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Author: search.filters.author.Alireza Ghodsvali

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    Application of Artificial Neural Networks for Predicting Cooking Dynamics in Industrial Sesame Seed Oil Extraction
    (United Scientific Group, 2024-10-01)
    Hamid Bakhshabadi
    ;
    Alireza Ghodsvali
    ;
    Abolfazl Bojmehrani
    ;
    Mohammad Ganje
    ;
    Toktam Mohammadi-Moghaddam
    Sesame seeds are a significant source of vegetable oil and were among the earliest grains used for oil extraction. In this study, aimed at designing an industrial-scale process for extracting oil from sesame seeds, we investigated three cooking temperatures (75°C, 90°C, and 105°C) and three different moisture contents of the seeds leaving the cooking pot (4.5%, 5.5%, and 6.5%). The study focused on several responses: the oil content of the pressed cake, the quantity of extracted oil, the protein and moisture contents of the resulting meal, and the percentage of insoluble fine particles in the extracted oil. To predict these responses, an artificial neural network (ANN) model was employed. Among the various backpropagation feedforward networks with different topologies studied, the configuration with 2 input nodes, 5 hidden nodes in one layer, and 5 output nodes was selected based on its high correlation coefficient (R² = 0.997) and low mean squared error (MSE = 0.0002). The sigmoid hyperbolic tangent activation function was used, and the Levenberg-Marquardt learning algorithm with 1000 learning cycles was identified as the optimal neural model. The selected optimized models demonstrated high R² ≥ 0.97 during the evaluation of their results.
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    Modeling and Optimization of the Osmotic Dehydration of Cantaloupe
    (United Scientific Group, 2024-10-09)
    Hamid Bakhshabadi
    ;
    Mohammad Ganje
    ;
    Masoumeh Moghimi
    ;
    Alireza Ghodsvali
    ;
    Toktam Mohammadi-Moghaddam
    In current research, the optimization of osmotic dehydration of cantaloupe pieces aimed to maximize water loss (WL) and minimize moisture reabsorption using artificial neural network (ANN). The effects of three parameters were studied: osmotic solution temperature (40-60°C), immersion time (40-240 min), and solution concentration (40-60°Brix), employing central composite design (CCD). Various parameters including WL, solid gain (SG), reduction in WL to SG ratio, and reduction in sample weight were analyzed. The results indicated that the optimal conditions for osmotic dehydration were achieved with a solution temperature of 60°C, immersion time of 85.71 min, and solution concentration of 40% sucrose (sugar). Under these conditions, the following parameters were observed: WL of 3.79%, SG of 43.74%, WL to SG ratio of 14.48, and sample weight reduction of 47.71%. Furthermore, results from the ANN revealed that a network structure with one hidden layer comprising 5 nodes (3-5-4 network with 3 inputs, 5 nodes in the hidden layer, and 4 outputs) provided the most accurate predictions. This network achieved correlation coefficients (R2) of 0.999 and root mean squared error (RMSE) of 0.000039, demonstrating high reliability and precision in predicting the selected responses.