I.3.7. Engineering
B. Hosseinzadeh Samani; H. HouriJafari; H. Zareiforoush
Abstract
In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated ...
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In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated according to the statistical source, balance-sheets and the method proposed in this paper. It can be seen that diesel and liquefied petroleum gas have respectively the highest and lowest shares of energy consumption compared with the other types of carriers. For each of the evaluated energy carriers (diesel, kerosene, fuel oil, natural gas, electricity, liquefied petroleum gas and gasoline), the best fitting model was selected after taking the average of runs of the developed models. At last, the developed models, representing the energy consumption of food and beverage industries by each energy carrier, were put into a finalized model using Simulink toolbox of Matlab software. Results of data analysis indicated that consumption of natural gas is being increased in Iran food and beverage industries, while in the case of fuel oil and liquefied petroleum gas a decreasing trend was estimated.
Mohaddeseh Dashti; Vali Derhami; Esfandiar Ekhtiyari
Abstract
Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity ...
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Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Ne. As the first step for modeling, the empirical data is collected for cotton yarns. Then, the structure of the neural network is determined and its parameters are adjusted by back propagation method. The efficiency and accuracy of the neural model is measured based on percentage of error as well as coefficient determination. The obtained experimental results show that the neural model could predicate the tenacity with less than 3.5% error. Afterwards, utilizing genetic algorithms, a new method is proposed for optimal determination of input values in yarn production to reach the desired tenacity. We conducted several experiments for different ranges with various production cost functions. The proposed approach could find the best input values to reach the desired tenacity considering the production costs.