Making decisions and managing competences in complex systems is a challenging task to accomplish. Specifically, the process industry is known for its complexity and sensitivity to critical procedures. Recent disasters like the ‘‘Deepwater Horizon” (2010, 11 fatalities), BP Texas City (2005, 15 fatalities), and AZF Toulouse (2001, 29 fatalities), clearly showed the risk to which we are all exposed. The increasing complexity of processes, due to the simultaneous escalation of automation, optimisation and intensification processes (followed to face globalisation challenges), are moving the attention to the management of abnormal situations, which are even more complex in nature and frequent. This increasing complexity, coupled with the fact that abnormal situations may lead to irreversible losses, is imposing the adoption of adequate approaches and tools that allow for better learning and properly managing abnormal situations. The paper presents a simulation-enabled, experiment-based approach that can be used to prevent and manage risk through competencies management. More specifically, the paper presents the results of the first experiment campaign performed in a Plant Simulator (PS), the first known in the process industry domain, and shows the efficacy of using Immersive Virtual Environments (IVE) both to make decisions and to train teams (not just single operators). The experiment results presented in the paper show the effectiveness of IVE to increase the competencies and train operators and managers. In addition, they explain how conveniently the data collected by means of the PS can be used for making daily decisions to better prevent and manage risks.

The Plant Simulator as viable means to prevent and manage risk through competencies management: Experiment results / Golzio, Luigi Enrico; Simone, Colombo. - In: SAFETY SCIENCE. - ISSN 0925-7535. - STAMPA. - 84(2016), pp. 46-56. [10.1016/j.ssci.2015.11.021]

The Plant Simulator as viable means to prevent and manage risk through competencies management: Experiment results

GOLZIO, Luigi Enrico;
2016

Abstract

Making decisions and managing competences in complex systems is a challenging task to accomplish. Specifically, the process industry is known for its complexity and sensitivity to critical procedures. Recent disasters like the ‘‘Deepwater Horizon” (2010, 11 fatalities), BP Texas City (2005, 15 fatalities), and AZF Toulouse (2001, 29 fatalities), clearly showed the risk to which we are all exposed. The increasing complexity of processes, due to the simultaneous escalation of automation, optimisation and intensification processes (followed to face globalisation challenges), are moving the attention to the management of abnormal situations, which are even more complex in nature and frequent. This increasing complexity, coupled with the fact that abnormal situations may lead to irreversible losses, is imposing the adoption of adequate approaches and tools that allow for better learning and properly managing abnormal situations. The paper presents a simulation-enabled, experiment-based approach that can be used to prevent and manage risk through competencies management. More specifically, the paper presents the results of the first experiment campaign performed in a Plant Simulator (PS), the first known in the process industry domain, and shows the efficacy of using Immersive Virtual Environments (IVE) both to make decisions and to train teams (not just single operators). The experiment results presented in the paper show the effectiveness of IVE to increase the competencies and train operators and managers. In addition, they explain how conveniently the data collected by means of the PS can be used for making daily decisions to better prevent and manage risks.
84
46
56
The Plant Simulator as viable means to prevent and manage risk through competencies management: Experiment results / Golzio, Luigi Enrico; Simone, Colombo. - In: SAFETY SCIENCE. - ISSN 0925-7535. - STAMPA. - 84(2016), pp. 46-56. [10.1016/j.ssci.2015.11.021]
Golzio, Luigi Enrico; Simone, Colombo
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0925753515003197-main.pdf

accesso aperto

Tipologia: Versione dell'editore (versione pubblicata)
Dimensione 1.69 MB
Formato Adobe PDF
1.69 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1080045
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 19
  • ???jsp.display-item.citation.isi??? 11
social impact