Kazem Jafarinaeimi

The increasing global demand for high-quality greenhouse products, coupled with the heavy reliance on fossil fuels for climate control, has created an urgent need for sustainable, renewable-based solutions in agriculture. Conventional systems not only consume large amounts of energy but also contribute significantly to environmental pollution and greenhouse gas emissions. This study experimentally investigates a novel hybrid renewable energy system that integrates a windcatcher, photovoltaic-thermal chimney, and earth-to-air heat exchanger to provide passive heating and cooling for greenhouses. Experiments were conducted at Shahid Bahonar University of Kerman, Iran, using a lean-to greenhouse structure during peak summer (July-September 2024) and winter (January-March 2025) conditions. Vector linear regression was employed to develop robust predictive equations with broader applicability across different climates. Key performance parameters, including greenhouse temperature, ventilation rate, generated electricity, and the ratio of supplied electricity to consumed electricity (SECE), were measured and analyzed. Results proved a maximum temperature reduction of 21 °C in summer and successful maintenance of internal temperature between 19.5 °C and 22 °C in winter when outdoor temperature dropped to as low as 4.5 °C. The system achieved a ratio of supplied to consumed electricity exceeding 1, showing full self-sufficiency in meeting electricity demand, and delivered a higher coefficient of performance than most previously reported single- or dual-renewable systems. Energy and exergy analyses confirmed an energy-balance closure below 5% and second-law efficiencies of 0.32 in summer and 0.45 in winter. This study presents the first experimental demonstration of the configuration comprising a triple-hybrid windcatcher, a photovoltaic-thermal chimney, and an earth-to-air heat exchanger for greenhouse applications, addressing a critical research gap in integrated passive renewable systems and offering a practical pathway toward sustainable greenhouse thermal management in regions with high solar potential and extreme temperature variations.

Providing healthy food for the growing population is one of the most important issues in every country. Pests are one of the main damaging sources of agricultural products. Applying chemical pesticides is a common method employed by growers to address this issue. However, chemical residue not only remains on crops but can also penetrate fruits, vegetables, and even grains. This study aimed to investigate the effect of magnitude and elapsed time of high-voltage electric field (HVEF) application on the reduction of Malathion pesticide residue tissues. Additionally, the possibility of employing thermal imagery to monitor the residual traces of pesticide was evaluated. The effect of the HVEF at four voltage levels, four elapsed periods, and four biological layers of tomato was investigated on the amount of the pesticide residue. In addition to using the thermal imaginary method, the Fourier Transform Infrared spectroscopy (FTIR) method was also used to investigate the reduction of pesticide residues. The results showed that the HVEF significantly reduced the pesticide residue in the 4 kV treatment after 24 h from the time of applying the HVEF. The results of the analysis of FTIR charts and the images obtained from the thermal imaging method showed that the pesticide residue in the product was reduced, and the thermal imaging method can be employed as a low-cost, fast, and reliable method for monitoring the pesticide residue be used. The highest temperature difference between high-voltage treated samples and reference samples in a controlled chamber was 2.1°C at the voltage of 4 kV. By using an HVEF, it is possible to reduce the pesticide residue in tomatoes. The general results show that at a voltage of 4 kV, and 24 h after applying HVEF to the contaminated tomato samples, the maximum amount of pesticide residue has decreased.

Temperature is one of the most important plant growth parameters that should be controlled in the greenhouses. The present study was aimed to assess the thermal behavior of a greenhouse with and without the fan and pad (FP) evaporative cooling system. A method was developed to approximate the greenhouse temperature based on the mass and energy balance equations. For this purpose, both of the fan and pad evaporative cooling system, and the greenhouse were studied. The results of the theoretical analysis were compared with those achieved by the experiments. Maximum deviations of 5.32, 5.56 and 4.53oC were observed between the theoretical and experimental temperatures of the inside air, the floor and the cover of the greenhouse without the cooling system, respectively. Whereas, the mean absolute error values associated with the predicted temperatures of the greenhouse with the FP system were ranged between 1.50 and 25.67%. Based on the obtained values for the correlation coefficient, root mean square error and mean absolute magnitude error, it was concluded that the models satisfactorily predicted the temperature of the greenhouse components. An air circulation system inside the greenhouse can be proposed to maintain the lumped condition even at the high temperatures, and lead to smaller errors. The results indicated that the inside air, the floor and the cover temperature of the greenhouse reduced by respectively 20.6, 13.0 and 20.6 oC when using the FP system with the air velocity of 4.4 ms-1 and the pad thickness of 6 cm.

Edible lamb fat is an illustrious frying fat due to its good flavor and stability to oxidation. Fat deodorization is a vacuum–steam distillation process that is accomplished for removing the unwanted components such as free fatty acids and volatile compounds. The present work has studied the kinetics of lamb fat deodorization under different temperatures in a batch deodorization system. Variations of acid value (AV), peroxide value, p-Anisidine value, TOTOX value and total color difference were measured during the deodorization process. The Logarithmic, Wang and Singh, Hénon et al., and linear models were fitted with obtained data using nonlinear regression method. Results indicated that the Logarithmic and Henon et al. models gave the best fitness respectively with AV and p-Anisidine value, based on the statistical criteria of correlation coefficient ( R ² ), reduced chi-square ( χ ² ) and root mean square error (RMSE). Furthermore, the linear model was selected as the best model to describe the variations of TOTOX value and peroxide value during the deodorization process.