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In recent decades, water scarcity has turned into a serious problem spanning many countries, now even capable of causing or inflaming ethnic and national conflicts. While our planet has very limited freshwater resources, it has huge amounts of saltwater in seas and oceans. There is a very limited number of ways that can make saltwater drinkable, the most important of them is desalination. This study aimed to provide a method for the simultaneous optimization of desalination plant location and its water distribution network based on mathematical modeling. For this purpose, the authors formulated a non-linear mathematical model with the objective of minimizing the costs of water production and transmission. A genetic algorithm was also developed for solving the proposed nonlinear model. The method was used in a case study of Sistan and Baluchestan, which is one of Iran's most water stressed provinces. The proposed genetic algorithm managed to provide an acceptable solution for this problem in 3.74 s. The best solution was found to be constructing a desalination facility with a capacity of 394,052 cubic meters per day in a single location, that is, the city of Chabahar. The water transmission lines needed for transporting water to other parts of the province and their capacities were also determined.

Hydropower boasts the capability to consistently generate electricity throughout the year, offering the lowest operating costs and the longest lifespan among renewable energy technologies. Given the aforementioned considerations and the absence of prior investigations into Iran's hydropower potential, this study employs HOMER software to explore the feasibility of supplying electricity to a village comprising 10 households near Koohrang Tunnel in Chaharmahal and Bakhtiari Province, leveraging solar, wind, and hydro turbine renewable energies. Three distinct scenarios, centered around hydro turbine utilization, were examined. These scenarios aimed to provide electricity in off-grid (1st scenario) and grid-connected modes (2nd scenario), as well as to generate electricity and hydrogen in a grid-connected mode (3rd scenario). In the first scenario, the most economically viable design yielded a cost of $0.187 per kWh of generated electricity, with 99% of the electricity sourced from the hydro turbine and the remaining 1% from a diesel generator. This scenario resulted in a CO2 emission of 23.2 kg/y. In the second scenario, the most cost-effective option supplied 94% of the electricity from the hydro turbine and the remaining portion from the main grid, at a cost of $0.033 per kWh. Notably, surplus electricity sold to the main grid facilitated an annual reduction of 1297 kg of CO2 emissions. The third scenario, which combined the hydro turbine with the main grid, presented the most financially viable option. Here, the costs of per kWh of generated electricity and per kg of produced hydrogen were $2.012 and $0.49, respectively.

Compared to coal and other fossil fuels, renewable energy (RE) sources emit significantly less carbon dioxide (CO2). In this sense, switching to such sources brings many positive effects to the environment through mitigating climate change, so the terms green energy and clean energy, have been derived from these constructive environmental impacts. Given the utmost importance of RE development, the primary objective of this study was to identify and prioritize the effective RE development strategies in Mazandaran Province, Iran, using different methods, including the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, along with other decision-making techniques. Recruiting a team of 11 industrial and academic experts, the strategies to implement in this region were developed in line with the RE development plans. For this purpose, the Multi-Criteria Decision-Making (MCDM) methodologies were utilized within the gray fuzzy environment to manage the existing uncertainties. The Gray-Additive Ratio Assessment System (Gray ARAS) was further applied to rank the main factors at each level. According to the SWOT analysis and the Stepwise Weight Assessment Ratio Analysis (SWARA) outcomes, among the major factors shaping RE development in Mazandaran Province, Iran, the economic criterion, with the final weight of 0.24, was ranked first; and then the geographical and environmental criteria, having the final weights of 0.23 and 0.19, were put in the second and third places, respectively. In this regard, appropriate location, with the final weight of 0.226, was ranked first; and subsequently pollution reduction and energy production costs, receiving the final weights of 0.103 and 0.094, were the second and third sub-criteria, respectively. As a final point, the validation results based on the Gray-Weighted Aggregated Sum Product Assessment (Gray-WASPAS) and ranking obtained through the Gray-ARAS were confirmed.

The health insurance system can play an effective role to control health expenditures. The purpose of this study is to provide a model for estimating the physician visit tariffs. To achieve this goal, a hybrid model was used. fuzzy logic is the most appropriate tool for controlling systems and deriving rules for the relationship between inputs and outputs. So, the output of the data mining techniques enter the fuzzy logic as an input variable. The data were collected from the Health Insurance Organization of Iran in two sections including the physicians' costs and physicians' deductions. Owing to the techniques used in this model, NN had the least error, as compared to other data mining techniques (0.0034 and 0.0013, respectively). After defining the variables, membership functions and fuzzy logic rules, the accuracy of the whole control model was confirmed by random data. This research has dealt with the domains of health insurance , their connections and defining effective variables better and more extensively than the other studies in the field.

Abstract Natural air ventilation in the hot‐dry regions plays a key role to decrease indoor air temperature in hot season, also to improve thermal comfort during the cold season. One of the most common ways to take advantage of natural ventilation is using wind catcher with an underground tunnel. In this method, the tower catches the airflow and directs it to the underground tunnel to decrease the air temperature by transferring heat to the ground, which is cooler in the summer and warmer in the winter. Earth‐to‐air heat exchanger (EAHE) is a modern form of wind catcher with underground tunnel. In this method, air after passing through buried pipes exchanges heat with the ground, and its temperature increases in the winter and decreases during the summer. This study analyzes the energy performance and cost‐effectiveness of earth‐to‐air heat exchanger to be utilized in a residential building in climate condition of the province of Kermanin Iran. In this regard, 9 different configurations of the EAHE are investigated to find the optimized EAHE. The system performance and cost‐effectiveness are studied in 3 different depths including 1, 2, and 3 m with 3 different pipe lengths including 25, 50, and 75 m. The results show that the EAHE is capable of reducing the inlet air temperature by 0.5‐9.9℃ in the summer and increasing it by 0.9‐11.2℃ in the winter. Furthermore, by integrating the EAHEs in a building design, one can reduce the total annual cooling load by 1.25%‐3.97% and for heating by 1.34%‐3.96%. The payback period of the system with a pipe length of 25 m is 16 years, but for the systems with pipe lengths of 50 and 75 m, this period reduces to almost 3 years.

Wind power as a renewable source of energy has numerous economic, environmental, and social benefits. To enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) was used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP optimized by the Genetic Algorithm (MLP-GA) and standalone MLP without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the MLP-GA and standalone MLP model. In other words, the hybrid MLP-WOA models, with acceptable performances, reduced the RMSE values from 0.570∼2.995 to 0523∼2.751. Also, the obtained results indicated that the examined MLP-GA did not have a significant effect in increasing the estimation accuracy of standalone MLP models. It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.

In the view of the latest status and the potential of developing wind energy in South Africa, the present study aims to perform technical–economic–environmental​ analysis on a wind turbine system with HOMER software using the 20-years average data of the wind speed obtained from NASA’s database, for providing the electricity to residential buildings. The results showed that the Port Elizabeth station, had the lowest levelized cost of electricity (LCOE) with the value of -0.363 $/kWh when using the EOLO wind turbine, and the Bloemfontein station had the highest LCOE with the value of 1.601 $/kWh when using the Turby wind turbine. The results from the step-wise assessment ratio analysis (SWARA) weighting method demonstrated the wind penetration, total production, and capital cost as the most important sub-indices with the weights of 0.106, 0.095, and 0.091, respectively. Using the additive ratio assessment (ARAS), weighted sum method (WSM), and weighted aggregated sum product assessment (WASPAS) techniques, the cities under study were ranked, and the cities of East London and Bloemfontein were identified as the most suitable and the most unsuitable stations for the use of household-scale wind turbines, respectively.

A decrease in the utilization of fossil energies, mainly by replacing them with renewable energy sources (RESs), is regarded as a potential energy source in today’s applications. RESs are broadly utilized for heating purposes and particularly with applications in solar water heater (SWH). Despite the accessibility of SWH technologies and their affordable prices in Iran, there is no comprehensive study to explain the potential of Iranian regions to supply hot water for household applications. This one-year work, hence, attempts the first dynamical simulation of a solar heating system to provide sanitary hot water (SHW) as well as hot water demanded to heat 47 stations in Iran. Weather data were extracted from METEONORM and environmental-technical analyses performed by thermal solar (TSOL) software. Stations were ranked based on CCR and BCC models in data envelopment analysis (DEA) method using GAMS V 24.1. As with results, a total of 223.1 MWh solar heat is generated annually from all stations that prevent the emission of 64.5 t CO2 every year. According to CCR and BCC models, Bandar Abbas, Chabahar, Fasa, Iranshahr, Kermanshah, Khoramabad, Sarab, Shahr-e-kord, Yasuj, Zanjan, and Zahedan are the best in this regard. Also according to the economic analysis, the average price of home solar heating in Iran is 0.160 $/kWh.

Currently, solar energy is considered one of the most suitable options for overcoming the problems of fossil fuel depletion, and global warming. Also, the high costs associated to photovoltaic systems, renders the maximum utilization of solar cells, a fundamental and undeniable necessity. Technical-economic-environmental analysis, using HOMER software, was performed under four different scenarios: without the tracker, with the horizontal axis tracker, with the vertical axis tracker, and with the dual-axis tracker. Consequently, the best configuration was chosen for each scenario. The optimal system for all four scenarios, in the circumstance of disconnection from the grid, only involves the solar cells, while in the circumstance of connection to the grid; both the solar cells and the wind turbine are included. The results demonstrate that in the off grid situation, the scenario involving the use of a vertical axis tracker would be the most cost-effective, with the price of 0.812 $/kWh for energy produced, while the lowest price for producing one kg of hydrogen is $77.97 is attributable to the scenario without the use of a solar tracker. In the circumstance of being connected to the power grid, the scenario involving the use of a vertical axis tracker would be most cost-effective, with the price of each kWh of energy produced equal to $ 0.223. At the price of $29.33 per kilogram, the scenario involving the use of a vertical axis tracker would also be most suitable for the production of hydrogen. Another important fact revealed through the results, is the crucial role of dump load, in the provision of the heat required in an off-grid situation. However, dump load is not associated to heat provision in a grid-connected situation.

In hot and dry regions, air conditioning is used for many different applications like residential, industry, and agriculture and dairy products. This research studies the applicability of wind and solar energies for cooling fruit storage warehouse in the hot and dry region of Yazd in Iran. The studied case is a fruit warehouse with an area of 4240 m2 resulting in a storage capacity of about 1000 tons. For this purpose, the heat gain of the warehouse is determined, and the obtained cooling load is then used to examine the solar and wind energy to power a conventional warehouse system. Different scenarios are examined for this research such as solar air conditioner, solar absorption chiller, wind catcher, and a combination of solar air conditioners and solar absorption chiller for cooling the fruit warehouse. Comparison and economic evaluation of different scenarios show that the solar air conditioning ranks first for this purpose. Results are then validated using value engineering methodology. Solar air conditioning with the highest net present value (NPV) of 4,865,040,418 Rials and the best internal rate of return (IRR) value of 182.98 % was determined to be the best approach among the studied methods. The results of this research can be applied to other regions with similar climatic conditions too.

Fuel cells are potential candidates for storing energy in many applications; however, their implementation is limited due to poor efficiency and high initial and operating costs. The purpose of this research is to find the most influential fuel cell parameters by applying the adaptive neuro-fuzzy inference system (ANFIS). The ANFIS method is implemented to select highly influential parameters for proton exchange membrane (PEM) element of fuel cells. Seven effective input parameters are considered including four parameters of semi-empirical coefficients, parametric coefficient, equivalent contact resistance, and adjustable parameter. Parameters with higher influence are then identified. An optimal combination of the influential parameters is presented and discussed. The ANFIS models used for predicting the most influential parameters in the performance of fuel cells were performed by the well-known statistical indicators of the root-mean-squared error (RMSE) and coefficient of determination (R2). Conventional error statistical indicators, RMSE, r, and R2, were calculated. Values of R2 were calculated as of 1.000, 0.9769, and 0.9652 for three different scenarios, respectively. R2 values showed that the ANFIS could be properly used for yield prediction in this study

While Afghanistan's power sector is almost completely dependent on fossil fuels, it still cannot meet the rising power demand of this country. Deploying a combination of renewable energy systems with hydrogen production as the excess energy storage mechanism could be a sustainable long-term approach for addressing some of the energy problems of Afghanistan. Since Badakhshan is known to have a higher average wind speed than any other Afghan province, in this study, a technical, economic, and carbon footprint assessment was performed to investigate the potential for wind power and hydrogen production in this province. Wind data of four stations in Badakhshan were used for technical assessment for three heights of 10, 30, and 40 m using the Weibull probability distribution function. This technical assessment was expanded by estimating the energy pattern factor, probability of wind speeds greater than 5 m/s, wind power density, annual power output, and annual hydrogen output. This was followed by an economic assessment, which involved computing the Leveled Cost Of Energy (LCOE), the Leveled Cost Of Hydrogen (LCOH), and the payback period, and finally an carbon footprint assessment, which involved estimating the consequent CO2 reduction in two scenarios. The assessments were performed for 22 turbines manufactured by reputable companies with capacities ranging from 600 kW to 2.3 MW. The results showed that the entire Badakhshan province, and especially Qal'eh-ye Panjeh and Fayazabad, have excellent potentials in terms of wind energy that can be harvested for wind power and hydrogen production. Also, wind power generation in this province will be highly cost-effective, as the produced electricity will cost about one-third of the price of electricity supplied by the government. For better evaluation, the GIS maps of wind power and hydrogen outputs were prepared using the IDW method. These maps showed that the eastern and northeastern parts of Badakhshan province have higher wind power-hydrogen production potentials. The results of ranking the stations with SWARA-EDAS hybrid MCDM methods showed that Qal'eh-ye Panjeh station was the best location to produce hydrogen from wind energy.

Solar energy is a free and environmentally friendly supply of power that has negligible impact on the environment, and it has long been used by humans through different methods. However, solar energy technology would use for heating water for buildings as an alternative resource, which would help reduce CO2 reduction and safe environment. A data analysis was performed using the RETScreen software, and the financial outcomes that were calculated using two methods. Clearly, the software is operational with two different methods. Both methods indicate that this project is economically feasible. The aim of this study is to evaluate the utilization of solar energy for household and commercial purposes. The first goal is to explore the technical, economic, and environmental aspects of using evacuated glass tube solar collectors to heat water in an indoor pool in the building, having an area of 50 m2, in the city of Yazd, Iran. The second goal is to evaluate the amount of hydrogen that can be obtained from the installation photovoltaic systems in the province. The results also show that this project will decrease greenhouse gas emissions by 142 ton-CO2 over 20 years of the useful life of a collector, thereby indicating the possible significant role of such collectors in the reduction of greenhouse gas emissions. Furthermore, the findings indicate that installing a single X21-345 photovoltaic system with a performance rate of 20% can result in the production of 2.1 kg of hydrogen annually.

The electricity consumption in residential/office buildings corresponded to 45% of the total annual electricity demand in hot-arid climates. This accounted for 27.2 TWh of electricity consumption with 14.2 MWh/capita/year in Kuwait. In this research, four offices in an educational building were equipped with a meteorological data logging system using temperature, humidity, and illuminance sensors. All four offices had double-glazed windows. Moreover, two offices were equipped with two types of commercially available window films. Two million data were stored in iCloud using Wi-Fi and an Internet of Things (IoT) system for the 3 months of June, July, and August 2019. Here, histograms and the kernel density estimation (KDE) of temperature/humidity were analyzed and compared for the two offices with/without 3M Neutral 20 window films. Two floors of the same building consisting of 31 offices were also modeled and simulated to study energy saving and CO2 footprint reduction using various window films. The results of simulations for the month of July 2019 using SOL 101 and SOL 102 window films, respectively, showed that about 250 kg and 255 kg of production of CO2 could be reduced and energy saving counted for 416 and 422 kWh. Measurements from offices with 3M Neutral 20% and 3M Neutral 70% window films for the month of July 2019 indicated that the carbon footprint could be reduced by about 82 kg and 0.43 kg and energy saving counted for 147.11 and 0.71 kWh, respectively. It was observed that an annual energy saving and CO2 footprint reduction of 2.76% could be achieved using window films in a hot-arid climate.

Algeria is a wealthy country with natural resources, namely, nuclear, renewable, and non-renewable sources. The non-renewable energy sources are considered the lion’s share for energy production (98%). Algeria’s efforts to ensure and strengthen its energy security will take an important step in the coming decades by commissioning new energy infrastructure based on intensive use of water, coal, nuclear, non-renewable, and renewable sources. The implementation of new power infrastructure is expected to be operational from 2030. The renewable power realization in Algeria is relatively less compared to other African countries, i.e., Morocco, Egypt, South Africa, etc. The total renewable power installed capacity in Algeria reached 686 MW in 2020, as part of its national energy portfolio, although the Algerian government has spent tremendous efforts on introducing new sustainable technologies to enable the transition towards a cleaner and sustainable energy system. Indeed, the country announced its plan to install around 22 GW of renewable energy capacity by 2030. It will include 1 GW bio-power from the waste, 13.5 GW from solar PV, 2 GW from CSP, 15 MW from geothermal, 400 MW cogeneration, and, finally, 5 GW from wind. The scope of the present research provides general information about the usage of energy resources such as fossil, nuclear, and renewable sources in Algeria and also covers the energy supply outlook. The present effort is the first of its kind which discusses the application of the coal and nuclear as clean energy sources as part of renewable energy transition. Additionally, it also includes the description of the existing Algerian energy sector and information about water and water desalination and their usage in other sectors.

Solar energy is the Earth's primary source of energy and the most widely available source of renewable energy. Its use in cooking is just one of its numerous applications and benefits. By concentrating solar energy into a small area, a solar cooker is capable of generating sufficient heat to cook, pasteurise and sterilise food items. This paper aims to identify factors affecting solar cooker 1) manufacturing and development and 2) project implementation in the Razavi Khorasan province of Iran. The necessary data was collected via an expert panel and a questionnaire. Confirmatory factor analysis (CFA) showed that factors such as Capital, Financial support, Economy, Technology and infrastructure, and Interactions directly impacted the successful promotion of solar cookers in Iran. While all these factors affected solar cooker development positively, Interactions had the most profoundly positive influence. The results of this research would benefit solar cooker manufacturers and users either living in the region and in other suitable locations.

Pakistan is the world’s sixth-most populous country with a semi-industrialized economy. It has been always an energy importer and dependent on fossil fuels. Great pressure is imposed on Pakistan’s national grid from the rise in fossil fuel costs, variations in the annual interest rate, and increased costs of greenhouse emissions. To meet the ever-increasing energy demand, the Government of Pakistan has decided to further harness wind and solar energies currently having a negligible share in Pakistan’s energy portfolio. Despite the importance of this issue, no study has been conducted so far on the cogeneration of power, heat, and hydrogen in Pakistan. Accordingly, this study is aimed at technical–economic–environmental sensitivity analysis of supplying electric and thermal loads of a residential building in Karachi by an off-grid wind-solar-fuel cell system. To this end, 4500000 possible cases were analyzed, simulated, and optimized with the HOMER software using 20-year average meteorological data from the NASA website. A sensitivity analysis was performed on this system for the first time in Pakistan. The other novelties are the use of dump loads for converting the surplus electricity into heat and also heat recovering in the fuel cells. The results showed the great potential of the station understudy for supplying the required power and heat by renewable energies. Hydrogen production was also affordable at every emission penalty price with an interest rate of less than 9%. Moreover, dump loads play a key role in supplying the thermal demand. Comparison of the wind turbine–solar cell–fuel cell–battery system with the wind turbine–solar cell–battery and solar cell–battery systems indicated that the internal rate of return and the payback period were, respectively, 9.39% and 11.4 years and 11.7% and 11 years. According to these results, it is recommend that Pakistani authorities promote the use of renewable energies through incentives and investment subsidies.

An API standard drilling fluid was investigated from laminar to turbulent flow conditions using an in-house-built viscometer at speeds from 200 to 1600 RPM. A power-based method was applied to obtain the apparent viscosity and the shear stress of the water-based drilling mud (WBM) in the annulus of the viscometer. Then, a MATLAB optimization program was developed to obtain model parameters for five rheology models integrated in a generalized Herschel-Bulkley-Extended (HBE) model and two widely used 4-parameter models in drilling industry. It is found that the Bingham, Cross, and HBE rheology models have precisely matched the WBM measurements in the viscometer. A generalized Reynolds number was applied to determine the Darcy friction factor although the PL (power law model) and the HB (Herschel-Bulkley model) exhibited a nonrealistic negative shift from the laminar friction factor.

This work presents a feasibility study on the provision of electricity and hydrogen with renewable grid connected and off-the-grid systems for Bandar Abbas City in the south of Iran. The software HOMER Pro® has been used to perform the analysis. A techno-enviro-economic study comparing a hybrid system consisting of the grid/wind turbine and solar cell is done. The wind turbine is analyzed using four types of commercially available vertical axis wind turbines (VAWTs). According to the literature review, no similar study has been performed so far on the feasibility of using VAWTs and also no work exists on the use of a hybrid system in the studied area. The results indicated that the lowest price of providing the required hydrogen was $0.496 which was achieved using the main grid. Also, the lowest price of the electricity generated was $1.55 which was obtained through using EOLO VAWT in the main grid/wind turbine/solar cell scenario. Also, the results suggested that the highest rate of preventing CO2 emission, which was also the lowest rate of using the national grid, with 3484 kg/year was associated with EOLO wind turbines where only 4% of the required electricity was generated by the national grid. Keywords: Hybrid system, Off-the-grid, Solar radiation, Wind speed, Total net present cost, Energy cost

The growing awareness about the adverse effects of fossil fuels and the ensuing pollution has encouraged and promoted the use of renewable energies throughout the world. This in turn has encouraged governments and investors to show more interest in generation of renewable energy through construction of facilities such as wind farms. The novelty of this research work is the budget constraints consideration for the first time in wind energy projects. This work is done for the Khorasan provinces of Iran. This study is based on regional and municipal data pertaining to the year 2016, and uses the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to deal with the uncertainty of the data. The fuzzy TOPSIS method is used to evaluate and rank the candidate cities, and the results are then used to develop a budget-constrained model by fuzzy linear programming with the Maxmin objective function. The resulting mathematical model is optimised with GAMS software. The modelling results show that in the presence of budget constraints, of 10 Khorasan cities considered as candidate locations, Afriz, Rudab, and Fadashk exhibit the best conditions for the construction of wind farms.

In this research, the factors affectinguniversity employees’ motivation and productivity are identified and classified in seven groups; the impact of each motivation factor on the productivity is presented by ANP fuzzy model.Eight universities in Iran were analyzed in this research work. The aim of this study is to explore the productivity of employees. This paper attempts to give new insights intodesigning the portfolio factors, motivating employees for productivity improvement by implementing BLP and ANP fuzzy models.The research results show that there is a positive and significant relationship among reward system, motivation factors, and human resources productivity. In addition, among the options of reward system, the factors of internal (inherent) reward, non-financial external reward, and financial external reward had the highestimpact on increasing motivation and productivity factors. At the next stage, a BLP model is designed according to the importance and impact of each reward system option on motivation and productivity factors and organization limitations, including budget, facilities, and conditions to design portfolio factors motivating employees with the aim of improving productivity. The research results show that actualizing performance evaluation, receiving the feedback from the results of doing tasks by different ways, providing an opportunity for all employees to progress, coordination between job specifications and employees’ abilities, and a manager competency are very critical for improving the organization productivity.

The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

At present, urbanization is a very common phenomenon around the world, especially in developing countries, and has a significant impact on the land-use/land-cover of specific areas, producing some unwanted effects. Bangladesh is a tightly inhabited country whose urban population is increasing every day due to the expansion of infrastructure and industry. This study explores the land-use/land-cover change detection and urban dynamics of Gazipur district, Bangladesh, a newly developed industrial hub and city corporation, by using satellite imagery covering every 10-year interval over the period from 1990 to 2020. Supervised classification with a maximum likelihood classifier was used to gather spatial and temporal information from Landsat 5 (TM), 7 (ETM+) and 8 (OLI/TIRS) images. The Geographical Information System (GIS) methodology was also employed to detect changes over time. The kappa coefficient ranged between 0.75 and 0.90. The agricultural land was observed to be shrinking very rapidly, with an area of 716 km2 in 2020. Urbanization increased rapidly in this area, and the urban area grew by more than 500% during the study period. The urbanized area expanded along major roads such as the Dhaka–Mymensingh Highway and Dhaka bypass road. The urbanized area was, moreover, concentrated near the boundary line of Dhaka, the capital city of Bangladesh. Urban expansion was found to be influenced by demographic-, economic-, location- and accessibility-related factors. Therefore, similarly to many countries, concrete urban and development policies should be formulated to preserve the environment and, thereby, achieve sustainable development goal (SDG) 11 (sustainable cities and communities).

The world is moving towards the generation of electricity with renewable energy sources (RES) due to the deterioration of the green environment and trying to replace non-renewable energy resources. The real-time results are achieved with the help of an arm controller, having good controller efficiency with the Waijung toolbox, compatible with MATLAB using STM32ST-link utility. In this paper, the authors are focused on areas such as easy to implement controller efficiency, and real-time solutions for modified direct-control perturbation & observation (DC-P&O) technique based on 32- bit ARM Cortex microcontroller (STM32F407VGT6) with embedded programming using Waijung blocksets, which offers very expected outcomes of the problem to make the stand-alone system efficient with fast-tracking. The observation setup is tested with a 40-watt photovoltaic (PV) panel with resistive load for achieving its stability. The designed algorithm enhances the efficiency of the controller by 84.48% for the real-time parameters of the PV panel at maximum power point (MPP) for a 57% duty ratio.

The present study aimed at ranking and selecting the superior geothermal project for hydrogen production in 14 provinces of Iran using a multi-objective optimization fuzzy hybrid approach through analyzing the ratio (fuzzy Moora) and expanded entropy weighting method. In this research, the extended entropy weighing method and the Fuzzy-Moora approach were utilized to weigh the criteria and project the ranking, respectively. In this research, 13 criteria for ranking the geothermal projects in Iran have been selected for hydrogen production. At first, the technical-economic feasibility of the projects was carried out in Homer software, and then the ranking process was performed with the proposed hybrid approach. The results showed that among 14 studied provinces using geothermal energy, the provinces of Bushehr, Hormozgan, Isfahan, Mazandaran, East Azarbaijan, Fars, Qazvin, Zanjan, Ardebil, Khorasan Razavi, Kerman, Sistan and Baluchestan, South Khorasan and West Azarbaijan were ranked in that order in terms of hydrogen production.

The energy insecurity, environmental pollution, climate change and even reduced rainfall in some countries are prime examples of consequences of the world’s excessive reliance on fossil fuels. This study suggests that in some southern islands and coastal areas of Iran, two such problems, namely the growing shortage of potable water and air pollution can be addressed by building a wind-powered seawater desalination plant at the locations. To evaluate such project, first the sites that may provide the highest efficiency need to be identified. In this study, 10 ports and 5 islands in southern Iran, which suffer from water shortage but have access to seawater, are identified as preliminary candidate sites for such project. The criteria influencing the suitability of a location are considered to be wind power density, economic feasibility, topographic condition, frequency of natural disasters, population, and the wind farm’s distance from desalination facility. After analyzing and weighting the criteria, the locations are ranked using the ELECTRE III method, and the results are validated using the PROMETHEE method. In conclusion, the results of ranking techniques show that Qeshm Island is the best location for construction of a wind-powered seawater desalination plant.

Supply chain management (SCM) addresses the management of materials and information across the entire chain from suppliers to producers, distributors, retailers, and customer. The theory of supply chain management suggests that lead time reduction is a pioneer to the use of market mediation to reduce transaction uncertainty in the chain, which can be conceptualized as the primary goal of supply chain management. In the past few decades, scholars gave ample attention about the impact of inventory on SCM. This paper relates to the development of a lot sizing model for a single component multiple delivery system with variable demand and lead time of a multinational transformer company. Two models and the modification were developed on the basis of the following assumptions. For first model distribution of demand is normal, distribution of procurement lead time is exponential and the quantity is coming in a single lot. For second model distribution of demand is normal distribution of ‘procurement’ and ‘administrative delay’ lead time is exponential and the quantity is coming in a single lot. Modification of the first model has been done by taking the effect of multiple deliveries in the models and correcting the Re-order point as obtained from the previous models. The results were observed by the second model and analysis has been done for different parametric conditions. The effect of multiple deliveries is also taken into account. The optimum re-order point and economic ordering quantity with various different inputs have been discussed.

In this paper, the recent trend of the worldwide wind energy utilisation is reviewed and the recent activities in using renewable energy sources in Iran are explained. As a case study, the wind characteristics of the province of Sistan and Baluchestan are statistically analysed. The wind characteristics such as the monthly mean wind speed and the wind power density of each station are presented. The monthly variation of the wind direction is presented and also the dominant wind direction is shown in a wind rose diagram. The values of turbulence intensity at different heights are calculated. The results show that the stations of Khash and Nosratabad are more suitable for limited off-grid utility applications. Lootak with the average annual wind power density of 388 W m−2 at the height of 40 m and constant wind direction is recommended for large-scale grid-connected wind turbines.

Manufacturers around the globe are competing for the identification of innovative value propositions to survive in the competitive and complex market. This paper is intended to investigate implementation of Value Engineering (VE) technique into the product design concept for necessary changes in design of the humidifier system in order to lower unnecessary costs and to increase quality of the product. Humidifiers are used for ventilation and cooling the air at most houses in cities located in hot and dry areas. Value Engineering as a systematic attempt is used to increase efficiency of the product, and optimize the life cycle cost. This leads to a shift from traditional design towards new efficient designs. For this study, an 8 stage job plan is used for VE job plan. In this article, different components of humidifier were analyzed thoroughly and then numerous suggestions were made at the brainstorming sessions. Function Analysis System Technique (FAST) was also utilized at the first stage. Main findings lead to a conclusion that there were more suggestions at the brain storming session. Main findings lead to a conclusion that best suggestion for improved design of the humidifier is change the material of fan cover from galvanized iron to hard plastic or fiber plastic.

The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers in most countries rely on prediction models to influence food security policies. This research aims to predict and reduce the amount of energy consumption in wheat production. Data were collected from the farms of Estahban city in Fars province of Iran by the Jihad Agricultural Department’s experts for 20 years from 1994 to 2013. In this study, a novel prediction method based on consumed energy in the production period is proposed. The model is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR). In the experimental stage, the value of elevation metrics for the EVM and ELM was reported to be equal to 0.000000409 and 0.9531, respectively. Total input energy (consumed) is found to be 1,460,503.1 Mega Joules (MJ), and output energy (produced wheat) is 1,401,011.945 MJ for the Estahban. The result indicates the superiority of the ELM model to enhance the decisions of the agricultural policymakers.

In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which to replace or overhaul equipments or components. The second approach involves the use of sensor-based monitoring of equipment condition in order to predict occurrence of machine failure. Under condition-based (C-B) PM, intervals between PM works are no longer fixed, but are performed only “when needed”. It is obvious that Condition Based Maintenance (CBM) needs an on-line inspection and monitoring system that causes CBM to be expensive. Whenever this cost is infeasible, we can develop other methods to improve the performance of traditional (S-R)-based PM method. In this research, the concept of Bayesian inference was used. The time between machine failures was observed, and with combining Bayesian Inference with (S-R)-based PM, it is tried to determine the optimal checkpoints. Therefore, this approach will be effective when it is combined with traditional (S-R)-based PM, even if large number of data is gathered.