A New Algorithm of Sensitivity Analysis Based on Neural Network for Safety Engineering
In order to make the sensitivity analysis for the practical problem in the oil and gas storage and transportation more accurate and convenient, the sensitivity analysis method based on neural network is proposed. First of all, use neural network to establish a model of needs analysis
problem. Then use the method add to the input vector white Gaussian noise to achieve the disturbance input variables, and the mean changes in the value of output variables be as the sensitivity coefficient of the input variables. Through the practical application in the wax deposition rate
problems, indicates that the data model is established by artificial neural network has high precision and reliability, and the sensitivity analysis method based on input variables disturbance has clear physical meaning, simple calculation features. Through these studies, the sensitivity analysis
shows that based on neural network is effective.
Keywords: Neural Network; Oil and Gas Storage and Transportation; Sensitivity Analysis; Wax Deposition Rate
Document Type: Research Article
Affiliations: 1: School of Civil Engineering, Wuhan University, Wuhan 430072, China 2: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Publication date: 01 November 2015
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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