نشریه مهندسی بیوسیستم ایران
سال پنجاه و یکم شماره 3 (پاییز 1399)

Computational Fluid Dynamics (CFD) was used to study airflow and temperature distribution in cold storage and finding temperature levels. Total sugar, reducing sugar and sucrose of potato tubers cv Sante stored at 5 °C and relative humidity of 80%, were extracted. The results showed that there was a significant difference between the concentration of total sugars, reducing sugar and sucrose due to temperature distribution heterogeneity.It led to a 2 °C difference between the warmest and coldest points in product mass. Changes in the content of sugars were matched with simulated temperature contours. The rate of increase in soluble sugars was predicted by reducing 1 °C air temperature. Total sugars, sucrose, and reducing sugar were 4.27, 3.15 and 0.67 mg/g dry matter, respectively. Computational fluid dynamics, by analyzing fluid flow and distributing temperature and detected critical areas, leads to improving packaging and product placement, and improve airflow in the stack.

In this research, to increase the performance of parabolic solar collector, PCM phase change materials were used inside the fluid storage tank. The effect of fluid flow rate at three levels of 1, 2.5 and 5 l/min and PCM mass at two levels of 1.5 and 3.2 kg on output temperature and thermal efficiency of the collector and tank efficiency using experimental methods and CFD and ANN were evaluated and compared. Drying efficiency changed from 21.11 to 25.20% and collector from 62.9 to 64.03. The collector efficiency error of CFD and ANN methods varied from 5.31 to 7.4% and 1.22 to 3.84%, respectively. According to the statistical data and the time spent, it was found that the ANN method can be used to predict the thermal behavior of the system more accurately and less time than the CFD method.

In this study, to predict sugarcane yield, an Artificial Neural Network based on meta-heuristic algorithms is used as an efficient method to estimate crop yield based on actual data. In order to predict sugarcane yield, effective parameters such as plant characteristics, electrical conductivity of soil and water, maximum temperature and average hours of sunshine during the growing season and on a time scale of seven years were used. Accordingly, four hybrid models were used to build neural networks which including of artificial neural network based on Back Propagation (BP) algorithm, combining neural network with Genetic Algorithm (GA), combining neural network with Particle Swarm Optimization (PSO) algorithm and finally a new approach as combining neural network with GA-PSO. The results show that the neural network performance can be improved using meta-heuristic algorithms and can be increased significantly the prediction power. The Mean Square Error (MSE) and correlation coefficient (R) in the hybrid method of neural network with GA-PSO were obtained 0.00057 and 0.91457 respectively on the test data, which show the superiority of this method to other patterns. In addition, cross validation test of the proposed model by K-Nearest Neighbors showed the accuracy of training and test data to predict sugarcane yield has been equal to 98.5% and 95.5%, respectively.

In order to investigate the mechanization development status, in this study, four wheat, barley, rice and forage maize crops in Ahvaz city were evaluated with two quantitative (degree and capacity of mechanization) and qualitative (income and labor desirability) indices. The data were collected through questionnaires and interviews, also statistics and recorded information. Four parameters were calculated and estimated in four products and finally the results were summarized and presented using fuzzy logic. The results showed that the average degree of mechanization of different operations (total mechanization degree) for wheat, barley, rice and forage maize products in the study area was 93, 92.5, 73.5 and 95%, respectively. The mechanization capacity in this order was 988/22, 944/72, 806/6 and 1136/936 hp.h/ha, the results were calculated as 62, 43, 57 and 54% for income desirability and 73, 69, 59 and 47% for labor desirability, respectively. The final de-fuzzy output obtained from the summation of four indicators using fuzzy logic showed that among the studied crops, wheat with 52.3% had the highest amount, followed by rice with 46.6%, barley with 42.2% and forage maize with 40.3% had the lowest amount.  In general, according to the results of the index defined for the study of mechanization development, the study area is almost in a moderate position in the four products under study.

The Magnetic Resonance Imaging is one of the non-destructive methods to study quality indices and fruit maturity, that uses different protocols to show the density and structure in which the hydrogen atoms is placed. In this research the MR images of intact, bruised and infested tissues, and seeds of none-infested and infested quince fruits by different MRI protocols were compared and the best protocol was introduced. So by using two T1 and T2 protocols, MR imaging was carried out from 18 loaded quince fruits. The quasi-static loading was performed at three levels of 300, 500 and 700 Newton. Then the fruits were stored for 30 and 50 days in a 4°C refrigerator. Imaging was carried out after each storage period. The ImageJ software was used to determine the contrast of T1 and T2 imaging protocols of the intact and bruised tissues and seeds of none-infested, infested and loaded fruit samples. The comparison of these images was concluded that the seeds and the intact area of none-infested Quince fruit in T1 images were sharper than T2 images. Also, the loaded area bruising of none-infested fruits in the T2 images was more recognizable than T1 images.

One of the best ways to use biomass is to convert them into a pellet that has more mass and energy per unit volume and makes it easier to use and transport. Various factors are involved in the process of pelletizing, which helps to optimize the process. In this study, the effect of temperature, compression force and mixing ratio were investigated on the mechanical properties of pellets made from rice bran and sugar cane molasses. The results showed that, except compression force and the interaction between the compression force and the temperature, other variables and their interactions have significant effect (p < 0.01) on the dependent variables. Mean comparison showed that increasing the amount of molasses in combination increased the compressive strength of the pellet in axial and radial direction. The highest compression strength was obtained at the ratio of 1 to 1.50 (rice bran to molasses) and 1300N compression force. The maximum bending strength of pellets was significant in all three ratios, and increasing sugar cane molasses increased the bending strength. Increasing the mold temperature and compression force increased the strength of the pellets in both axial and radial directions as well as their bending strength. Maximum density and drop resistance of pellet in the ratio of 1 to 1.65 (rice bran to molasses), compression force of 1300 N and temperature of 70 ° C were 1213 kg/m3 and 93.5%, respectively. Generally, in the pelletizing process the mixing ratio, compression force and temperature have an effect on the strength of the pellet.

Energy is one of the main components of the sustainable development strategy of any country. Efficient use of energy must be considered to support agricultural production; Because in addition to economic savings, it protects fossil resources. On the other hand, intensive agriculture, which is based on the intensive use of agricultural inputs, machinery, fossil and non-fossil energy, is itself the cause of many of the world's environmental problems today. In this study, the energy and environmental impacts of apple production in West Azarbaijan Province have been evaluated. To collect required data on the production process, a questionnaire was filled out and interviews were conducted with 141 gardeners in the area. The results showed that the energy ratio is equal to 1.65, and the net energy added was equal to 27.780 MJ/ha. Among the energy sources, diesel fuel accounted for the highest share of total energy inputs (61%). To evaluate the environmental impacts of apple production, the life cycle assessment approach was used and ten parts of the work were examined. The results of the life cycle assessment showed that chemical pesticides, diesel fuel and nitrogen fertilizer had a great influence on the parts of the study.

In this study, optimum drying conditions of compressed tablet tomato under vacuum-infrared conditions were determined to produce tablet with appropriate chemical and qualitative properties. The content of lycopene, total phenol, vitamin C concentration, antioxidant activity as chemical properties and color indices ΔL*, Δa* and Δb* were studied as qualitative properties. Drying process of samples was performed at five levels of air temperature of the compartment 40, 50, 60, 70 and 80 °C and five vacuum pressures 20, 30, 40, 50 and 60 kPa. Statistical analysis of data and optimization of drying process were performed using response surface methodology. The results showed that in vacuum-infrared drying, as the temperature of the dryer chamber increased, the concentration of vitamin C and total phenol content of tomato tablet decreased and lycopene content and antioxidant activity of tomato tablet increased. Also, increasing temperature increased the difference between the color indices L* and a* and reduced the difference between the color index b* compared to fresh tomatoes. The optimum point the drying process of tomato tablet was obtained at 56 °C and vacuum pressure of 30 kPa. The reduction rates of vitamin C concentration, lycopene content, and total phenol content of dried tomato tablets in optimal condition were 54.3, 2.6 and 29.62 percent, respectively. Drying at lower temperature increased the model's desirability index obtained by response surface methodology.

Common fog production methods in aeroponic systems have some the disadvantages and limitations. So, an initial study was carried out to assess the feasibility to use the fog production technology through ultrasound. The effect of four independent variables, namely, over piezoelectric fluid head at three levels of 20, 30 and 40 mm, three levels of reservoir dimensions, three levels of piezoelectric location at the bottom of the reservoir, and fertilizer concentration in solution at four levels of 0, 2.5, 5, and 7.5 g.L-1 on fog production rate, electrical conductivity changes, and total dissolved solids as dependent variables were investigated. The effects of three variables of concentration, piezoelectric position and reservoir dimensions on the rate of fog production were significant at 1% probability level. Also, the effect of fluid head was significant at probability level of 5%. The results indicated that the water-soluble nutrients could be transported better by ultrasound at a certain range of soluble concentrations.

Development of non-destructive detection methods to rapidly assess the safety of agricultural products in terms of microbial contamination due to the increasing demand for safe ones is very important. In this research, a non-destructive optical method based on NIR hyperspectral imaging with (PLS-DA) method for rapid detection of microbial contaminated lettuce class (contaminated with Escherichia coli) from control class (with no contamination) was developed. To this end, dimensionality reduction (Spatial preprocessing) and spectral preprocessing were performed using (PCA) and Standard Normal Variate (SNV) with Mean Centering (MC) methods. The results of PLS-DA analysis showed the good ability in classification of control class and contaminated ones with different microbial population with high accuracy of 90% and class error of 0.008. Besides, the spectral region of 1400 to 1500 nm and the wavelength of 1200 nm were selected as the most important wavelengths that provide the most information for target identification and group classification using Variable Importance in Projection (VIP) score of the loading plot for PLS-DA. In general, the result showed that the NIR hyperspectral imaging method with PLS-DA analysis could be a fast and accurate method for real-time and non-destructive detection of microbial contamination in lettuce samples and classification of different classes (safe and contaminated).

This study was conducted in West Azarbayjan province in the crop year 2019 With the aim of rainfed chickpea life cycle assessment and energy consumption optimization and emissions reduction in small farms (equals smaller than 2 hectares), medium farms (between 2 and 4 hectares) and large farms (larger than 4 hectares). Required data, were obtained through face-to-face interviews with farmers and filling out 250 Specialized questionnaires and were evaluated by data envelopment analysis method. Results indicated that total input energy for chickpea production in small, medium and large farms was 6366, 6048 and 6848 MJ.ha1-, respectively and the maximum energy consumption was related to large farms. The largest share of consumption energy was related to diesel fuel at all farm levels. The energy ratio in small, medium and large farms was obtained 2.47, 2.49 and 2.13, respectively. Chickpea life cycle assessment results revealed that environmental impacts on medium-sized farms were lower than small and large sizes in all effect sections. Seed, diesel fuel, and agricultural machinery inputs had the largest share in diffusion of environmental indexes. According to energy consumption optimization results, the average of energy efficiency in small, medium and large farms was 93%, 88% and 90%, respectively which demonstrates that farmers of small farms have more precise information about proper production methods or have used different inputs at better time and in a more optimal amount.

Developing a robust and reliable estimation method to detect suspended sediment concentrations from various environmental and geomorphological aspects including water quality, riverbed sustainability engineering, flood management and aquatic habitats is an unavoidable necessity. In this research, a new approach has been developed using a combined optical-acoustic sensors and hybrid intelligence-based system of ANFIS modeling to predict the concentration of suspended river sediments. Also, two measurement systems were placed in a water tank in vitro, and every 50 seconds, 10 g of soil (passed through sieve 140) was added to the water as suspended sediment until the total sediment in the water was 100 grams. The operation was performed in 20 iterations and the output values ​​of the two measurement methods were given as inputs. Interface structure with only optical sensor inputs with higher efficiency coefficient of determination (R2) 0.94 and mean square error root mean square error (RMSE) 7.15 (gr) compared with the ultrasonic sensor inputs with coefficient of determination (R2) of 0.91 and root of the mean squared error was 8.72 (gr). Also, the highest efficiency of hybrid structure with two inputs of two measurement methods had coefficient of determination (R2) 0.97 and root mean square error was 5.26 (gr). According to the results, the best distance between receiver and transmitter in the ultrasonic sensor was between 8 and 15 cm and the use of hybrid system in sediment estimation was more efficient with an error of 3 and 1.5 percent less than the error of separate ultrasonic and optical systems.