A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments. (March 2020)

• Record Type: Journal Article
• Authors: Ancione, Giuseppa; Bragatto, Paolo; Milazzo, Maria Francesca
• Is Part Of: Journal of loss prevention in the process industries. Volume 64(2020)
• Publication Date: 2020-03
• Subjects: Equipment ageing -- Major accident hazard -- Seveso directive -- Deterioration mechanism -- Risk-based inspection -- Bayesian network; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2020.104080
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts' knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method. Highlights: Ageing is as a significant cause of major accidents in chemical industries. The Bayesian network theory verified the robustness of an ageing assessment method. A cross-validation demonstrated a high accuracy of the network in ageing predicting.