In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures the uncertainties inherent in complex industrial processes. It excels in anomaly detection and recommending optimal actions. Furthermore, this model is improved through a strategic collaboration with reinforcement learning, which refines the recommendations to be more context-specific and accurate. The primary goal of this AI framework is to equip operators with a live, reliable DSS that significantly enhances their response during process upsets. This paper describes the development of the AI framework and its implementation in a simulated control room environment. Our results show that the DSS can improve operator performance and reduce cognitive workload. However, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance. Our study highlights the necessity of balancing the advantages of decision support with the need to maintain operator engagement and understanding during process operations.

Energy saving is an important issue for any industrial sector; in particular, for the process industry, it can help to minimize both energy costs and environmental impact. Maintenance optimization and operational procedures can offer margins to increase energy efficiency in process plants, even if they are seldom explicitly taken into account in the predictive models guiding the energy saving policies. To ensure that the plant achieves the desired performance, maintenance operations and maintenance results should be monitored, and the connection between the inputs and the outcomes of the maintenance process, in terms of total contribution to manufacturing performance, should be explicit. In this study, a model for the energy efficiency analysis was developed, based on cost and benefits balance. It is aimed at supporting the decision making in terms of technical and operational solutions for energy efficiency, through the optimization of maintenance interventions and operational procedures. A case study is here described: the effects on energy efficiency of technical and operational optimization measures for bituminous materials production process equipment. The idea of the Conservation Supply Curve (CSC) was used to capture both the cost effectiveness of the measures and the energy efficiency effectiveness. The optimization was thus based on the energy consumption data registered on-site: data collection and modelling of the relevant data were used as a base to implement a prognostic and health management (PHM) policy in the company. Based on the results from the analysis, efficiency measures for the industrial case study were proposed, also in relation to maintenance optimization and operating procedures. In the end, the impacts of the implementation of energy saving measures on the performance of the system, in terms of technical and economic feasibility, were demonstrated. The results showed that maintenance optimization could help in reaching an energy costs recovery equal to the 10% of the total costs for an electric motor system.

Data on occupational accidents are usually stored in large databases by worker compensation authorities, and by the safety and prevention teams of companies. An analysis of these databases can play an important role in the prevention of accidents and the reduction of risks, but it can be a complex procedure because of the dimensions and complexity of such databases. The SKM (SOM K-Means) method, a two-level clustering system, made up of SOM (Self Organizing Map) and K-Means clustering, has obtained positive results in identifying the dynamics of critical accidents by referring to a database of 1200 occupational accidents that had occurred in the wood industry. The present research has been conducted to validate the recently presented SKM methodology through the analysis of a larger data set of more than 4000 occupational accidents that occurred in Piedmont (Italy), between 2006 and 2013. This work has partitioned the accidents into groups of different accident dynamics families and has quantified the severity and frequency of occurrence of these accidents. The obtained information may be of help to Company Managers and National Authorities to better address preventive measures and policies concerning the clusters that have been identified as being the most critical within a risk-based decision-making framework.

The deep modifications to climate are currently provoking risks of increasing impact, that can cause unexpected consequences, interacting with other risks. However, the available planning regulations and instruments appear inadequate to face this challenge, most of all at a local scale. This paper presents a semi-quantitative methodology for the assessment of multiple risks, developed for the direct use of the municipality technicians, in order to increase their awareness towards multiple risks and unexpected events that could hit their territory. The methodology is based on the assignation of rates to the risks, and on a simple calculation of the binary interactions. It was tested on two Italian case studies, revealing a good feasibility in the results obtained for the interactions, and highlighting some problems neglected in the sectorial risk plans. The methodology is a background knowledge of the ‘Responsible Risk Resilience Center’ (R3C) of Politecnico di Torino, and it was furtherly developed through an in-depth analysis of the territorial vulnerabilities. This paper introduces two new indicators of sensitivity towards external risks, related to fire and flood risks, proposed for the application at a local scale. The indicators belong to a wider R3C framework in the phase of development to operationalize resilience.