In the present work, the influences of temperature, solvent concentration and ultrasonic irradiation time were numerically analyzed on viscosity reduction of residue fuel oil (RFO). Ultrasonic irradiation was applied at power of 280 W and low frequency of 24 kHz. The main feature of this research is prediction and optimization of the kinematic viscosity data. The measured results of eighty-four...

This study investigates the oil extraction from Pistacia Khinjuk by the application of enzyme.Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were applied formodeling and prediction of oil extraction yield. 16 data points were collected and the ANN was trained with onehidden layer using various numbers of neurons. A two-layered ANN provides the best results, us...

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1),GARCH(1,1), EGARCH(1,1) and ARIMA-...

in this search drying characteristics of green pea (pisum satium) with an initial moisture content of 76% (db) was studied in a fluidized bed dryer assisted by microwave heating. four drying air temperatures (30, 40, 50 and 60ºc) and five microwave powers (180, 360, 540, 720 and 900w) were adopted. at each drying operating conditions the volume of green pea was calculated by measuring the three...

in this study greenhouse tomato production was investigated from energy consumption and greenhouse gas (ghg) emission point of views. moreover, artificial neural networks (anns) and adaptive neuro-fuzzy inference systems (anfis) were employed to model energy consumption for greenhouse tomato production. total energy input and output were calculated as 1316.14 and 281.1 gj/ha. among the all ener...

In this study, an artificial neural network (ANN) model was developed for predicting an optimum bonding strength of heat treated woods. The MATLAB Neural Network Toolbox was used for the training and optimization of the ANN model. The ANN model having the best prediction performance was detected by trying various networks. Then, the ANN results were compared with the results of multiple linear ...

This study presents an artificial neural network (ANN) model to predict the values of the longitudinal dispersion coefficient ) ( l D in rivers from their main hydraulic parameters. The model can be considered as a useful aid to water quality monitoring in rivers. The ANN model is a relatively new promising technique which can make use of the river width, depth, velocity, and shear velocity for...

Deterioration models are important and essential part of any Pavement Management System (PMS). These models are used to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are based on roughness which is one of the most important indices in p...

Thermal errors can have significant effects on CNC machine tool accuracy. The errors usually come from thermal deformations of the machine elements created by heat sources within the machine structure or from ambient change. The performance of a thermal error compensation system inherently depends on the accuracy and robustness of the thermal error model. In this paper, Adaptive Neuro Fuzzy Inf...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.