بررسی اثر سرعت دورانی، دما، نوع پیچ فشاری و قطر دای بر میزان روغن استخراج‌شده از دانه روغنی کنجد

در سال‌های اخیر همگام با رشد جمعیت و بهبود سطح زندگی، مصرف روغن‌های گیاهی رو به افزایش نهاده و موجب افزایش سطح زیر کشت دانه‌های روغنی شده است. اخیراً کنجد به‌عنوان یک گیاه مناسب روغنی برای کشت در شرایط آب و هوایی ایران مورد توجه قرار گرفته است. در این پژوهش یک دستگاه عصاره‌گیری از دانه‌ی روغنی کنجد، به روش پیچ پرسی طراحی و ساخته شد. آزمایش‌های مختلفی برای تعیین میزان روغن استخراج شده بر اساس پارامتر‌های قابل تغییر همچون شکل هندسی پیچ‌های پرسی، سرعت دورانی پیچ پرسی، دمای استخراج و قطر دای انجام شد. آزمایش‌ها در سه سطح دمایی (°c 30، 60 و 90)، سه سطح سرعت دورانی (20، 50 و 80 rpm)، سه مدل پیچ پرسی (سر راست با گام ثابت، سر راست با گام متغیر و مخروطی) و سه قطر دای (6، 8 و 10mm)، به‌صورت فاکتوریل در قالب طرح کاملاً تصادفی انجام گردید. نتایج تحقیق نشان داد که اثر نوع پیچ پرسی، سرعت دورانی، دمای استخراجی و اندازه دای بر میزان استخراج روغن در سطح یک درصد معنی‌دار است به‌گونه‌ای که پیچ پرسی مخروطی با سطح سرعت rpm 50 و سطح دمایی°c 60 و قطر دای mm6 بیشترین میزان استخراج روغن را داشت. برای پیش‌بینی میزان روغن کنجد استخراج شده از شبکه عصبی مصنوعی از نوع پرسپترون چندلایه و مقایسه آن با مدل‌های رگرسیونی استفاده شد. نتایج نشان داد که مدل شبکه عصبی مصنوعی با توپولوژی 183 با ضریب همبستگی 47/97 درصد و مجذور میانگین مربعات خطای، 0.65 در مقایسه با مدل‌های رگرسیون خطی و درجه دوم کارایی بالاتری در پیش‌بینی میزان روغن استخراجی دارد.

The Effect of Rotational Speed, Temperature, Type of Screw and Die Diameter on the Amount of Oil Extracted from Sesame

Introduction;In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all over the world. Sesame has been considered as an oil plant for cultivation in Iran 's climatic conditions recently. Sesame contains about 5844% oil, 1825% protein and 13.5% carbohydrate. Sesame is grown mainly in the developing tropical and subtropical areas of Asia, Africa. The three countries of China, India and Myanmar are accounted as the largest producers of this product in the world. Screw pressing is the most reliable method for extracting oil from oilseed grains. This method is simpler than others and is more efficient in terms of cost and food security. The general objective of this research was to investigate the effects of rotational speed, temperature, type of screwing and die diameter on the amount of oil extraction from sesame oil and prediction of oil extraction using artificial neural network and compare to regression models.;Materials and Methods;In this research, a sesame oil extractor machine was designed and manufactured. Various experiments were carried out to determine the amount of oil extracted based on variable parameters such as the shape of the press screw, the rotational speed, the temperature and the diameter of the die. The experiment was performed at three levels of press screw type (constant pitch, variable pitch and conical), temperature (30, 60, 90), three levels of rotational speed (20, 50, 80 rpm) and three level of die diameter (6, 8, 10mm). The experimental design was factorial based on completely randomized design with three replications. The mathematical software (Matlab, 2012b) was used to determine the optimal neural network. The type of network was MultiLayer Perceptron (MLP). In order to design this network, there were 3 neurons in the first layer (input), which was equal to the number of studied variable parameters (type of screw, rotational speed and temperature), the second layer was hidden layer, and the last layer (the output) had a neuron for the extracted oil) was equal to the number of outputs examined in this network. The LevenbergMarquardt algorithm (LM) was used to train it, which is one of the fastest neural network training methods. The Secondorder polynomial regressions were performed based on the stepbystep method and nonmeaningful sentences were eliminated from the model. The accuracy of the models was determined by calculating the correlation coefficient and root mean square error (RMSE) indices.;Results and Discussion;The results of the experiments showed that the effect of type of press screw, rotational speed, extraction temperature and die diameter on the amount of oil extraction was significant (p≤0.01). The highest amount of extracted oil was obtained at conical press screw , rotational speed of 50 rpm, temperature of 60 °C and die diamter of 6 mm. An artificial neural network of threelayer perceptron and regression models were used to predict the amount of sesame oil extracted. The results showed that the artificial neural network model (183) with a correlation coefficient of 97.47% and a RMSE of 0.65 compared to linear regression and quadratic regression models had the higher efficiency in predicting the amount of extracted oil.;Conclusions;In this study, the effect of temperature, rotational speed, press screw type and die diameter on the amount of extracted oil were investigated. The results of this study showed that the change in the type of screw, rotational speed, diameter of die and temperature on the amount of extracted oil was significant at 1% level. Results also showed that the artificial neural network method was more efficient than linear and second order regression methods.