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Shrinkage of lakes is considered a serious ecological challenge. The impact of this phenomenon on the environment can be devastating. Monitoring the spatiotemporal variations of lake's water surface and its salinization is essential for planning and adopting mitigation measures. In this study, a new multiscale-kernel-based method was used to assess the spatiotemporal variations of the shrinkage of the Urmia Lake and develop soil salinity vulnerability map for its basin. For this aim, remote sensing, empirical wavelet transform (EWT), differential symbolic entropy (DSE), and Gaussian regression process (GPR) techniques were used. Vulnerable areas were identified using geo-environmental parameters extracted from the in situ observations and satellite datasets. In the next step, considering three time periods including the lake normal period, lake drying period, and lake restoration period, the variations in the quality of groundwater were investigated. Results showed that the east and south sections of the lake were more prone to severe salinization. Saline lands caused negative impacts on air quality and agricultural activities in these areas. It was found that both climate change and human activities had contributed to the shrinking of the lake. Results showed that the quality of groundwater in the area around the lake has been affected by the excessive salinity of the lake water and the encroachment process of saline water. The water quality index has increased during the drying period of the lake and caused negative effects on the quality of water used for drinking and agricultural activities. In the lake restoration period, a slight increase in water quality was observed. [ABSTRACT FROM AUTHOR]

Lakes are natural water resources that are affected by different factors. The increase in soil salinity is one of the major issues created due to the drying up of a lake. In this study, a two-step methodology was used to assess drought vulnerability and salinity variation of the Urmia Lake basin. In this regard, firstly, a multi-criteria intelligence method based on empirical wavelet transform-long short-term memory, which integrated 15 geo-environmental variables extracted from the in-situ observations and satellite datasets, was used for developing drought vulnerability maps of the basin. In the next step, the salinization progress of the basin and its impacts on the environment were investigated using satellite datasets. Results showed that the Southern and Eastern sections of the lake were more prone to severe droughts. It was found that temperature and precipitation variations did not lead to significant shrinkage of the lake, but human activities along with climate changes caused the basin to dry up. The results showed that the biomass production in the basin is affected by salinity, and there is a negative correlation between the salinity index and the normalized difference vegetation index. Also, a positive correlation was found between land subsidence and the density of drilled wells in the basin. The rate of land subsidence varied between − 1.8 and − 8.4 mm/year. The quality of groundwater was investigated for the existing wells in the basin. Results showed that the excessive use of groundwater resources has affected the quality of water. [ABSTRACT FROM AUTHOR]

This study introduces two integrated methods to model the short- to long-term droughts in terms of the Standardized Precipitation Index (SPI). In this regard, the monthly precipitation data from three stations located in different climates of Iran was used. Firstly, the capability of Random Forest (RF) and M5 Pruning tree (M5P) models were assessed for short- to long-term drought forecasting. Then, the impact of data multi-pre-processing on improving the models' efficiency was investigated. Therefore, inputs were serially decomposed via Discrete Wavelet Transform (DWT) and Variational Mode Decomposition (VMD). The most dominate subseries were selected based on their energy values and used as inputs of the RF and M5P. Furthermore, since the drought variable consists of two deterministic and stochastic sections and in the modeling process the stochastic part is not often considered, in the third step, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was applied for determining the random parts of the SPIs. Results showed that the integrated models led to better outcomes compared to the standalone models. The second and third steps increased the modeling accuracy up to 45% and 55%, respectively. Results showed that the distribution range of the Root Mean Square Errors (RMSE) criteria for the SPI-3 modeling decreased from 0.438–0.551 (in using raw data) to 0.184–0.26 (in using the integrated-GARCH model). Results showed that using the integrated-GARCH models, SPIs could be successfully simulated with considering only one input variable. [ABSTRACT FROM AUTHOR]

In addition to the waste of resources and economic losses, environmental damage by gas flaring is widespread and significant. Since flaring the associated gas gives no added value in exchange for its pollution and greenhouse gas (GHG) emissions, it could be identified as a top priority for mitigation. Iran is the third gas flaring country after Russia and Iraq among those facing this issue, and is responsible for 12.1% of the world's gas flaring. While the necessity of developing a method for the precise estimation of flaring GHG emissions is clear, especially for evaluating the result of countries' efforts to meet their nationally determined contribution target, there are huge uncertainties and discrepancies in the values of emission factors among various data sources due to the lack of actual measurements of the volume and diversity of the composition of flare gas. This study aimed to fill the gap in providing authentic data on Iran's gas flaring GHG and air pollutant emissions by developing a model based on satellite data on flare volumes, gas compositions, and combustion equations. Our results revealed that based on 2021 data on flaring volume, Iranian gas flares are emitting approximately 50 million metric tons of CO2 equivalent to the atmosphere annually, which could be reduced to 43 by only enhancing the flares' efficiency. It accounted for 5.5%–6% of the total GHG emissions of the country. Integr Environ Assess Manag 2023;19:735–748. © 2022 SETAC. Key Points: Iranian gas flares are emitting some 50 million metric tons of CO2 equivalent to the atmosphere annually, accounting for 5.5%–6% of the country's total GHG emissions.Slight increases in the flare's destruction efficiency can cause a huge decrease in its value of global warming potential.The gas flare's emission factors suggested by USEPA would be reliable only if a combustion efficiency of 98% and a destruction efficiency of 99% were considered, while emission factors suggested by Ecoinvent seem to be too optimistic to be true.By taking advantage of fair access to financial opportunities and international cooperation envisaged through the Convention (COP21), the Paris Agreement could be a historic opportunity for the complete elimination of gas flaring from Iran's upstream and downstream oil industries. [ABSTRACT FROM AUTHOR]

Drought as a natural disaster is one of the human's ecological, hydrological, agricultural, and economic concerns. In this study, multiscale intelligence methods were proposed for drought severity detection and mapping in the northwest part of Iran for the years of 2007 to 2020. In the modeling process two scenarios were considered and in-situ and remote sensing datasets were adopted with two machine learning models namely M5 Pruning tree (M5P) and Random Forest (RF). In the first scenario, the in-situ datasets including the precipitation, relative humidity, evaporation, and temperature were used as inputs of the intelligence models to assess drought severity in terms of the Standardized Precipitation Index. In the second scenario, the SM2RAIN-ASCAT precipitation product and Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) products of the MODIS were considered as inputs. During the drought severity modeling process, the input time series were first broken down into several subseries via the Variational Mode Decomposition; then, the most effective subseries were selected and imposed into the M5P and RF as inputs. Also, the potential of the relatively new TemperatureVegetation Water Stress Index (T-VWSI), which has developed based on the NDVI and LST, was assessed in drought severity monitoring. The results proved the appropriate efficiency of the proposed multiscale methods in effectively detecting drought severity. Also, it was observed that the T-VWSI could be successfully used for detecting drought occurrences in areas without meteorological datasets. [ABSTRACT FROM AUTHOR]

Drought is one of the severe natural disasters that has devastating effects on various parts of the environment. Therefore, spatiotemporal monitoring of drought using reliable methods is very important. In this study, using a new Discrete Wavelet Transform (DWT)-Variational Mode Decomposition (VMD)-Energy multi-scale approach, the efficiency of the new Soil Moisture to RainAdvanced SCATterometer (SM2RAIN-ASCAT) precipitation product was investigated in monitoring the spatiotemporal patterns of short- to long-term droughts for the Northwest part of Iran. In this regard, after validating the accuracy of the SM2RAIN-ASCAT datasets, a multi-scale method was developed based on the serial decomposition of the drought signals. The mean energy amounts of the obtained subseries were imposed into the K-means (K signifies the number of clusters) to identify the drought-prone regions. Results showed that the highest Correlation Coefficient (R) between SM2RAIN-ASCAT and in-situ observations are found at monthly time scales, in which 73% of the stations had R higher than 0.7. Investigating the reliability of this product for drought monitoring showed that for short- and mid-term timescales in 75% of the stations, the values of R were higher than 0.7. The applied methodology successfully showed the drought-prone regions and a direct relationship was found between energy and drought intensity. The capability of the applied methodology was verified via the CPC Merged Analysis of Precipitation (CMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) products and the obtained results proved the desirable performance of the SM2RAIN-ASCAT datasets. It was found that SOI, Nino3, and Nino3.4 had major effects on the drought index in the selected area, while AO had the least effect. The relationship between the LST and SPIs was negative, while there was a positive relationship between NDVI and SPIs. [ABSTRACT FROM AUTHOR]

Drought as a complex event depends on some geo-environmental parameters. The impact of this phenomenon on the economy and environment can be devastating. Recognizing drought-prone regions is essential for planning and adopting mitigation measures. This study focused on introducing a multiscale method based on the satellite datasets to assess drought vulnerability in non-gage areas. In this regard, two steps were considered and drought monitoring was done for the northwest part of Iran as the study area. In the first step, vulnerable areas were identified using multi-criteria-kernel-based techniques which integrated 17 geo-environmental parameters extracted from the in-situ observations and satellite datasets to develop drought vulnerability map. The considered models were evaluated via different metrics and most important variables were determined by performing sensitivity analysis. Results indicated that the south and central parts of the selected area were more prone to severe droughts. Among the used variables, soil moisture, precipitation, evaporation, humidity, and vegetation condition were the most effective parameters. In the second step, due to limitations of the in-situ observations, drought-prone areas were identified using only precipitation, vegetation condition, and land surface temperature variables extracted from the satellites. For this aim, a new multi-scale method was developed based on the Wavelet Transform (WT), Variational Mode Decomposition (VMD), Permutation Entropy (HPE), and kernel-based methods. The obtained results showed the appropriate efficiency of the proposed multiscale method in identifying drought-prone areas with different intensity. [ABSTRACT FROM AUTHOR]

Streamflow is one of the most important issues in river engineering due to its impact on planning and operation of the water resources system. In this study, the capability of newly integrated hybrid prediction models based on artificial intelligence and data processing methods was assessed for monthly river streamflow modeling. In this regard, three successive hydrometric stations of Housatonic River were selected and based on the previous time steps of streamflow values during the period of 1941–2018 several models were developed. During the modeling process, two states based on stations own data (state 1) and upstream stations data (state 2) were considered. For data preprocessing, first temporal features of the streamflow series were decomposed via wavelet transform (WT). Then, the obtained subseries were further broken down into intrinsic mode functions using ensemble empirical mode decomposition (EEMD) to obtain features with higher stationary properties. Finally, the most efficient subseries were selected and used for artificial intelligence approaches [i.e., feed forward neural network (FFNN), kernel extreme learning machine (KELM), and support vector machine (SVM)] as inputs. Also, data post-proceeding was done using simple linear averaging (SLAM) and nonlinear neural ensemble (NNEM) methods. Based on the results, the pre- and post- processing data were more accurate as compared to single artificial intelligence method. The integrated pre-post-processing models improved the models efficiency by approximately 45%. It was observed that via the integrated approaches, the upstream stations data could be applied successfully for streamflow modeling when the stations own data were not available. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to study the thermodynamic, economic, and environmental aspects of a integrated system combined cooling, heating, and power generation system empowered by biomass and natural gas. Eco-Indicator 99 method is utilized to quantify the environmental impact. The proposed system consists of four main subsystems producing power, heating, and cooling. Natural gas is mixed with the syngas to enhance its heating value. The results indicate that the exergy efficiency of system is 39.45%, the products cost per exergy unit is 9.71 $ h−1, and the products environmental impact per exergy unit is 4422 mpt GJ−1. Also, when the natural gas mass flow rate-to-syngas mass flow rate ratio increases from 0 to 0.5, the exergy efficiency is found to improve by 71.97%, whereas the products cost per exergy unit and environmental impact per exergy unit of total products are seen to decline by 70.75 and 64.09%, correspondingly. Additionally, the exergy efficiency enhances by 19.48%, while the cost and environmental impact per exergy unit of the total products drop by 13.39 and 13.02%, respectively, as the splitter separation ratio increases from 0 to1. [ABSTRACT FROM AUTHOR]

Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6. [ABSTRACT FROM AUTHOR]

Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT-EEMD-K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period. [ABSTRACT FROM AUTHOR]

In this study, a three‐dimensional electrochemical process with aluminum electrodes in the presence of granular activated carbon derived from walnut shell (WGAC) was employed to remove Diclofenac from synthetic water. Optimum operating conditions were determined using response surface methodology, also, an empirical relationship was established between response and independent variables (activated carbon amount, contact time, pH, and electrolyte concentration). The results showed that activated carbon amount and NaCl concentration were the most influential factors on the removal efficiency, and the most significant factor affecting electrical energy was NaCl concentration. Additionally, three‐dimensional electrochemical process with granular activated carbon has the potential to work in the pH range from 1 to 9. The optimal conditions to gain maximum removal efficiency and minimum electrical energy were determined to be 4.36 g granular activated carbon, 40 min contact time, and 4390 ppm of NaCl. In addition, to show the physical characteristics of activated carbon, the field emission scanning electron microscopy and BET analysis were applied, and Spectrophotometric technique was used to determine diclofenac concentration. Considering high removal efficiency and low electrical energy consumption, the three‐dimensional electrochemical process with WGAC can be considered a cost‐effective and practicable method to remove diclofenac from water and wastewater. [ABSTRACT FROM AUTHOR]

Accurate prediction of drought indices is a useful method to reduce its undesirable consequences. In this study, the workability of newly integrated hybrid forecasting approach based on Meta model and data processing methods was assessed for forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) in districts with different climates. The short-, mid-, and long-term SPEIs series (i.e. timescale of 3, 9, and 24 month) were computed during the period of 1951–2019 for five sites located in Iran. In this regard, first temporal features of the SPEIs were broken down using Wavelet Transform (WT). Then, for obtaining features with higher stationary properties, Ensemble Empirical Mode Decomposition (EEMD) was applied to further decompose the obtained subseries. Finally, the most efficient subseries were selected and inserted to Meta model approaches [i.e. Feed Forward Neural Network (FFNN), Kernel Extreme Learning Machine (KELM), and Gaussian Process Regression (GPR)] as inputs. Results showed that the proposed methods enhanced the models' capability between 35 to 45%. The capability of the proposed model was verified via Overlap Discrete Wavelet Transform (MODWT) method. Results showed that the distribution range of the Root Mean Square Errors (RMSE) criteria for integrated methods decreased from 0.036–0.172 (in raw data) to 0.025–0.109 (in decomposed data). The Monte Carlo uncertainty analysis was used to assess the applied models dependability. Results showed that the integrated model with having values of 72.8% to 89.2% for the 95PPU indicator had an allowable degree of uncertainty in short- to long-tern drought modeling. [ABSTRACT FROM AUTHOR]

Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983-2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD-PE-AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas. [ABSTRACT FROM AUTHOR]

Stand-alone hybrid energy systems based on solar and energy storage are an effective option for rural areas to meet the load demand. The objective of the current work is to the optimal configuration of a stand-alone hybrid photovoltaic/battery energy storage system with the help of an efficient metaheuristic algorithm, improved harmony search, to supply electrical of a residential load in Iran. The objective function is a minimization of total life cycle cost (TLCC) subject to the reliability index (loss of load probability). The optimal configurations of hybrid systems are compared in respect of different losses of load probability (0 to 20%). Sensitivity analysis and effects of economic parameters based on photovoltaic and battery prices are carried out to study the possibility of the suggested scheme. The results show that, by increasing the reliability index from 0 to 20%, the optimal number of panels and batteries decreases by 52 and 1202. Also, it is found that the TLCC of the system and cost of system components are increased by decreasing of the reliability index value. [ABSTRACT FROM AUTHOR]

Industrial surplus heat is a great available source and because of potential for external use can create benefits for society and industry. Utilizing surplus heat can deliver a way to decrease the use of primary energy and to play a part in global CO2 mitigation. The potential of using excess heat in the main industrial CO2 capture and utilization plant of Iran is investigated. A CO2 liquefaction cycle i.e., ammonia-water absorption system is developed using the heat waste of the flue gas. Process modeling is developed in Aspen Hysys™ v.10 software with the aid of Peng-Robinson equation of state. Energy, exergy, economic and exergoeconomic analyses are then employed to evaluate the developed CO2 liquefaction cycle integrated into the carbon capture and utilization plant. Results of process design and simulation show that the developed CO2 liquefaction system can liquify CO2 with the capacity of 54.5 tons per day using the flue gas enthalpy. The developed CO2 liquefaction system has the COP of 0.28, and overall exergy efficiency of 69.7%. The highest amount of exergy is destructed in ammonia reboiler with the amount of 281.92 kW. Exergoeconomic results reveal that the compressors in CO2 compression unit along with ammonia absorber and stripper have the highest importance among equipment. [ABSTRACT FROM AUTHOR]

In the present study, classical and proposed methods were used to investigate the monthly precipitation characteristics of 30 stations in the southeastern United States during 1968-2018. Maximal overlap discrete wavelet transform (MODWT) as preprocessing method and K-means clustering method were used. First, the monthly precipitation time series of stations were decomposed into several subseries using MODWT and considering db as the mother wavelet. Then, the energy values of theses subseries were calculated and used as inputs in K-means and radial basis functions (RBF) methods. The optimum number of clusters obtained for the considered stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the data correlation was evaluated by variogram. Based on the results of clustering and in accordance with the latitude and longitude variations of the stations, it was found that with increasing the energy of the clusters, the amount of precipitation in the stations decreased and vice versa. The silhouette coefficient of clustering for the classical method obtained was 0.3 and for the proposed method it was 0.8, which indicates better clustering of the selected area using the proposed method. [ABSTRACT FROM AUTHOR]

In general, total roughness coefficient in open channels includes both grain resistance and bedform resistance. Due to the nonlinearity of the roughness coefficient, an accurate prediction of the bedform roughness is difficult. In this study, the capability of artificial neural network and multilayer perceptron (MLP) with firefly algorithm (MLP-FFA) were assessed in predicting the form resistance in channels with dune bedform. In this regard, different input combinations based on flow, bedform, and sediment characteristics were developed in order to determine the best combination. Five different experimental data series were applied to train and test the models. It was found that in predicting the form resistance, the model which took the advantages of both flow and sediment characteristics yielded to better outcomes. It was observed that the bedform characteristics led to an improvement in models accuracy. The results of the sensitivity analysis showed that the Reynolds number and the relative discharge were more effective parameters in the modeling process. Also, investigating the dune geometry (i.e., relative dune height) showed that the densimetric Froude number was the most significant variable. [ABSTRACT FROM AUTHOR]

Viscosity can be mentioned as one of the most crucial properties of nanofluids due to its ability to describe the fluid resistance to flow, and as the result it affects other phenomena. The effects of nanofluids' viscosity on different parameters can be enumerated as pressure drop, pumping power, feasibility of the nanofluid, and its convective heat transfer coefficient. In this investigation, the viscosity of TiO2/water nanofluid was compared and analyzed with experimental data. The primary goal of this investigation was to introduce a combination of experimental and modeling approaches to predict viscosity values using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. The regression diagram of experimental and estimated values shows an R2 coefficient of 0.995 and 0.993 for training and testing sections of the ANFIS model. These values for MLP-ANN, RBF-ANN, and LSSVM models were 0.998 and 0.999, 0.996 and 0.997, and 0.997 and 1.000 for their training and testing parts, respectively. Furthermore, the effect of different parameters was investigated using a sensitivity analysis which demonstrates that the average diameter can be considered as the most affecting parameter on the viscosity TiO2–water nanofluid with a relevancy factor of 0.992123. [ABSTRACT FROM AUTHOR]

The thermodynamic and economic analysis of a transcritical carbon dioxide power cycle is coupled with solar thermal subsystem and LNG subsystem. Solar thermal subsystem consists of parabolic trough collectors and a thermal storage tank, and the Copper-Therminol VP1 nanofluid is used in this subsystem. In most hours of the day, the solar subsystem based on Copper-Therminol VP1 nanofluid has a better thermal performance, which includes the output temperature of the collector and temperature of the storage tank, compared to the solar subsystem based on Therminol VP1. LNG subsystem is employed as a heat sink of the transcritical CO2 power subsystem, as well as the power generation by LNG turbine. The exergoeconomic analysis is performed to evaluate the effects of the key parameters, including turbine inlet temperatures and pressures, condensate pressure, CO2 mass flow rate and LNG pressure, on the exergy efficiency and product cost rate. In addition, parameter optimization is conducted via genetic algorithm. TOPSIS decision making technique is employed to select optimum point. System is capable of producing power with exergy efficiency of 8.53%, and product cost rate is equal to 2.09 million dollars per year, under optimum conditions. The values of exergoeconomic variables for each component of the system are calculated. The results represent that solar collector, evaporator, condenser, CO2 turbine and LNG turbine have the highest total cost rate of exergy destruction and investment (C ˙ d + Z ˙) , respectively. The highest amount of investment cost rate Z ˙ occurs in solar collector. The condenser has the lowest value of exergoeconomic factor which indicates that the costs associated with condenser come from exergy destruction rate. [ABSTRACT FROM AUTHOR]

The thermophysical specifications of working fluid play a key role in thermal performance of various types of heat pipes. Fluids with high thermal conductivity, low viscosity and surface tension are more favorable to be applied in heat pipes. In order to have fluids with higher thermal conductivity, adding nanoparticles can be an acceptable idea. In the present study, the effects of using nanofluids in several types of heat pipes are reviewed. The nanofluids are categorized based on the types of particles (as carbonic, metallic, etc.). Based on the results of the literature review, applying nanostructures in the base fluid can significantly reduce the thermal resistance of heat pipes compared with utilizing pure as operating fluid. For instance, it is observed that using graphene oxide/water nanofluid in pulsating heat pipe reduces the thermal resistance up to 42% in comparison with the water-filled heat pipe. In addition, reviewed studies revealed that the type of nanoparticle, concentration and their stability are among the most important parameters affecting thermal performance. The enhancement in thermal performance of heat pipes by using nanofluid is mainly attributed to higher thermal conductivity of the nanofluids and increase in nucleation sites. [ABSTRACT FROM AUTHOR]

In this study, a mixture of nano-encapsulated phase change materials in water has been used as the working fluid in a pulsating heat pipe in order to investigate its thermal performance. The results of the experiments showed that using the nano-encapsulated paraffin dispersed in water as working fluid improves heat transfer and decreases the thermal resistance of the pulsating heat pipe. On the other hand, among the tested concentrations for nano-encapsulated paraffin, there is an optimal value in which increasing the concentration causes an increase in the thermal resistance of the pulsating heat pipe due to the increased dynamic viscosity of the fluid. Enhancement in the thermal performance of the pulsating heat pipe by adding paraffin nanocapsules is attributed to the increase in effective specific heat of the fluid mixture as a result of the latent heat of paraffin nanocapsules. Higher effective sensible heat leads to an augmentation of convective heat transfer which has the main role in heat transfer of heat pipes. Moreover, the existence of nano-encapsulated paraffin can result in more mixing which is another reason for heat transfer enhancement. In addition, the use of nano-encapsulated phase change paraffin at high concentrations causes a cessation in the dry-out phenomenon at higher heat input compared with water or, in other words, increases the operating range of the pulsating heat pipe, which is due to an increase in the heat capacity of the fluid. In addition, the effect of inclination angle (30°, 60°, and 90°) on the thermal performance of the PHP at an optimum concentration among tested concentrations of nano-encapsulated paraffin was investigated, which was equal to 6.25 g L−1. It was observed that the PHP had its best thermal performance at the vertical position. The lowest measured thermal resistance of the PHP was 0.86 K W−1 in a vertical orientation and 50 W heat input. Moreover, at 30°, the thermal resistance of the PHP sharply increased, which can be attributed to the reduced effect of gravity in fluid return from the condenser to the evaporator. The maximum thermal resistance of the PHP observed in the case of inclination angle equal to 30° and 10 W heat input was 5.38 K W−1. [ABSTRACT FROM AUTHOR]

Hydrogen as a CO2-free fuel has been considered as a serious alternative for problematic fossil fuels in recent decades Photoelectrochemical (PEC) water splitting is a developing solar-based technology for hydrogen production. In this study, some possible options for upgrading this technology from R&D stage to prototype stage through a material selection approach is investigated. For these purpose, TOPSIS algorithm through a multi criteria decision making (MCDM) approach was utilized for evaluating different (PEC)-based hydrogen production materials. TiO2, WO3 and BiVO4 as three semiconductors known for their PEC application, were selected as alternatives in this decision-making study. After defining a set of criteria, which were assessed based on similar studies and experts' visions, a group of ten PEC-experts including university professors and PhD students were asked to fill the questionnaires. The eight criteria considered in this study are include "Study Cost", "Synthesis Simplicity", "Facility & Availability", "Deposition capability on TCO", "Modifiability", "Commercialization in H2 production", "Physical and Chemical Durability" and "Eco-friendly Fabrication". The final TOPSIS results indicates that TiO2 is selected as the best semiconductor for further investments in order to upgrade the PEC-based hydrogen production technology from R&D level to prototype stage. [ABSTRACT FROM AUTHOR]

High consumption of fossil fuel resources has always been associated with large amounts of pollutants' production. Due to this, Tehran weather condition reaches emergency state every year and causes a health threat to residents. Residential sector consumes over 40% of the total energy in Iran and causes a high amount of emission. In this article, the amount of loss reduction is calculated when new insulations are used (instead of stone-wool) in a building located in district 3 of Tehran. Innovative insulations involve the use of expanded polystyrene (EPS) and hollow silica nanospheres (HSN). To validate the previous part and observe the wall condition in detail, walls with current and new insulations are modelled by ANSYS FLUENT. Results indicate that EPS and HSN reduce total energy demand in the studied apartment by 1.2% and 5.1%, respectively. Finally, Tehran's under-construction buildings during 2015 are assumed to utilize mentioned insulations to observe their effects. The annual amount of reduced six air pollutants (CO2, SPM, CO, SO2, CH4 and NOx) by using EPS and HSN are 16,681 and 60,712 tonnes, respectively. It is found out that by using EPS and HSN, CO2 external costs can be decreased by 4,376,846$ and 15,929,919$ per year. [ABSTRACT FROM AUTHOR]

Renewable energies have many advantages and their importance is rising owing to gravely mounting concerns for environmental issues and lack of fossil fuels in the future. Solar energy, well acknowledged as an inexhaustible source of energy, is developing dramatically for different purposes such as desalination and electricity generation. Appropriate solar power plant is very important factor for power generation due to its cost and other constraints. The applied technology is as important as the solar power plants location. In this paper, a wide variety multi criteria decision making (MCDM) methods, investigated by various researchers, are presented to obtain effective criteria in selecting solar plants sites and solar plants technologies. There is not any comprehensive research providing all required criteria for decision making for site and technology selection. Based on the reviewed researches, weight of each criterion depends on many factors such as region, economy, accessibility, power network, maintenance costs, operating costs, etc. The important criteria for site selection are represented and investigated thoroughly in this review paper. [ABSTRACT FROM AUTHOR]

In this study, a model is proposed by applying the least squares support vector machine (LSSVM). In addition, genetic algorithm is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. In addition to temperature and concentration of nanoparticles, the parameters which are used in most of the modeling procedures for thermal conductivity, the effect of particle size is considered. By considering the size of nanoparticles as one of the input variables, a more comprehensive model is obtained which is applicable for wider ranges of influential factor on the thermal conductivity of the nanofluid. The coefficient of determination (R2) for the introduced model is equal to 0.9902, and the mean squared error is 8.64 × 10−4 for the thermal conductivity ratio of Al2O3/EG. [ABSTRACT FROM AUTHOR]

Growing energy demand, rising greenhouse gas emissions and the depletion of fossil fuels necessitates the development of renewable energy sources. In order to improve renewable energy utilization, it is necessary to determine the most important factors influencing energy strategy. The energy system of Iran is highly dependent on fossil fuels; however, Iran has a high potential for solar energy development and several policies are being pursued by the government to develop power generation by renewable energy resources. This study presents and discusses several important parameters of energy policy and security which make the installation of photovoltaic (PV) systems crucial for the future. Installation of solar PV will improve energy security, foster job creation and reduce environmental problems. It also introduces and investigates those institutions and organizations involved in developing renewable energies, especially solar energy. These include the Ministry of Energy and universities, both of which play key roles in the development of solar energy. [ABSTRACT FROM AUTHOR]

The thermal energy of the sun can be used for electricity generation. Solar thermal energy can be applied with fossil fuels or independently in order to reduce the cost of generated electricity and CO2 emission. Several cycles are introduced to extract solar thermal energy to be used in power plants. Brayton cycles, Rankine‐Brayton cycles, and supercritical Brayton cycles are among the most conventional ones. Based on the reviewed researches, using solar energy in addition to fossil fuels results in lower carbon dioxide emission and lower levelized cost of the generated electricity. Moreover, thermodynamic and economic analyses of the cycles revealed that heat recovery leads to higher efficiency while increase capital cost. The efficiency of solar‐assisted gas turbines depend on various parameters including pressure ratio, turbine inlet temperature, heat absorber geometry and the performance of the components. The enhancement in the efficiency of the cycles by applying each method depends on the configuration, operating condition. For instance, results have shown that 10% increase in turbine efficiency can led to 6%‐12% improvement in the efficiency of a closed‐Brayton cycle. Applications of gas turbines in various thermodynamic cycles are reviewed. Higher heat recovery will improve efficiency; however, it leads to more investment cost. Solar energy can be utilized in order to provide the heat source for various power generation cycles. Using solar Rankine‐Brayton cycles is one of the most appropriate approaches to obtain higher overall efficiency and power output. [ABSTRACT FROM AUTHOR]

Thermal conductivity and dynamic viscosity play key role in heat transfer capacity of nanofluids. In the present study, thermal conductivity and dynamic viscosity of Fe2O3/water are modeled by applying various artificial neural network algorithms. The applied algorithms are MLP, GA-RBF, LSSVM, and CHPSO ANFIS algorithms. The data for modeling procedure are extracted from several experimental studies. Obtained results by the different algorithms are compared and it was concluded that the highest R-squared values belonged to GA-RBF algorithm which were equal to 0.9962 and 0.9982 for thermal conductivity ratio and dynamic viscosity, respectively. [ABSTRACT FROM AUTHOR]

Hydraulic jump is a useful means of dissipating excess energy of a supercritical flow so that objectionable scour downstream is minimized. The present study applies gene expression programming (GEP) to estimate hydraulic jump characteristics in sudden expanding channels. Three types of expanding channels were considered: channels without appurtenances, with a central sill, and with a negative step. 1,000 experimental data were considered as input data to develop models. The results proved the capability of GEP in predicting hydraulic jump characteristics in expanding channels. It was found that the developed models for channel with a central sill performed better than other channels. In the jump length prediction, the model with input parameters Fr1 and (y2-y1)/y1, and in the sequent depth ratio and relative energy dissipation prediction the model with input parameters Fr1 and y1/B led to more accurate outcomes (Fr1, y1, y2, and B are Froude number, sequent depth of upstream and downstream, and expansion ratio, respectively). Sensitivity analysis showed that Fr1 had the key role in modeling. The GEP models were compared with existing empirical equations and it was found that the GEP models yielded better results. It was also observed that channel and appurtenances geometry affected the modeling. [ABSTRACT FROM AUTHOR]

Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F1 (Froude number) and (h2--h1)/h1 (h1 and h2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F1 and h1/B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM. [ABSTRACT FROM AUTHOR]

Introduction: Iron plays an important role in physiological processes as a trace element. Today, iron oxide nanoparticles have attracted extensive attention due to their super paramagnetic properties and a variety of potential applications in many fields. The main objective of this study was to evaluate in vitro and in vivo toxic effects of the iron oxide nanoparticles on L929 cell line, kidney and liver function, in order to achieve a safe application of the mentioned nanoparticles. Methods: The toxicity effects of 200 and 800 μg/ml iron oxide nanorods on L929 were determined using (3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide )MTT( test. One and 24 h after the injection of iron oxide via tail vein, serum iron, blood urea nitrogen (BUN) and liver enzymes (ALT, AST, and ALP) were measured as indicators of the kidney and liver function, respectively. Histopathological studies on liver and kidney was carried out using light microscopy following tissue processing steps and standard H&E staining. Results: The viability of the cells exposed to iron oxide nanorods, was decreased with increasing dose. No significant differences were observed between biochemical factors, 1 and 24 h after the injection of nanoparticles. Serum iron level showed no difference with control 1 h after the injection. However, it exhibited a significant increase 24 h after the injection as compared to the control. Meanwhile, pathological studies did not show any acute toxic damage. Conclusion: Use of the iron oxide in short-time and in doses less than 800μg/ml may be safe. More studies are needed for accurate assessment of the toxicity of these particles in terms of dose time and the type of covering. [ABSTRACT FROM AUTHOR]

Correction to: Water Resour Manag https://doi.org/10.1007/s11269-021-02974-5 In this article the affiliation "Near East University, Faculty of Civil and Environmental Engineering, Nicosia, via Mersin 10, Turkey" is added to Author Vahid Nourani. The original article can be found online at https://doi.org/10.1007/s11269-021-02974-5. [Extracted from the article]

The type of solar panels has a great impact on the optimal sizing of a hybrid photovoltaic–battery scheme. The optimization of these schemes based on a powerful optimization approach results in more cost-effective schemes. In this paper, a new global dynamic harmony search method, as an optimization method, is presented for the optimal sizing of a hybrid photovoltaic–battery scheme. The new optimization method is aimed at minimizing the total cost and loss of load supply probability of the scheme at the same time. In this regard, the effect of the type of solar panels on the optimal sizing of the hybrid scheme is investigated. Furthermore, performance optimizations are performed with an original global dynamic harmony search, an original harmony search, and simulated annealing to determine the effectiveness of the suggested optimization method. The effects of the initial costs and efficiency of monocrystalline and polycrystalline solar panels on the optimization of hybrid systems are analyzed. The superiority of the suggested method in terms of time and cost indicators of the hybrid scheme is presented comparing the other algorithm. [ABSTRACT FROM AUTHOR]

Carbon capture and utilization (CCU) may offer a response to climate change mitigation from major industrial emitters. CCU can turn waste CO2 emissions into valuable products such as chemicals and fuels. Consequently, attention has been paid to petrochemical industries as one of the best options for CCU. The largest industrial CO2 removal monoethanol amine-based plant in Iran has been simulated with the aid of a chemical process simulator, i.e., Aspen HYSYS® v.10. The thermodynamic properties are calculated with the acid gas property package models, which are available in Aspen HYSYS®. The results of simulation are validated by the actual data provided by Kermanshah Petrochemical Industries Co. Results show that there is a good agreement between simulated results and real performance of the plant under different operational conditions. The main parameters such as capture efficiency in percent, the heat consumption in MJ/kg CO2 removed, and the working capacity of the plant are calculated as a function of inlet pressure and temperature of absorber column. The best case occurred at the approximate temperature of 40 to 42 °C and atmospheric pressure with CO2 removal of 80.8 to 81.2%; working capacity of 0.232 to 0.233; and heat consumption of 4.78 MJ/kg CO2. [ABSTRACT FROM AUTHOR]