Browsing by Author "Ganje, Mohammad"
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- Application of an adaptive neuro_fuzzy inference system (ANFIS) in the modeling of rapeseeds' oil extractionPublication . Farzaneh, Vahid; Bakhshabadi, Hamid; Gharekhani, Mehdi; Ganje, Mohammad; Farzaneh, Farahnaz; Rashidzadeh, Shilan; Carvalho, Isabel S.In the present study, the temperature and moisture content of the output seeds of the cooking pot were considered as inputs or independent variables and the insoluble fine partial content of the extracted oil, moisture content of the extracted oil and obtained meals, as well as the oil content of the achieved meals and acidity value of the extracted oil were considered as responses and were designed. Three different activation functions, including Gaussian membership and triangular as well as trapezoidal were applied and studied. The trapezoidal function with a 3-3 membership function for the three output variables including insoluble fine partials of oil, oil acidity and moisture content of the meals as well as the triangle membership function with 2-2 and 3-3 functions, respectively, for moisture content of the extracted oil and the oil content of the obtained meals were evaluated and detected as optimized models in the current study. The above mentioned models demonstrated higher correlation coefficients (R-2) between the experimental and predicted values and the lowest root mean squared errors, confirming the adaptability of the applied models in the present study. Practical applicationsToday, because of the high demand for crops for extensive application in the human diet, increases in the efficiency of the processing are attracting much more attention. In this regard, discovering and detection of the optimized conditions for processing with the minimum wastes looks very important and economic. Therefore, the uses of predictive methods in different food processes have been considered appropriate tools for improving of the efficiency of the processes as well as the enhancement of the quality of the produced products. In this respect, the ANFIS design as a novel predictive analytic tool, along with Response Surface Methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Thus regarding the above mentioned content, ANFIS design was applied to predict and optimize some of the selected physico-chemical properties of the extracted oil through the extraction process. Therefore prediction of the optimized conditions of oil extraction could improve the quality of the extracted oil and performance of the extraction process with the minimum wastes during short and logical extraction time.
- Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic DesignPublication . Farzaneh, Vahid; Ghodsvali, Alireza; Bakhshabadi, Hamid; Ganje, Mohammad; Dolatabadi, Zahra; Carvalho, Isabel S.The current paper indicates the systematic determination of the optimal conditions for the selected physical properties of the fava bean. The effects of varying moisture content of the Barkat fava bean grown in Golestan, Iran, in the range of 9.3-31.3% (Input) on the 15 selected physical properties of the crop, including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image; gravity and frictional parameters like the weight of 1000 seeds; true density; bulk density; volume and porosity as well as friction (filling and vacating angle stability) as the outputs were predicted. Afterwards, a model relying on fuzzy logic for the prediction of the 15 outputs had been presented. To build the model, training and testing using experimental results from the Barkat fava bean were conducted. The data used as the input of the fuzzy logic model are arranged in a format of one input parameter that covers the percentage of the moisture contents of the beans. In relation to the varying moisture content (input), the outcomes (15 physical parameters) were predicted. The correlation coefficients obtained between the experimental and predicted outputs as well as the Mean Standard Deviation indicated the competence of fuzzy logic design in predicting the selected physical properties of fava bean seeds. Practical ApplicationToday, because of the high demand for crops to be used extensively in the human diet, enhancements in the efficiency of the processing are getting more attention. In this way, finding and/or the determination of the optimal conditions for processing with minimum waste looks very substantial. Therefore, the use of prediction methods in food processing is considered to be a tool for improving the efficiency and the quality of the produced products. In this regard, the fuzzy logic design as a novel prediction tool, along with response surface methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Therefore Fuzzy Logic Design is optimized to predict the some of the selected physical properties of fava bean, as a function of seed's moisture content. Therefore predicting the behavior of this crop against different moisture contents can improve the quality and performance of the products with the minimum wastes during very short time.(c) 2016 Wiley Periodicals, Inc