Approximate physical modelling in dynamic PSA using artificial neural networks

✍ Scribed by M. Marseguerra; M. Nutini; E. Zio

• Publisher: Elsevier Science
• Year: 1994
• Tongue: English
• Weight: 712 KB
• Volume: 45
• Category: Article
• ISSN: 0951-8320
• DOI: 10.1016/0951-8320(94)90075-2

✦ Synopsis

The continuous increase in the computational power of modern computers allows us to consider the feasibility of extending the present PSA studies, based on the usual probabilistic approach, to those aspects connected with the plant's dynamics. Indeed, in many cases the evolution of the process variables strongly affects the safety characteristics of a plant and, therefore, it cannot be neglected. Such a dynamic analysis requires solving the mathematical models describing the plant's behaviour. The corresponding equations need to be integrated with time steps which are related to the time evolution of the physical processes and therefore much smaller than the characteristic times typical of the PSA analyses. This procedure leads, in general, to very large computer times, so that it is presently still prohibitive for real plants. Therefore many current investigations are concerned with the development of new methodologies currently tested on simple study cases.

In the present paper we consider the application of a multilayered, supervised artificial neural network trained by the error back-propagation algorithm for the solution of the mathematical models related to a simple study case of a dynamic PSA. In the examined case, the results indicate a reduction in the computer time, while the inherent approximations do not exceed those resulting from the uncertainties in the input data. These advantages are expected to increase when the future parallel computers become available.