mohammad vahedi Torshizi; Arash Rokhbin; Armin Ziaratban
Abstract
This research, which has practical implications for the food engineering and processing industry, estimated the energy and exergy of apple drying using a Group Method of Data Handling (GMDH) neural network and a hybrid artificial neural network-genetic algorithm (ANN-GA). Osmotic and natural samples ...
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This research, which has practical implications for the food engineering and processing industry, estimated the energy and exergy of apple drying using a Group Method of Data Handling (GMDH) neural network and a hybrid artificial neural network-genetic algorithm (ANN-GA). Osmotic and natural samples were tested in the form of apple cubes with a height of 5 mm and sides of 10 mm ×10 mm, 8 mm × 8 mm, and 6 mm × 6 mm dried by fluidized bed method at three air velocities of 4, 6, and 8 m/s and temperatures of 40, 45, and 50℃. The results showed that energy consumption, energy use ratio, exergy efficiency, and exergy loss were elevated by increasing the temperature and air velocity and reducing the sample sizes in natural and osmotic samples, which were directly relevant to the industry. It was also observed that the GMDH neural network for energy consumption, energy use ratio, exergy loss, and exergy efficiency have linear correlation coefficients (R) of 0.95097, 0.92202, 0.91464, and 0.91258, respectively, suggesting that it outperforms the ANN-GA. The weakest performance of the GMDH network was associated with the exergy efficiency. The ANN-GA had the best energy consumption and lowest exergy loss performance.
Seyed Mohamamd Safieddin Ardebili; Ibham Veza; Aslan Deniz Karaoglan; Erol Ileri; Mostafa Kiani Deh Kiani; Masoud Rabeti
Abstract
For the HCCI experiments, four different compression ratios were used (CR9, CR10, CR11, and CR12). The intake air temperatures varied between 313 and 373 K, while the engine speed changed from 800 to 1800 rpm. Three fuel blends were used, i.e., RON20, RON40, and RON60. The RON60 indicates 60% iso-octane ...
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For the HCCI experiments, four different compression ratios were used (CR9, CR10, CR11, and CR12). The intake air temperatures varied between 313 and 373 K, while the engine speed changed from 800 to 1800 rpm. Three fuel blends were used, i.e., RON20, RON40, and RON60. The RON60 indicates 60% iso-octane and 40% n-heptane. A modified social group optimization (MSGO) algorithm was used for HCCI optimization purposes. Regression modeling was first employed to calculate the mathematical relations between the factors (compression ratio, research octane number (RON), intake air temperature, engine speed, and lambda) and the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). FThe regression models fit the given observations well with a low prediction error. The calculated R^2 obtained from this study show that the compression ratio (X_1), RON (X_2), intake air temperature (X_3), engine speed (X_4), and lambda (X_5) are sufficient to model the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). ANOVA results show p-value < α (under the 95% confidence type-I error= α=5%), indicating that the model is significant (H1 is true). Then MSGO is run via these mathematical models to determine the parameters with optimal optimization values. In the verification phase, 13 additional experimental runs that were not used in the mathematical modeling phase were used. It was found that the regression models fit the observed values well with a low PE (%). MSGO algorithm suggested the best value for studied parameters as X1=11.47, X2=60, X3=313, X4=800, and X5=1.45. The verification shows satisfying results with a high accuracy. The optimized factor levels indicates that the effective torque, IMEP, and indicated thermal efficiency were maximized while the other responses were minimized. Therefore, the findings signify the potential of the MSGO algorithm for HCCI optimization.
Aliasghar Tatari; Mehrshad Nazarpour; Mohammadreza Pourpilekesh
Abstract
Pyrolysis is a promising process for converting lignocellulosic materials to high-value-added products (bio-oil, biochar, and syngas). This study aimed to produce and characterize bio-oil obtained from pine cone via pyrolysis using a fixed bed reactor system (FBRS). This study investigated the effect ...
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Pyrolysis is a promising process for converting lignocellulosic materials to high-value-added products (bio-oil, biochar, and syngas). This study aimed to produce and characterize bio-oil obtained from pine cone via pyrolysis using a fixed bed reactor system (FBRS). This study investigated the effect of temperature (500, 600, and 850 °C) on the pyrolysis product yield. The findings showed that with increasing the temperature, the bio-oil and gas yield increase, and the bio-char decreases. The highest calorific value of bio-oil (23.74 MJ/kg) and bio-char (32.89 MJ/kg) was obtained at 600 and 850 °C, respectively. The optimal pyrolysis temperature is 850 °C, which maximizes syngas production at 45.5%, making it the most favorable condition for syngas-focused applications. At this temperature, the yields of bio-oil and biochar are 36.2% and 18.3%, respectively. The qualitative analysis conducted through gas chromatography-mass spectrometry (GC/MS) revealed that the bio-oil produced from the pyrolysis of pine cones is a complex mixture of various organic compounds, including but not limited to aldehydes, alcohols, organic acids, furans, phenolic compounds, and several aromatic substances. The presence of these bioactive compounds underscores the potential utility of this bio-oil as a viable biofuel, offering promising opportunities for renewable energy solutions and reduced dependence on fossil fuels.
Mohammad Vahedi Torshizi; Arash Rokhbin; mohammad javad Mahmoodi
Abstract
This study examines the rosemary thermal properties, including thermal conductivity, specific heat capacity, and thermal diffusion coefficient in the microwave, blanching, and oven pretreatments. To test the microwave pretreatment for three-time levels of 60, 90, and 120 seconds, the samples were placed ...
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This study examines the rosemary thermal properties, including thermal conductivity, specific heat capacity, and thermal diffusion coefficient in the microwave, blanching, and oven pretreatments. To test the microwave pretreatment for three-time levels of 60, 90, and 120 seconds, the samples were placed in the microwave, and their weight changes were recorded. The samples were pre-treated for blanching pretreatment at three-time levels of 180, 360, and 540 seconds. Finally, in the oven treatment, the rosemary leaf samples were placed at three temperature levels of 30, 45, and 60 °C for 15 minutes. Then, in three voltages of 4, 7, and 10 volts, the thermal properties were obtained separately for each of the pretreatments, and for all cases, a control treatment was considered. The results were analyzed using a completely randomized design and a factorial experiment. According to the results obtained for microwave and blanching pretreatment, increasing the pretreatment time and pretreatment of the oven with decreasing pretreatment temperature resulted in a decreasing trend for thermal conductivity coefficient, exceptional heat capacity, and thermal diffusion coefficient. The increase in voltage also occurred in the thermal properties of the rosemary leaf of all three pretreatments. The pretreatment levels were significantly higher than the process voltage for the thermal conductivity and diffusion coefficient, which indicates that pretreatment has a more significant impact on the thermal properties of the process. The maximum value of the electrical conductivity coefficient is 0.4256, 0.5851, and 0.510 Wm-1 ° C-1, and for the specific heat capacity of 2.58, 2.68, and 2.65 kJ kg-1 ° C-1, and also for the thermal diffusion coefficient of 2.48×6-10, 2.81× 6-10and 2.50×6.-10 m2s-1 for microwave, blanching, and oven pretreatments, respectively. Among the three pretreatments, microwave pretreatment significantly reduced the thermal conductivity, specific heat capacity, and thermal diffusion coefficient of rosemary leaves.
Abbas Rezaeiasl; Hosain Nasiritalashi; Mohammadhashem Rahmati
Abstract
This study aimed to determine the effects of the diameter and curvature shape of a planter’s seed tube on the uniformity of seed distribution and deviation from the cultivation line. The effects of tube diameter (at four levels, 5, 7, 9, and 11 mm) and tube curvature radius (at three levels, 5, ...
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This study aimed to determine the effects of the diameter and curvature shape of a planter’s seed tube on the uniformity of seed distribution and deviation from the cultivation line. The effects of tube diameter (at four levels, 5, 7, 9, and 11 mm) and tube curvature radius (at three levels, 5, 15, and 20 cm) were investigated on the quality of the seed distribution. The location of the seeds distributed by the planter was obtained by image processing techniques. The results revealed that the diameter and curvature radius of the seed tube did not have significant effects on the uniform distribution of seeds in the travel direction. However, the effects of both factors and their interaction were significant (P < 0.01) on the deviation of seed locations from the cultivation row. Response surface methodology (RSM) was then utilized to optimize the average distance of the seeds in travel and its perpendicular directions. According to the results, the distance of the planted seeds can be optimized with high desirability rates (≈ 1) based on the design parameters of pressurized pneumatic planters. Finally, the results showed that the partial least squares (PLS) method could efficiently predict the average distance of the seeds having the diameter and curvature radius of the seed tube with an R2 value equal to 0.99.
Alireza Soleimanipour; Abbas Rezaei Asl; Roghaieh Shamloo
Abstract
The excessive use of agricultural pesticides and inputs has caused severe environmental damages to agricultural ecosystems. By applying digital agriculture and variable rate application systems, different sections of a farm can be managed with varying levels of pesticides and inputs, which is beneficial ...
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The excessive use of agricultural pesticides and inputs has caused severe environmental damages to agricultural ecosystems. By applying digital agriculture and variable rate application systems, different sections of a farm can be managed with varying levels of pesticides and inputs, which is beneficial both in terms of production costs and environmental issues. In this study, a weed and saffron plant detection model was designed and evaluated to develop a selective weed control system in saffron fields. The proposed weed detection model is based on the YOLOv5 object detection model. Specifically, several CBS and C3 modules in the YOLOv5s model were replaced with Ghost Bottleneck and C3Ghost modules, respectively. This was done to reduce the number of model parameters and make the network lighter, which increases the speed of image processing during model training and inference. Furthermore, to improve the detection accuracy of the proposed model, a coordinate attention (CoordAtt) layer was used. The results showed that the number of parameters in the proposed model was reduced by 47% compared to the corresponding model in terms of network width and depth coefficients in YOLOv5 versions. Meanwhile, among the six trained models, the modified Yolov5s model demonstrated the best performance, achieving 81% accuracy and 67% recall. The detection accuracy of the proposed model was 3.93% higher than that of the best-performing YOLOv5 algorithm. Due to the lightweight nature of the proposed algorithm, it can be used for real-time weed detection in agricultural fields to develop selective control systems.
himan khodkam
Abstract
A lot of waste enters nature from various sources every day, most of which are organic materials. The release or improper disposal of organic waste leads to the destruction of human and animal ecosystems, and the best approach to dispose of this type of waste is anaerobic digestion technology. This technology ...
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A lot of waste enters nature from various sources every day, most of which are organic materials. The release or improper disposal of organic waste leads to the destruction of human and animal ecosystems, and the best approach to dispose of this type of waste is anaerobic digestion technology. This technology causes proper disposal and the production of economically valuable materials, which can generally be said to be a win-win approach. In this article, the goal is to produce the highest amount of biogas production to reduce the cost of production rate. Biogas is produced in four stages, and many key factors, including temperature, pH, C/N ratio, residence time, mixing, and moisture, affect biogas production. Each of these factors has an optimal point that can be observed to achieve the highest production rate. In this way, the production cost will be minimized and the majority of the needs of society will be met. Also, to accelerate and increase efficiency, types of pretreatment can be used, which often play a positive role due to the conversion of lignin and silica. Sodium hydroxide is one of the lignocellulosic pretreatments that is widely used. Biological pretreatment has low energy consumption and is environmentally friendly. Zero iron nanoparticles have shown better potential capacity than other nanoparticles. The summary of the studies is that if the coarse material is reduced to small pieces, the feed material concentration is about 8, the C/N ratio is about 25, the pH is neutral and the digester temperature is set to mesophile, the maximum amount of biogas production is achieved. It was also concluded that a definitive opinion cannot be given about the pretreatment because it depends on the materials and digestion conditions.
Amin Fathi; Kamran Kheiralipour
Abstract
Energy consumption is one of the main issues in production systems because of the role in production costs and environmental impacts. The present study was conducted to investigate the environmental aspect of mung bean farms in the 2022-2023 crop year. The required data were collected through questionnaires ...
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Energy consumption is one of the main issues in production systems because of the role in production costs and environmental impacts. The present study was conducted to investigate the environmental aspect of mung bean farms in the 2022-2023 crop year. The required data were collected through questionnaires and face-to-face interviews with 78 mung bean farmers in Darehshahr, Ilam, Iran. Input-output materials were calculated and based on them material and energy indicators and carbon footprint were calculated. The results showed that the carbon footprint of mung bean production was 2.74 ton CO2 eq.ha-1 (2.42 kg CO2 eq.kg-1). The total amount of input and output energy in mung bean production was 12586.49 and 16604.40 MJ.ha-1, respectively. The most important energy-intensive inputs in mung bean production were nitrogen fertilizer, fuel, and electricity with the shares of 32.57, 25.88, and 24.43%, respectively. The values of energy efficiency, productivity, intensity, net gain, and net gain efficiency were 1.32, 0.09 kg/MJ, 11.11 MJ/kg, 4017.91 MJ/ha, and 0.32 MJ.MJ-1, respectively. Given the low share of renewable energy (3.49%), increasing renewable energy consumption is necessary in mung bean production.
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