PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Nicola Tamascelli Co-author(s): Antonio Javier Nakhal Akel, Riccardo Patriarca, Nicola Paltrinieri, Ana Maria Cruz
| Are we going towards "no-brainer" risk management? A case study on climate hazards |
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| Industry is stepping into its 4.0 phase by implementing and increasingly relaying on cyber-technological systems. Wider networks of sensors may allow for continuous monitoring of industrial process conditions. Enhanced computational power provides the capability of processing the collected “big data”. Early warnings can then be picked and lead to suggestion for proactive safety strategies, or directly initiate the action of autonomous actuators ensuring the required level of system safety. But have we reached these safety 4.0 promises yet, or will we ever reach them? A traditional view on safety defines it as the absence of accidents and incidents. A forward-looking perspective on safety affirms that it involves ensuring that “as many things as possible go right”. However, in both the views there is an element of uncertainty associated to the prediction of future risks and, more subtle, to the capability of possessing all the necessary information for such prediction. This uncertainty does not simply disappear once we apply advanced artificial intelligence (AI) techniques to the infinite series of possible accident scenarios, but it can be found behind modelling choices and parameters setting. In a nutshell, “there ain't no such thing as a free lunch”, i.e. any model claiming superior flexibility usually introduce extra assumptions. This contribution will illustrate a series of examples where AI techniques are used to continuously update the evaluation of the safety level in an industrial system. This will allow us to affirm with certain confidence that we are not even close to a “no-brainer” condition in which the responsibility for human and system safety is entirely moved to the machine. However, this shows that such advanced techniques are progressively providing a reliable support for critical decision making and guiding industry towards more risk-informed and safety-responsible planning. |
Paper NI33 Preview
Author and Presentation Info
Lead Author Name: Nicola Tamascelli (nicola.tamascelli@ntnu.no)
Bio: I am a second-year Ph.D. student enrolled in a jointly-supervised doctorate at the Norwegian University of Science and Technology (Norway) and The University of Bologna (Italy). My research focuses on the development of Machine Learning algorithms to support a dynamic and proactive approach to process safety. My interests include the analysis and monitoring of industrial alarm systems, the development of classification algorithms to predict the consequences of major accidents, the implementation of regression algorithms for prognostic purposes, and the integration between data-driven simulation models and risk analysis techniques to improve environmental risk management in the Waste-to-Energy industry.
Country: Italy
Company: Norwegian University of Science and Technology
Job Title: Ph.D. student
Bio: I am a second-year Ph.D. student enrolled in a jointly-supervised doctorate at the Norwegian University of Science and Technology (Norway) and The University of Bologna (Italy). My research focuses on the development of Machine Learning algorithms to support a dynamic and proactive approach to process safety. My interests include the analysis and monitoring of industrial alarm systems, the development of classification algorithms to predict the consequences of major accidents, the implementation of regression algorithms for prognostic purposes, and the integration between data-driven simulation models and risk analysis techniques to improve environmental risk management in the Waste-to-Energy industry.
Country: Italy
Company: Norwegian University of Science and Technology
Job Title: Ph.D. student