Artificial neural network modelling of oil sands extraction processes. (1st April 2004)

• Record Type: Journal Article
• Authors: Zhang, Qing J; Sawatzky, Ronald P; Wallace, E Dean; London, Michael J; Stanley, Stephen J
• Is Part Of: Journal of environmental engineering and science. Volume 3(2004)Supplement 1
• Page Start: S99
• Page End: S110
• Publication Date: 2004-04-01
• Subjects: environment; Environmental engineering -- Periodicals; Sanitary engineering -- Periodicals; Environmental engineering -- Canada -- Periodicals; Environnement, Technique de l' -- Périodiques; Technique sanitaire -- Périodiques; Environnement, Technique de l' -- Canada -- Périodiques; Environmental engineering; Sanitary engineering; Canada; Electronic journals; Computer network resources; Periodicals; 628.05
• DOI: 10.1139/s04-006
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : Although the artificial neural network (ANN) approach has been used in various disciplines of engineering since the 1980s, its use in the oil sands extraction industry is quite new and has great potential. This paper demonstrates two important ANN modelling techniques that will be very useful to the oil sand industry through a case study. First, the ANN pattern recognition approach is illustrated to categorize a large oil sand processing database consisting of experimental data from the last 20 years. Second, within each category, the authors demonstrate how to follow a general protocol to build ANN models that are capable of predicting the primary recovery and the primary froth quality for the oil sand treatment process with fairly good accuracy. To verify the reliability of the ANN models, besides the regular statistical and graphical analysis, the authors also conducted a sensitivity analysis to test the response logic, parameter interaction, and extrapolation capability of the ANN models. As shown in the paper, these tests are both satisfactory and interesting. With sufficient accuracy and robustness in performance, these ANN models can be used to evaluate both the qualitative and the quantitative response of the oil sand treatment process to the known key parameters, which, in turn, can then be used to optimize the oil sand treatment process. Furthermore, these ANN models can be linked with the geological survey results to estimate the production potential of an oil sand ore field and to manage the cost of stockpiling the chemicals for the treatment process. Key words: oil sand processing, artificial neural network, pattern recognition.