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Qualitative grading of milled rice grains was carried out in this study using a machine vision system combined with some metaheuristic classification approaches. Images of four different classes of milled rice including Low-processed sound grains (LPS), Low-processed broken grains (LPB), High-processed sound grains (HPS), and High-processed broken grains (HPB), representing quality grades of the product, were acquired using a computer vision system. Four different metaheuristic classification techniques including artificial neural networks, support vector machines, decision trees and Bayesian Networks were utilized to classify milled rice samples. Results of validation process indicated that artificial neural network with 12-5*4 topology had the highest classification accuracy (98.72 %). Next, support vector machine with Universal Pearson VII kernel function (98.48 %), decision tree with REP algorithm (97.50 %), and Bayesian Network with Hill Climber search algorithm (96.89 %) had the higher accuracy, respectively. Results presented in this paper can be utilized for developing an efficient system for fully automated classification and sorting of milled rice grains.

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This paper evaluates and optimizes the continuous production of biodiesel from waste cooking oil. In this research work, methanol and potassium hydroxide were used as catalyst engaging response surface methodology. For this purpose, the central composite experimental design (CCED), the effects of various factors such as irradiation distance, probe diameter, ultrasonic amplitude, vibration pulse and material flow into the reactor on reaction yield were studied to optimize the process. The results showed that all of the considered parameters affect the reaction efficiency significantly. The optimum combination of the findings include: irradiation distance which was 75 mm, probe diameter of 28 mm, ultrasonic amplitude of 56%, vibration pulse of 62% and flow rate of 50 ml/min that caused the reaction yield of 91.6% and energy consumption of 102.8 W. To verify this optimized combination, three tests were carried out. The results showed an average efficiency of 91.12% and 102.4 W power consumption which is well matched with the model's predictions.

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