مطالعه تجربی و بررسی عملکرد شبکه‌های عصبی mlp و rbf برای مدل‌سازی فرآیند خشک شدن شلتوک در خشک‌کن مایکروویو

برنج یکی از گیاهان مهم تیره غلات است و غذای اصلی اکثر مردم دنیا به‌شمار می آید. خشک کردن برنج بعد از برداشت، به جهت غیرفعال کردن عاملین فساد، امری مهم و ضروری است. در این تحقیق به‌منظور بررسی اثر توان مایکروویو بر روی سینتیک خشک شدن شلتوک، ضریب نفوذ موثر رطوبت، درصد برنج سالم و کیفیت برنج از یک خشک‌کن مایکروویو استفاده شده است. به‌منظور خشک کردن شلتوک از رطوبت اولیه 21 درصد تا رطوبت نهایی 11 درصد از 3 توان270، 360 و 450 وات استفاده شد. همچنین مدل‌سازی سینتیک خشک شدن شلتوک در خشک‌کن مایکروویو با استفاده از 2 شبکه عصبی mlp و rbf انجام شد. نتایج حاصل از این بررسی بدین صورت بوده که حداکثر و حداقل زمان خشک شدن به‌ترتیب در توان 270 و 450 وات و برابر با 42 و 20 دقیقه بوده است. با افزایش توان مایکروویو میزان شکستگی افزایش یافته و درصد برنج سالم کاهش می یابد. و همچنین خشک کردن برنج توسط خشک‌کن مایکروویو تاثیری بر روی کیفیت برنج نداشته است. از نتایج مدل‌سازی انجام شده می‌توان دریافت که شبکه rbf با تابع انتقال گوسی با شاخص پراکندگی و تعداد نورون بالا بهترین عملکرد را در قیاس با سایر حالات طراحی شده در شبکه rbf داشته است و شبکه mlp با الگوریتم آموزش لونبرگ مارکوارت و تابع انتقال تانژانت سیگموئید با تعداد نورون پایین قادر بوده مدل‌سازی سینتیک خشک شدن را به‌خوبی انجام دهد. به‌طور کلی شبکه عصبی mlp عملکرد بهتری نسبت به شبکه عصبی rbf داشته است و میزان خطا و ضریب همبستگی آن به‌ترتیب کمتر و بیشتر از شبکه عصبی rbf بوده است.

Modeling and investigation of the performance of MLP and RBF during the paddy rice drying in microwave dryer

Introduction: Rice is one of the most important cereals and is the secondhighest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial moisture content of nearly 2528% (wet basis) that caused corruption. Therefore, in order to prevent corruption and safe storage, it must be dried to 1013% moisture content. Drying is one of the oldest methods of preserving food and agricultural products that used to increase the food’s storage time. There are several methods for drying paddy rice that none of them are ideal and have several advantages and disadvantages that one of them that recently the use of it has been increased is microwave drying. Microwave drying uses electromagnetic radiations with the frequency range of 300 MHz to 300 GHz and the wavelengths of 10.01m. In microwave drying due to better energy concentration, moisture is removed more quickly so the drying time decreases. Due to the complex relationship between input and output variables in the drying process, the selection of the model that can estimate the drying behavior of the products is difficult. Hence, the use of intelligent modeling methods such as neural networks is the best choice.; ;Materials and methods: In this research, in order to investigate the effect of microwave power on kinetics of rice drying, head rice yield and effective diffusivity coefficient of moisture, a continuous type of domestic microwave dryer ( DEM281 QOTPW) were used. This dryer has a microwave radiation chamber where the samples are put on it on the tray that was placed on a digital balance. The experiments were performed at three microwave power levels designated as 270, 360 and 450 W. Also, Shirudi paddy rice was used as the raw material and the drying rice process from the initial moisture content of 21% to the final moisture content of 11% is examined. In this study, the neural network toolbox of MATLAB 2017R was used to model the kinetics of rice drying in the microwave dryers. RBF and MLP have 3 layers including input, hidden and output layers. The input layer has two neurons that show the number of input variables that were time and microwave power and the output layer has one neuron that shows the number of output variables that was MR in this study. 70% and 30% of the data was used for training and testing the network, respectively. To estimate the ANN performance, mean square error (MSE) and the coefficient of determination (R2) was used.; ;Results and discussion: The maximum and minimum drying time was 42 and 20 minutes in 270 and 450 watts, respectively. Also, the maximum and minimum effective diffusivity coefficient of moisture were 4.17 * 10^9 and 1.82* 10^9 in 450 and 270 watts, respectively. RBF network with Guassian transfer function and high neurons number and MLP network with LevenbergMarquardt ( LM) learning algorithm and tansigmoid (tansig) transfer function with low neurons number were able to model the kinetics of drying as well as. In general, the drying time and head rice yield decreased but the effective diffusivity coefficient of moisture increased by increasing the microwave power. Also drying at different microwave power did not affect rice color and quality. The results of the modeling of rice drying by using two different neural networks including MLP and RBF demonstrated that the MLP network with LevenbergMrrqurdt (LM) learning algorithm and tansigmoid (tansig) transfer function has the better performance than the RBF network with Gussian transfer function and the error and the correlation coefficient in MLP are less and higher than the RBF, respectively.