Automation in Port Areas and Industry for Safe and Effective Management of Dangerous Goods

In the present era, the spread of cyber-physical systems within the framework Industry 4.0, is leading towards a complete automation of industrial processes, which are increasingly decentralized, smart, and require fewer and fewer frontline personnel. The storage, transportation, and handling of dangerous goods in port areas pose significant challenges due to the potential risks involved. The risk assessment process is certainly not excluded from the revolution and in perspective needs to be automatic, dynamic, and linked with the actual emerging conditions of complex systems. This paper focuses on designing an operational management system suitable to predict and reduce operational errors, improving the learning and education patterns, thus decreasing personnel vulnerability to hazardous substances. The system, based on ML algorithms and Bayesian reasoning, is constantly “learning” with the data provided by the physical system.