Ali Motevali

Given the economic and environmental importance ascribed to agriculture, there has been a rising focus on adopting greenhouse technology and renewable energy sources as sustainable and eco-friendly in this sector. Due to this emphasis, the current study utilized mathematical modeling of response surface methodology to analyze and optimization of the photovoltaic-thermal system’s performance of data obtained from a greenhouse. Through the use of Design Expert software and optimal factorial design, optimized treatments were achieved. The studied factors were time (ranging from 10 am to 3 pm), fluid types (pure water, SiO2, Al2O3, SiO2, and Al2O3- SiO2), nanoparticle concentrations (0.1%, 0.3%, and 0.5%) and the photovoltaic-thermal system location (inside and outside the greenhouse). The main objective of the optimization conditions was to maximize the energy parameters. The response optimization design was then examined for power, electrical efficiency, thermal efficiency, and total efficiency. The results demonstrated a strong correlation between the statistical model and the collected data (R2>0.90). Based on the experimental design factors, it was determined that the environmental condition of the system and the type of nanofluid had the most significant impact on the response. After implementing the design, the proposed optimal solution, with a desirability of 99%, involved utilizing a 0.5% concentration of Al2O3- SiO2 nanofluid in the system located outside the greenhouse at 3 pm.

The increase in population has increased the need for agricultural and food products, and thus agricultural production should be increased. This goal may cause increases in emissions and environmental impacts by increasing the consumption of agricultural inputs. The prediction of environmental impacts plays an important role in evaluating pollutant emissions in crop production. This study employed two artificial intelligence (AI) methods: the adaptive neuro-fuzzy inference system–fuzzy c-means (ANFIS–FCM) algorithm as a novel computational method, and an artificial neural network (ANN) as a conventional computational method to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. In the present study, the production of one ton of soybeans was considered the functional unit, and the boundary of the system was considered the gate of the field. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF × m² × yr, and 563.55 to 880.61 kg CO₂-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment, in which the R-S scenario had the best environmental performance. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The range of calculated R² for the ANFIS-FCM and ANN models were between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

One of the key challenges in using fossil fuels is the environmental impacts of these energy sources, and to reduce these destructive effects, the use of renewable energy sources (biofuels) is necessary. One of the important biofuels is biodiesel, which can be produced from cottonseed. To properly manage the chain dealing with biodiesel production from the cottonseed chain (from farm to manufactory), environmental hotspots must be pinpointed. In the present study, it was attempted to examine the environmental impacts of the biodiesel production cycle from cottonseed (agronomic stages, ginning, oiling, and biodiesel production). The data obtained in all three stages were analyzed by the Impact 2002+ method in the SimaPro software. The highest contribution to creating environmental indicators at the agricultural stage was related to the use of nitrogen fertilizers, direct emission from the farm and fossil fuels, the ginning and oiling stage involving the use of diesel fuel and sulfuric acid, and the production of biodiesel in the manufactory involving the use of methanol and electricity. The potential environmental impacts of a functional unit of 1 kg of biodiesel include: human health, 9.05–10⁻⁶ (DAYLY); ecosystem quality, 1.369 (PDF*m²*year); climate changes, (kg CO₂ eq.) 17.247; and resources (MJ primary), 89.116. Results showed that agriculture has more significant participation in the environmental impact than other sections (ginning and oiling and biodiesel production), especially due to the application of fertilizers and fuel. Surveying the environmental indicators of the results showed that at the agricultural stage, the human health indicator is 10.43, 1.21, and 5.32 times higher than the ecosystem quality, climate change, and resource indicators, respectively; at the ginning and oiling stages, it is 2.35, 31.68, and 2.09 times higher, respectively; and at the stage of biodiesel production in the manufactory, it is 16.41, 1.96, and 0.99 times higher, respectively, in terms of the destructive effects. The overall results showed that the hotspot points in the present study can be largely modified by reducing the consumption of nitrogen fertilizers, using new equipment and machinery, ginning and oiling, and using fewer methanol ratios than oil.

This study employed two artificial intelligence (AI) methods called the ANFIS–FCM algorithm as a novel computational method and an artificial neural network (ANN) as a conventional computational method in order to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The 175 data of life cycle assessment (LCA) method were collected from soybean farms. The two methods called the adaptive neuro-fuzzy inference based on fuzzy C-means clustering algorithm (FCM) and the artificial neural network (ANN) were adopted to predict environmental parameters. For this purpose, the life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF×m²×yr, and 563.55 to 880.61 kg CO₂-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment in which the R-S scenario had the best environmental performance. Notably, the emissions caused by the production and application of the diesel fuel followed by chemical fertilizers, particularly N and P fertilizers, were recognized as the most important environmental hotspots in soybean production. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The RMSE and MAPE values obtained from ANFIS–FCM were lower than the values obtained from the ANN model for all environmental indicators. For the ANFIS–FCM and ANN algorithms, R² ranged between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

In this work, the melting process of phase change material (PCM) in double tube heat exchangers was investigated and evaluated through the use of different combinations (1, 2, 3% Nano-Enhanced PCM and 1, 3, 5% Nano-HTF) of GQD, as well as SWCNT nanoparticles and PCM (RT82). In this study, the effect of three different methods, namely the dispersion of nanoparticles in PCM (nano-enhanced PCM), the dispersion of nanoparticles in HTF (nano-HTF), and the simultaneous dispersion of nanoparticles in PCM and HTF (nano-enhanced PCM, nano-HTF) concerning the nanoparticles participation in the thermal energy storage system in a double tube heat exchanger was evaluated. Other effective factors, such as the inlet fluid temperature, different Reynolds numbers, fin as well as new parameter of pipe, and fin thickness were also evaluated. The results showed that the highest effect of different parameters on the PCM melting process was related to the 1% nano-HTF and 3% nano-enhanced PCM nanoparticles of SWCNT, which decreased the PCM melting rate by about 39%. The evaluation of the effect of pipe and fan thickness also showed that the melting rate improved by 31% through reducing the thickness of the HTF fin and pipe. In general, the current study followed two purposes first, to examine three methods of the dispersion of nanoparticles in the thermal energy storage system; second, to reduce the thickness of the tube and fin. Findings of the study yielded positive results.

Freshwater supply is currently exacerbated in most parts of the world. Freshwater production technologies require spending considerable costs and energy. In this study, a solar collector with a Fresnel lens was developed and evaluated as the collector unit. This collector was designed with a cavity collector and multi-tube absorber arrangement for using preheated water required by water stills. Experiments were conducted from mid-May to mid-June, 2014. Thermal evaluations were conducted at three flow rates (100, 200, and 400 ml/min), resulting in the maximum thermal efficiencies of 75.7%, 81.1%, and 88.2% and minimum thermal efficiencies of 66.5%, 70.4%, and 73.7%, respectively. The maximum output temperatures for the studied flow rates (100, 200, and 400 ml/min) were 96, 77, and 63 °C, respectively. Maximum and minimum heats absorbed at three flow rates (100, 200, and 400 ml/min) were 636.67, 643.21, and 677.38 W and 451.44, 509.66, and 441.67 W, respectively.