Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-...

Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cut...

Artificial neural networks are richly connected networks of simple computational elements modeled on biological processes. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In thi...

Free swelling index (FSI) is an important parameter for cokeability and combustion of coals. In this research, the effects of chemical properties of coals on the coal free swelling index were studied by artificial neural network methods. The artificial neural networks (ANNs) method was used for 200 datasets to estimate the free swelling index value. In this investigation, ten input parameters ...

Anti–correlation has been used in training neural network ensembles. Negative correlation learning (NCL) is the state of the art anti–correlation measure. We present an alternative anti–correlation measure, RTQRT–NCL, which shows significant improvements on our test examples for both artificial neural networks (ANN) and genetic programming (GP) learning machines. We analyze the behavior of the ...

Abstract Anti–correlation has been used in training neural network ensembles. Negative correlation learning (NCL) is the state of the art anti–correlation measure. An alternative anti–correlation measure, RTQRT–NCL, which shows significant improvements on our test examples for both artificial neural networks (ANN) and genetic programming (GP) learning machines, is presented. We analyze the beha...

the present investigation entails a procedure by which the surface tension and viscosity of liquids could be redicted.to this end, capillary experiments were performed for porous media by utilizing fifteen different liquids and powders. the time of capillary rise to a certain known height of each liquid in a particular powder was recorded. two artificial neural networks (anns) were designed and...

This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of ne...

Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...