Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D_eff) of moisture in CD drying (1.95 × 10⁻⁷–4.09 × 10⁻⁷ m²/s) was found to be lower than that observed in MD (2.94 × 10⁻⁷–8.21 × 10⁻⁷ m²/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R² values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.

On a global scale, millions of tons of diverse agricultural residues are generated annually. Most of these wastes are burned or dumped in landfills, which causes environmental pollution. Addressing environmental issues arising from agricultural waste materials, in addition to mitigating heating and cooling expenses in the construction sector, is an interesting challenge for researchers. The utilization of agricultural wastes in different parts of construction is producing positive findings day by day, and investigating research in this field is a curiosity for researchers. This short study reviewed the most recent achievements in using agricultural wastes as a substitute or additive material for construction. Using these wastes as aggregate, ash (as a supplementary for cement), or fibers for foam concrete, insulation materials, etc. has been reviewed. This review has focused on very recent published papers. Several studies have demonstrated the effective influences of agro-waste materials in construction, like retaining the compressive strength (155 MPa) of concrete at standard levels and reducing heat losses in buildings (69% energy savings for brick insulated using wheat straw), as well as sound insulation. The use of agro-waste materials for insulation positively improved thermal conductivity, costs, and energy savings. However, some wastes did not provide a high added value, which shows that more investigations still need be performed to fill this gap in the research. Considering the global scale of agricultural waste generation and the potential benefits to both the environment and construction industry, continued research in this area is essential.

In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis/NIRS and prediction models such as PLSR and ANN. First, Vis/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350–1100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.

This study deals with the optimization of energetic and exergetic parameters of a hybrid-solar dryer to dry basil leaves under determined experimental conditions at three air temperatures (40 °C, 55 °C, and 70 °C) and three bed thickness levels (2, 4, and 6 cm). The optimization of the thermodynamic parameters was performed using the response surface method (RSM) based on the central composite design (CCD) and the desirability function (DF) to maximize the drying rate, exergy efficiency, improvement potential rate and the sustainability index, and to minimize the energy utilization, energy utilization ratio and exergy loss rate. These parameters were calculated on the basis of the first and second laws of thermodynamics as the response variables. Based on the results obtained, it was determined that the optimal conditions for basil drying were at a drying air temperature of 63.8 °C and a bed thickness of 2 cm. At this point, the parameters of the drying rate, energy utilization, energy utilization ratio, exergy efficiency, exergy loss rate, improvement potential rate and sustainability index were obtained with the maximum utility function (D = 0.548) as 0.27, 0.019 (kJ/s), 0.23, 65.75%, 0.016 (kJ/s), 1.10 (kJ/s) and 0.015, respectively.

In recent years, global warming, climate change, and carbon emissions have emerged due to the uncontrolled use of fossil fuels and the lack of widespread use of renewable energy sources on a global scale. This research investigated specific energy consumption (SEC) and greenhouse gases (GHG) emissions (carbon dioxide [CO₂] and nitrogen oxides [NO_X]) during the drying of pear samples by five different types of dryers, namely, convective (CV), infrared (IR), microwave (MW), combined IR/CV, and MW/CV. Moreover, the quality properties of dried pears, such as shrinkage, rehydration (RR), and color change were determined. The results showed that the highest shrinkage (72.53%) and color change (ΔE = 33.41) values were obtained in CV drying at 50 °C and thickness of 2 mm and IR dryer 1000 W and thickness of 6 mm, respectively. The greatest rehydration rate (4.25) was also determined in MW/CV drying at 450 W and an air temperature of 60 °C. The lowest SEC was observed for the MW/CV dryer with a power of 630 W and air temperature 70 °C (20.25 MJ/kg), while the highest SEC (267.61 MJ/kg) was obtained in the CV drying (50 °C, sample thickness of 6 mm). The highest CO₂ and NO_X emissions (GT-GO power plant) were 280.45 and 1.55 kg/kg water in the CV dryer at 50 °C and a thickness of 6 mm. In conclusion, the increases in IR and MW power and temperatures led to reduced CO₂ and NO_X emissions, while the increases in sample thickness led to increase CO₂ and NO_X emissions.

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.