فهرست مطالب m. khojastehpour

Drying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized.

This study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90℃, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4℃ and the initial humidity of the fresh aloe vera sample is 110 gwater/gdry matter. Heat is supplied only by hot water from the bottom surface of the product.

The drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 gwater/gdry matter during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90℃, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90℃, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90℃, the drying rates were 0.915, 1.099, 1.57, and 2.198 gwater/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 gwater/min for 10 mm thick layer of aloe vera gel, respectively.

Based on the simulation results, the optimal model is with a water temperature of 90℃ and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data.

With the emergence of new automation and mechanized technologies in the production and processing of agricultural products in Iran, which aim to accelerate the food supply process, adopting appropriate management models in the field of maintenance becomes inevitable. This is crucial to maintain and enhance the operational reliability of agricultural machinery, tools, and equipment. Furthermore, proper management of various physical assets in the agricultural industry, including operation and maintenance, is one of the most important requirements. This is due to their crucial role in ensuring readiness and high availability during the seasons of planting, cultivating, and harvesting agricultural products. These needs differ from that of other continuous production processes. 

To achieve an efficient model in the field of maintenance, the following steps have been investigated:a) Reviewing and identifying the most important criteria and sub-criteria driving the maintenance management. This is based on the previous literature and the experts’ opinion.b) Evaluating and prioritizing the main criteria and the interactions between their sub-criteria using the Best-Worst Method (BWM).c) Providing improved solutions for maintenance management of Iranian agro-industries.We decided to employ BWM because, compared to similar methods, it (i) provides more reliable pairwise comparisons, (ii) reduces the possible anchoring bias that may occur during the weighting process by respondents, (iii) is the most data-efficient method, and (iv) provides multiple optimal solutions which increase flexibility when accessing the best weight point. The process of weighting by BWM is summarized in five steps:1) Determine a set of evaluation criteria identified by the experts or decision-makers.2) Identify the most important (Best) and the least important (Worst) criteria according to the experts or decision-makers, each of which may have their own Best and Worst.3) Determine the preference of the Best criterion over all the other criteria using a number from 1 to 9 (where 1 represents equal importance and 9 represents extremely more important).4) Determine the preference of all the decision criteria over the Worst criterion.5) Compute optimal weights. 

According to the preliminary surveys, the most important criteria in the excellence maintenance model were identified as “organizational management”, “human-related factors”, and “organizational aspects”, respectively. The results of the BWM revealed that sub-criteria such as "top management support," "fund allocation and inventory resource management," and "appropriate maintenance strategies" had the greatest impact on maintenance management in agro-industries, with global weights of 0.108, 0.075, and 0.067, respectively. Additionally, these findings were compared to previous research conducted in the field of agricultural and production system maintenance models. 

The findings of this study could assist managers in revising and developing maintenance management models in the agro-industries. Future studies could consider calculating the interactions among the criteria that were omitted in this study to simplify the evaluation process which might improve the accuracy of weighing criteria. This can be achieved through the combination of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and structural equation modeling.

The dual fuel diesel engine (DDF) is a one of the various IC engine that can used alternative fuel for power generation. Emission and fuel consumption reduction are some of the properties that use a combination of fuel mixture in dual fuel diesel engine (gas-diesel). We focused in this research to study the effect of start of injection (SOI) and blend of fuel variation in OM355 EU2 dual fuel diesel engine at two various speeds with the help of the computational fluids dynamic. The modeling of the artificial neural network (ANN) has been used to study the interaction effects of SOI and blend of fuel on the operational parameters. The non-sorted genetic algorithm (NSGA II) has been used for determining the optimized levels of the variables. The results of this study show that the RBFNN has acceptable predictions about the outputs’ variables (R^2=0.99 ,RMSE=0.01), and the optimized range of the engine function in the specific speeds has been attained with the help of the responding surface of the neural network. Besides, the optimizing capabilities of the NSGA II have provided the optimized levels of the input and output variables in the various speeds.

In this study, the energy consumption of button mushroom production in Khorasan Razavi province (Iran) was calculated. Data were collected from 13 production units with face to face questionnaire method. The production units were divided into three levels (categories) based on their area as: smaller than 7,000 m2 , 7,000-20,000 m2 and larger than 20,000 m2 . The results showed that the energy input per areafor the production of button mushroom was 2,480.86 MJ.m-2 . In between the the different energy sources, the highest share of energy consumption belonged to fuel (79.76%) followed by wheat straw (12.40%) and machinery (5.59%). The Cobb-Douglass model results revealed that the wheat corn and poultry manure had the highest regression coefficient (1.30 and 0.77) among the other inputs (for button mushroom and by-product compost production). Furthermore, the effect of poultry manure input on button mushroom yield was significant at the level of 5%. The production units with cultivated area of 7,000-20,000 m2 consumed the least amount of energy and had the highest energy ratio.