigation of nanofluids effects on the performance of solar energy devices has converted to an important topic of research in recent years. The present experimental study deals with the effects of using WO 3 /water nanofluids on the efficiency of a flat plate solar collector which operates under weather conditions of Budapest, Hungary. First, water based nanofluids containing WO 3 nanoparticles (with an average size of 90 nm) at three different volume fractions including 0.0167%, 0.0333%, and 0.0666% have been synthesized. The stability of nanofluids has been evaluated through Zeta potential tests which unveiled the prepared suspensions have high stability. In the next step, the thermal performance of the flat plate solar collector using nanofluids is investigated at different mass flux rates including 0.0156, 0.0183, and 0.0195 kg/s.m 2 . The results showed that adding WO 3 nanoparticles to water ameliorates the efficiency of the solar collector. The experiment results reveal that the maximum enhancement in efficiency of the collector at zero value of [(Ti–Ta)/G T ] was 13.48% for the volume fraction of 0.0666% and mass flux rate of 0.0195 kg/s.m 2 compared to water, which clearly shows the high potential of WO 3 nanoparticles for solar energy applications. [ABSTRACT FROM AUTHOR]

A CFD model was performed with commercial software through the adoption of the finite volume method and a SIMPLE algorithm. SiO2-P25 particles were added to water/ethylene glycol as a base fluid. The result is considered a new hybrid nanofluid (HN) for investigating heat transfer (HT). The volume concentrations were 0.5, 1.0, and 1.5%. The Reynolds number was in the range of 5000–17,000. The heat flux (HF) was 7955 W/m2, and the wall temperature was 340.15 K. The numerical experiments were performed strictly following the rules that one should follow in HT experiments. This is important because many studies related to nanofluid HT overlook these details. The empirical correlations that contain the friction factor perform better with higher Reynolds numbers than the correlations based only on Reynolds and Prandtl numbers. When temperature differences are moderate, researchers may consider using constant properties to lower computational costs, as they may give results that are similar to temperature-dependent ones. Compared with previous research, our simulation results are in agreement with the experiments in real time. [ABSTRACT FROM AUTHOR]

The progressive use of renewable energy sources to ensure a continuous and abundant energy supply is the significant target towards a sustainable and secure energy system. Previously, the countries that had relied on fossil fuel as a dominating energy source are now endorsing energy system transition towards renewable energy sources. In this study, a comparative assessment of the energy problems of South Asian countries is summarized. Nevertheless, there are many similarities and differences in the electricity supply system of these countries. Long-term planning for renewable energy development is suggested for a diverse population and dispersed geographical location considering all the significant challenges. However, appropriate schemes are imperative for integrating significant renewable energy sources. This study introduces a foresight plan of the electricity model according to the demand and supply balance for extensive technical analysis. The EnergyPLAN modeling tool was employed to work out a more ambitious VRE integration scenario than the official plans. The reference model was validated according to legitimate and authentic data, and then, the technically most feasible renewable energy-based alternate scenario was built. The additional grid integration cost of variable renewable energy was quantitatively investigated for comprehensive power system modeling for a real-time economic analysis. The results may be adapted and support developing more sustainable power generation serving 1787 million in South Asian countries. [ABSTRACT FROM AUTHOR]

Thermodynamics -- contrary to its name -- is not a dynamic theory. However, some relatively new developments, like finite time, endo-reversible or ordinary thermodynamics introduce time into thermostatics. In this framework the Second Law becomes a set of conditions ensuring the asymptotic stability of equilibrium. In this manuscript some examples are shown, how this basic conceptual change can lead to a profound understanding. [ABSTRACT FROM AUTHOR]

Abstract: A new application of the cost function-based rearrangement of genes (proposed by Liu (2008) ) is presented in this paper through the genetic algorithm-based solution of the inverse heat conduction problem of identifying the temperature dependent thermal conductivity of a solid material using transient temperature histories. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. Through the solution of the inverse problem (using simulated measurements), different approaches of the application of the rearrangement of genes were studied and compared. Application of the rearrangement significantly improved the convergence performance and accuracy of the inverse solution compared to a real-valued genetic algorithm, which was adapted to the problem by the authors. In the algorithm that performed best, the rearrangement was applied in an approach different from Liu’s. The effect of random noise added to the temperature history and the effect of regularization was also studied. With significant improvement in computational efficiency, the proposed algorithm is likely to be very effective in evaluation of real measured temperature histories to determine thermophysical properties. [Copyright &y& Elsevier]

Simultaneous inverse identification of the temperature-dependent volumetric heat capacity and thermal conductivity of a solid material based on transient temperature histories was studied. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The material property functions were defined by several data points with linear interpolation between them. In this way, no previous information is needed about the expected temperature dependency of the material property functions. The inverse problem was solved by a real-valued genetic algorithm using simulated measurement results. New genetic operators (smooth initialization and smooth mutation) were developed and applied. The accuracy of the inverse solution was studied in two test cases including linear, square, and sinusoidal functions. The effects of regularization and random noise in the temperature histories were also analyzed. Based on the results, the proposed method is likely to be effective in evaluation of real measured temperature histories to simultaneously determine temperature-dependent thermophysical properties. [ABSTRACT FROM AUTHOR]

• Irradiance separation and transposition modeling are the two most critical steps. • 13% MAE, 12% RMSE and 23–33% skill score improvement is possible by model selection. • Detailed physical model chains, inlc. reflection and shading, have the lowest MAE. • Simple models have the lowest RMSE, but high bias and underdispersed forecast. • Wind speed has only marginal effect on physical photovoltaic power forecasting. Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models. The two most critical calculation steps are identified as irradiance separation and transposition modelling, while the inverter models are the least important. Absolute and squared errors are two conflicting metrics, as the more detailed models result in the lowest MAE, while the simplest ones have the lowest RMSE. Wind speed forecasts have only a marginal effect on the PV power prediction. The results of this study contribute to a deeper understanding of the physical forecasting approach in the research community, while the main conclusions are also beneficial for PV plant owners in preparing their generation forecasts. [ABSTRACT FROM AUTHOR]

• Modeling of CO 2 hydrogenation to hydrocarbons in a three-side reactor is developed. • Tr, Ts, Tt, θ and φ are considered as main effects in determining product yields. • Statistical analysis is performed to study the effects of variables on the yields. • Optimized operating conditions are determined to maximize olefin yields. Direct CO 2 hydrogenation to hydrocarbons is a promising method of reducing CO 2 emissions along with producing value-added products. However, reactor design and performance have remained a challenging issue because of low olefin efficiency and high water production as a by-product. Accordingly, a one-dimensional non-isothermal mathematical model is proposed to predict the membrane reactor performance and statistical analysis is used to assess the effects of important variables such as temperatures of reactor (Tr:A), shell (Ts:B) and tube (Tt:C) as well as sweep ratio (θ:D) and pressure ratio (φ:E) and their interactions on the products yields. In addition, the optimized operating conditions are also obtained to achieve maximum olefin yields. Results reveal that interacting effects comprising AB (TrTs), AC (TrTt), AE (Trφ), BC (TsTt), CE (Ttφ), CD (Ttθ) and DE (θφ) play important roles on the product yields. It is concluded that higher temperatures at low sweep and pressure ratios can maximize the yields of olefins, while simultaneously the yields of paraffins are minimized. In this regard, optimized values for Tr, Ts, Tt, θ and φ are determined as 325 °C, 306.96 °C, 325 °C, 1 and 1, respectively. [ABSTRACT FROM AUTHOR]

The development of an efficient reactor for hydrocarbons (C 2 –C 4) production through hydrogenation of CO 2 , requires a deep understanding of the operating conditions effects. Subsequently, a model is proposed to analyze the reaction rates and investigate the sensitivity of hydrocarbons yield and products distribution to the variations of temperature, pressure and space velocity (SV). Besides, Thiele modulus and effectiveness factor are calculated for all of the reactions considered in the model. Results reveal that simultaneous occurrence of both endothermic reverse water gas shift (RWGS) and exothermic Fischer-Tropsch (FT) reactions, may be the main reason of temperature and rate fluctuations at the fixed-bed reactor inlet. In addition, increasing temperature and pressure, and decreasing SV can shift the process to produce more light olefins. Finally, sensitivity analysis demonstrates that reactor behavior is independent of the changes in pressure and SV at high temperature, which is an indication of high temperature dependency of this process. These findings can be effectively employed to achieve a better insight about appropriate operating conditions of hydrocarbons production via hydrogenation of CO 2. Image 1 • Effectiveness factor and Thiele modulus for all reactions are calculated. • Sensitivity analysis are performed via the mathematical modeling of reactor. • Variations of reaction rates and reactor temperature are analyzed. • Effect of temperature, pressure and space velocity on products are studied. [ABSTRACT FROM AUTHOR]

In the current research, surface-modified SiO2 nanoparticles were used upon immersion in an applied base fluid (ethylene glycol:water = 1:1). The atomic layer deposition method (ALD) was introduced to obtain a thin layer of TiO2 to cover the surface of SiO2 particles. After the ALD modification, the TiO2 content was monitored by energy dispersive X-ray spectroscopy (EDS). Transmission electron microscopy (TEM) and FT-IR spectroscopy were applied for the particle characterization. The nanofluids contained 0.5, 1.0, and 1.5 volume% solid particles and zeta potential measurements were examined in terms of colloid stability. A rotation viscosimeter and thermal conductivity analyzer were used to study the nanofluids' rheological properties and thermal conductivity. These two parameters were investigated in the temperature range of 20 °C and 60 °C. Based on the results, the thin TiO2 coating significant impacted these parameters. [ABSTRACT FROM AUTHOR]

In this paper, we present a study on thermal conductivity and viscosity of nanofluids containing novel atomic layer deposition surface-modified carbon nanosphere (ALD-CNS) and carbon nanopowder (ALD-CNP) core-shell nanocomposites. The nanocomposites were produced by atomic layer deposition of amorphous TiO2. The nanostructures were characterised by scanning (SEM) and transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, thermogravimetry/differential thermal analysis (TG/DTA) and X-ray powder diffraction (XRD). High-concentration, stable nanofluids were prepared with 1.5, 1.0 and 0.5 vol% nanoparticle content. The thermal conductivity and viscosity of the nanofluids were measured, and their stability was evaluated with Zeta potential measurements. The ALD-CNS enhanced the thermal conductivity of the 1:5 ethanol:water mixture by 4.6% with a 1.5 vol% concentration, and the viscosity increased by 37.5%. The ALD-CNS increased the thermal conductivity of ethylene–glycol by 10.8, whereas the viscosity increased by 15.9%. The use of a surfactant was unnecessary due to the ALD-deposited TiO2 layer. [ABSTRACT FROM AUTHOR]

Abstract: An artificial neural network based solution of the inverse heat conduction problem of simultaneous identification of the temperature-dependent volumetric heat capacity and thermal conductivity function of a solid material is presented in this paper. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The volumetric heat capacity and thermal conductivity vs. temperature functions are to be determined using the measured transient temperature histories of two sensors. In this study noiseless and noisy artificial measurements were generated by the numerical solution of the corresponding direct heat conduction problem. The inverse problem was solved by multi-layer feed-forward neural network trained by back-propagation algorithm (BP) and radial basis function (RBF) type neural network applying the whole history mapping approach. As suggested by Czél et al. [1], a novel (time vs. temperature) representation of the input data was applied. Numerical tests were performed to analyze the accuracy of the two network types with noiseless and noisy inputs. Moreover, it is not necessary to retrain the network when the temperature range of the measurement is changed. Based on the presented results it can be stated that feed-forward neural networks are powerful tools in non-iterative solution of function estimation inverse heat conduction problems and they are likely to be very effective in evaluation of real measured temperature histories to simultaneously determine the volumetric heat capacity and thermal conductivity as an arbitrary function of temperature. [Copyright &y& Elsevier]

An artificial neural network (NN)-based solution of the inverse heat conduction problem of identifying the temperature-dependent volumetric heat capacity function of a solid material is presented in this paper. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The volumetric heat capacity versus temperature function is to be determined using the measured transient temperature history of a single sensor. In this study, noiseless and noisy artificial measurements were generated by the numerical solution of the corresponding direct heat conduction problem. The inverse problem was solved by back-propagation and radial basis function type neural networks applying the whole history mapping approach. The numerical tests included the comparison of two different data representations of the network inputs (i.e., temperature vs. time and time vs. temperature) and accuracy analysis of the two network types with noiseless and noisy inputs. Based on the results presented, it can be stated that feed-forward NNs are powerful tools in a non-iterative solution of function estimation inverse heat conduction problems and they are likely to be very effective in evaluation of real measured temperature histories to determine the volumetric heat capacity as an arbitrary function of temperature. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power is emission-free during operation. However, its life-cycle environmental impacts should be accounted for during the design phase. This paper presents the first methodology to calculate the product environmental footprint (PEF) and the levelized cost of electricity (LCOE) based on a unified technical modeling framework of ground-mounted, grid-connected PV power plants. To test the model, we determined the single- and multiobjective optima of 8 main balance-of-system design parameters applying a genetic algorithm for a case study of two geographical locations with distinct solar resources. Results show that impacts can be reduced by 1–13% in each of the 16 different PEF environmental impact categories. Unfortunately, the different types of impacts cannot be reduced simultaneously with a single design solution, calling for the use of a weighted environmental footprint as the most favorable single-score environmental indicator. Pareto-optimal design solutions for multiple objectives show that increasing the AC/DC ratio, row distance, tilt angle, and cable losses compared to the economic optimum is beneficial for carbon and environmental footprint reduction. By accepting a small cost increase, the majority of the potential impact reduction can be achieved. Trade-offs between economic and environmental objectives are analyzed by assigning a price tag to environmental impacts. Based on the proposed weighting scheme, regions with polluting electricity production mix should choose the most profitable plant design for the largest environmental benefit. In other places eco-design could lead to a 1% overall impact reduction for just 0.1% extra cost. Balancing power requirements, copper recycling scenarios, the uncertainty of ecological impacts, and combined land use are identified as future research areas. Image 1 • First study to integrate environmental footprint method in the design of PV plants. • Economic and environmental optimization results in fairly distinctive designs. • Environmental optimization leads to greater capacity factor but larger land area. • Weighting method between environmental and economic objective is proposed. • Taxing environmental impacts leads to a minor change in least-cost design. [ABSTRACT FROM AUTHOR]

• Modeling of CO 2 hydrogenation to hydrocarbons in CR, AFSR and RFSR are accomplished. • Kinetic parameters are estimated via heuristic DA optimization algorithm. • Performance of the theoretical CR is compared with available experimental data. • Effect of reactor configuration is investigated in terms of conversion and selectivity. Climate change and global warming, as well as growing global demand for hydrocarbons in industrial sectors, make great incentives to investigate the utilization of CO 2 for hydrocarbons production. Therefore, finding an in-depth understanding of the CO 2 hydrogenation reactors along with simulating reactor responses to different operating conditions are of paramount importance. However, the reaction mechanisms for CO 2 hydrogenation and their corresponding kinetic parameters have been disputable yet. In this regard, considering the previously proposed Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism, which considered CO 2 hydrogenation as a combination of reverse water gas shift (RWGS) and Fischer-Tropsch (FT) reactions, and using a one-dimensional pseudo-homogeneous non-isothermal model, kinetic parameters of the rate expressions are estimated via fitting experimental and modelling data through a novel swarm intelligence optimization technique called dragonfly algorithm (DA). The predicted reactants conversion using DA algorithm are closer to the experimental data (with about 4% error) comparing to those obtained by the artificial bee colony (ABC) algorithm, and are in significant agreement with available literature data. The proposed model is used to assess the effect of reactor configuration on the performance and temperature fluctuations. Results show that axial flow spherical reactor (AFSR) and radial flow spherical reactor (RFSR) exhibiting the same surface area with that of the cylindrical reactor (CR), i.e., AFSR-2 and RFSR-2-i are the most efficient exhibiting hydrocarbons selectivity of 40.330% and 40.286% at CO 2 conversion of 53.763% and 53.891%. In addition, it is revealed that the location of the jacket has an essential role in controlling the reactor temperature. [ABSTRACT FROM AUTHOR]

• Technical, economic and environmental modeling frameworks are proposed. • Economic and environmental optima of hybrid renewable energy systems are different. • Local climatic conditions highly influence the impacts and the optimal design. • Single- and multi-objective optimization both have advantages and disadvantages. • Installing photovoltaics is the cheapest way to decrease environmental impacts. The rapid spread of renewables made it essential to design optimal hybrid renewable energy systems (HRES) with the distinctive economic and environmental impacts of each technology in mind. According to a comprehensive literature review, very few studies consider life-cycle environmental impacts in small-scale hybrid renewable energy system optimization. This paper aims to fill this gap by providing a multi-objective design framework for household-scale systems based on the technical modeling of several typical components. Solar photovoltaic, wind turbine, solar heat collector, heat pump, heat storage, battery, and as a novelty, heat insulation thickness are considered. Backup power is either drawn from the grid or produced by a diesel generator in grid-connected and off-grid scenarios, respectively. Single objective optimization using genetic algorithm resulted in the least cost and the least environmental footprint options in a case study of three different locations across Europe. Then, Pareto-optimal solutions between the two extremities were explored with a multi-objective genetic algorithm. Single objective results show substantial differences between environmental and economic optima, while multi-objective optimization proved to be an efficient tool to investigate the trade-offs between the two conflicting goals. Solar photovoltaics is proved to be the most competitive technology to reduce environmental impacts in the case of grid-connected systems. Off-grid systems, however, benefit the most from a balanced mix of different renewable energy sources. Life-cycle impacts in the design of systems involving renewables is proven to be relevant while potential applications of the framework are also revealed. Further research areas, as well as the limitations of the methodology are identified in the conclusion. [ABSTRACT FROM AUTHOR]

The solar chimney power plant (SCPP) is a straightforward and clean technique to generate electricity from solar radiation. However, this technology still faces major challenges, such as low efficiency, which has hindered its industrialization. This study experimentally develops a novel collector design to improve the solar chimney collector's efficiency. The new design includes metallic tubes as solar radiation absorbers hung from the canopy of the collector. The metallic tubes are open at the top and sealed with transparent sheets at the bottom to decrease the solar radiation reflected into the ambient air. Experimental and 3-D computational fluid dynamics (CFD) analyses are performed to validate the new design. The effects of hanging metallic tubes on temperature and velocity distribution are explored. The temperature increased by about 5 K at the chimney inlet, causing a roughly 8% rise in collector efficiency due to the fact that metallic tubes operate as an extended surface. The impact of various tube geometries on solar chimneys' efficiency is examined. The CFD findings reveal that the metallic tube geometry variation has considerably impacted the collector's efficiency. Thus, the collector efficiency is increased by changing tube diameter by around 33.7%, similar to changing tube length by 30%. [Display omitted] • A new solar chimney's collector design was experimentally developed and compared with CFD simulation. • Effects of hung metallic tubes on air temperature and velocity distribution were explored. • Effects of various metallic tube geometries on the collector's efficiency were studied and compared. • The collector efficiency increased from 25% in conventional solar chimneys to 33.90% in the improved ones. [ABSTRACT FROM AUTHOR]

• MCDM and machine learning models were compared for flood modelling. • Results show that machine learning is more potential for flood modelling. • Out of machine learning methods, NB outperforms NBT. • Out of MCDM methods, SAW and VIKOR outperform TOPSIS. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards. [ABSTRACT FROM AUTHOR]

From both environmental and energy-saving points of view, solar heating and cooling systems have recently proven themselves in the commercial world as the environmentally friendly and sustainable energy systems which can replace the systems powered by conventional sources of energy such as fossil fuels and electricity. In the present paper, the research contributions conducted on solar absorption cooling systems, integrated with different auxiliary energy devices, are comprehensively reviewed and discussed. Also, various cooling systems integrated with solar technologies, i.e., flat-plate collector, evacuated tube collector, compound parabolic collector and parabolic trough collector, are reviewed. The survey and comparison are carried out in terms of some crucial parameters such as coefficient of performance, annual energy consumption and payback period. Further, this work briefly presents and discusses some possible opportunities for future works from the efficiency viewpoint. [ABSTRACT FROM AUTHOR]

We report heat pulse experiments at room temperature that cannot be described by Fourier's law. The experimental data are modeled properly by the Guyer-Krumhansl equation, in its over-diffusion regime. The phenomenon may be due to conduction channels with differing conductivities and parallel to the direction of the heat flux. [ABSTRACT FROM AUTHOR]

Halloysite nanotube (HNT) which is cheap, natural, and easily accessible 1D clay, can be used in many applications, particularly heat transfer enhancement. The aim of this research is to study experimentally the pool boiling heat transfer (PBHT) performance of novel halloysite nanofluids at atmospheric pressure condition from typical horizontal heater. The nanofluids are prepared from halloysite nanotubes (HNTs) nanomaterials-based deionized water (DI water) with the presence of sodium hydroxide (NaOH) solution to control pH = 12 to obtain stable nanofluid. The nanofluids were prepared with dilute volume concentrations of 0.01–0.5 vol%. The performance of PBHT is studied via pool boiling curve and pool boiling heat transfer coefficient (PBHTC) from the typical heater which is the copper horizontal tube with a thickness of 1 mm and a diameter of 22 mm. The temperatures of the heated tube surface are measured to obtain the PBHTC. The results show an improvement of PBHTC for halloysite nanofluids compared to the base fluid. At 0.05 vol% concentration, HNT nanofluid has the best enhancement of 5.8% at moderate heat flux (HF). This indicates that HNT is a potential material in heat transfer applications. [ABSTRACT FROM AUTHOR]

The paper presents a curve-fitting-based calculation of the thermal contact resistance and other parameters (absorbed energy and material properties) from laser flash measurements considering a two-layer specimen (aluminum substrate and stainless-steel film). Sensitivity analysis of different cases was used to examine the sensitivities of the unknown parameters: thermal contact resistance, absorbed energy, specific heat of the film, and thermal conductivity of the film. A nonlinear curve-fitting method was applied to perform the estimation of the unknown parameters using simulated measurements generated by the solution of the direct problem. An extensive analysis was performed to examine which parameters might be estimated simultaneously with the contact resistance for different noise levels of the simulated measurement. It was concluded that in the noiseless case all four unknown parameters can be estimated simultaneously with high accuracy. The noise has a significant effect on the accuracy of the parameter estimation, but even when a reasonable noise level is present, it is still possible to accurately estimate one or two parameters together with the thermal contact resistance. [ABSTRACT FROM AUTHOR]

A comparative research on stability, viscosity (µ), and thermal conductivity (k) of carbon nanosphere (CNS) and carbon nanopowder (CNP) nanofluids was performed. CNS was synthesized by the hydrothermal method, while CNP was provided by the manufacturer. Stable nanofluids at high concentrations 0.5, 1.0, and 1.5 vol% were prepared successfully. The properties of CNS and CNP nanoparticles were analyzed with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), specific surface area (SBET), X-ray powder diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and energy dispersive X-ray analysis (EDX). The CNP nanofluids have the highest k enhancement of 10.61% for 1.5 vol% concentration compared to the base fluid, while the CNS does not make the thermal conductivity of nanofluids (knf) significantly higher. The studied nanofluids were Newtonian. The relative µ of CNS and CNP nanofluids was 1.04 and 1.07 at 0.5 vol% concentration and 30 °C. These results can be explained by the different sizes and crystallinity of the used nanoparticles. [ABSTRACT FROM AUTHOR]

Nanofluids obtained from halloysite and de-ionized water (DI) were prepared by using surfactants and changing pH for heat-transfer applications. The halloysite nanotubes (HNTs) nanofluids were studied for several volume fractions (0.5, 1.0, and 1.5 vol%) and temperatures (20, 30, 40, 50, and 60 °C). The properties of HNTs were studied with a scanning electron microscope (SEM), energy-dispersive X-ray analysis (EDX), Fourier-transform infrared (FT-IR) spectroscopy, X-ray powder diffraction (XRD), Raman spectroscopy and thermogravimetry/differential thermal analysis (TG/DTA). The stability of the nanofluids was proven by zeta potentials measurements and visual observation. With surfactants, the HNT nanofluids had the highest thermal conductivity increment of 18.30% for 1.5 vol% concentration in comparison with the base fluid. The thermal conductivity enhancement of nanofluids containing surfactant was slightly higher than nanofluids with pH = 12. The prepared nanofluids were Newtonian. The viscosity enhancements of the nanofluid were 11% and 12.8% at 30 °C for 0.5% volume concentration with surfactants and at pH = 12, respectively. Empirical correlations of viscosity and thermal conductivity for these nanofluids were proposed for practical applications. [ABSTRACT FROM AUTHOR]

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811). [ABSTRACT FROM AUTHOR]

We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides. [ABSTRACT FROM AUTHOR]

The non-Fourier heat conduction phenomenon on room temperature is analyzed from various aspects. The first one shows its experimental side, in what form it occurs, and how we treated it. It is demonstrated that the Guyer-Krumhansl equation can be the next appropriate extension of Fourier's law for room-temperature phenomena in modeling of heterogeneous materials. The second approach provides an interpretation of generalized heat conduction equations using a simple thermo-mechanical background. Here, Fourier heat conduction is coupled to elasticity via thermal expansion, resulting in a particular generalized heat equation for the temperature field. Both aforementioned approaches show the size dependency of non-Fourier heat conduction. Finally, a third approach is presented, called pseudo-temperature modeling. It is shown that non-Fourier temperature history can be produced by mixing different solutions of Fourier's law. That kind of explanation indicates the interpretation of underlying heat conduction mechanics behind non-Fourier phenomena. [ABSTRACT FROM AUTHOR]