Simplified Model of the Internal Atmosphere of Flammable Liquid Tanks in Case of Air Inlet from a Pressure Safety Valve

Storage of flammable liquids is a common activity in many industrial domains. A history of accidents shows that liquid storage has been involved in several critical accidents due to the large amount of hazardous substances potentially involved in the incident. Safe storage of flammable liquids is often guaranteed through blanketing of the internal atmosphere of the tank through the introduction of an inert gas, usually nitrogen. A double action pressure safety valve is often installed on the tank to protect the tank from damage in the event of overpressure or depression. In case of depression, an inert gas, usually nitrogen, is fed to the vapor space of the tank to maintain the vapor composition outside of the flammability limits. In case of lack of nitrogen, the opening of the pressure safety valve allows air to enter. The entry of air, especially if prolonged, can bring the atmosphere inside the tank to explosive conditions. This paper presents a simplified model for the estimation of the internal composition of the tank following the entry of air due to the opening of the pressure safety valve, following the process of fluid removal in case of lack of nitrogen. The model also allows the estimation of how much liquid can be safely removed. The simplified model can analyze both the case of a single tank and a tank farm

Using Field Data for Energy Efficiency Based on Maintenance and Operational Optimisation. A Step towards PHM in Process Plants

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 operationsandmaintenanceresultsshouldbemonitored,andtheconnectionbetweentheinputsand theoutcomesofthemaintenanceprocess,intermsoftotalcontributiontomanufacturingperformance, 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 operationalsolutionsforenergyefficiency,throughtheoptimizationofmaintenanceinterventionsand 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

Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

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 Effect of Human Error on the Temperature Monitoring and Control of Freeze Drying Processes by Means of Thermocouples

Monitoring product temperature is mandatory in a freeze-drying process, in particular in the process development stage, as final product quality may be jeopardized when its temperature trespasses a threshold value, that is a characteristic of each product being freeze-dried. To this purpose thermocouples are usually inserted in some of the vials of the batch to track product dynamics. The position of the thermocouple inside the vials strongly affects the reading of the temperature evolution during the freeze-drying process and, thus, it is necessary to place them in the right position, in such a way that correct information about product temperature is obtained. In this work, at first, the probability of the operational error resulting into a wrong positioning of the thermocouple inside the vial has been estimated experimentally. Then, the effect of this error has been assessed in terms of risk of exceeding the limit temperature in the primary drying step. Both 4R and 10R vials have been considered, and the investigation evidenced that the probability of incorrect thermocouples placement can reach 30% for 10R vials, and about 32% for 4R vials. These probability values increase, respectively, to 47 and 39% when the trays containing the vials are shifted to their final position. Then, through IR thermal imaging it has been possible to evaluate the temperature gradients in a vial, pointing out that the temperature difference between the product at the center of the vial, where the thermocouple is supposed to be, and that of the wall, that is quite often measured by the thermocouples, can be about 1°C. Therefore, associated to each thermocouple reading there is a probability distribution of product temperature. These figures can be used to assess the risk of exceeding the limit temperature in a freeze-drying process and, thus, to quantify suitable safety margins when evaluating thermocouple readings to take into account the operational errors, given a risk tolerability criteria

Human factors in alarm response procedures: An empyrical analysis of paper versus digital support.

The objective of a Human Machine Interface (HMI) is to communicate process monitoring information, data, metrics and graphics to an operator through a screen or dashboard and offer an opportunity to control equipment and processes in factories and plants. But after the annunciation of an alarm, how effective is the supporting documentation? The existence of a refined set of instructions and procedures in the form of checklists has been a major factor contributing to the improved safety outcomes observed in the nuclear and aviation sectors. The use of paper-based checklists has been the norm; however, trials of digitized instruction systems have been on the rise in these sectors. The focus of the paper is to analyse an operator on his behaviour and situational awareness from when an alarm is annunciated to the completion of the intervention process using either paper or digitised procedures. The participants (n D 46) were split equally into two groups, each testing three tasks with increasing levels of complexity. Results showed that those who were presented with the procedures on paper had slightly better situational awareness and preferred to use paper procedures when compared to those using the digitised procedures. The rationale for this outcome and recommendation for subsequent redesign of the HMI are presented in this paper

Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework and Application for Decision Support for Operators in Control Rooms

In today’s complex industrial environment, operators are often faced with challenging situations that require quick and accurate decision-making. The human-machine interface (HMI) can display too much information, leading to information overload and potentially compromising the operator’s ability to respond effectively. To address this challenge, decision support models are needed to assist operators in identifying and responding to potential safety incidents. In this paper, we present an experiment to evaluate the effectiveness of a recommendation system in addressing the challenge of information overload. The case study focuses on a formaldehyde production simulator and examines the performance of an improved Human-Machine Interface (HMI) with the use of an AI-based recommendation system utilizing a dynamic influence diagram in conjunction with reinforcement learning. The preliminary results indicate the potential of these methods to aid operators in decision-making during challenging situations and enhance process safety in the industry

Enhancing Control Room Operator Decision Making

: In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations

Experiment data: Human-in-the-loop decision support in process control rooms

These datasets contain measures from multi -modal data sources. They include objective and subjective measures commonly used to determine cognitive states of workload, situational awareness, stress, and fatigue using data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, a survey to assess training, and a thinkaloud situational awareness assessment following the SPAM methodology. Also, data from a simulation formaldehyde production plant based on the interaction of the participants in a controlled control room experimental setting is included. The interaction with the plant is based on a human -in -theloop alarm handling and process control task flow, which includes Monitoring, Alarm Handling, Recovery planning, and intervention (Troubleshooting, Control and Evaluation). Data was collected from 92 participants, split into four groups while they underwent the described task flow. Each participant tested three scenarios lasting 15-18 min with a -10min survey completion and break period in between using different combinations of decision support tools. The decision support tools tested and varied for each group include alarm prioritisation vs. none, paper-based vs. Digitised screen-based procedures, and an AI recommendation system. This is relevant to compare current practices in the industry and the impact on operators' performance and safety. It is also applicable to validate proposed solutions for the industry. A statistical analysis was performed on the dataset to compare the outcomes of the different groups. Decision -makers can use these datasets for control room design and optimisation, process safety engineers, system engineers, human factors engineers, all in process industries, and researchers in similar or close domains

Lean VOC-Air Mixtures Catalytic Treatment: Cost-Benefit Analysis of Competing Technologies

Various processing routes are available for the treatment of lean VOC-air mixtures, and a cost-benefit analysis is the tool we propose to identify the most suitable technology. Two systems have been compared in this paper, namely a “traditional” plant, with a catalytic fixed-bed reactor with a heat exchanger for heat recovery purposes, and a “non-traditional” plant, with a catalytic reverse-flow reactor, where regenerative heat recovery may be achieved thanks to the periodical reversal of the flow direction. To be useful for decisions-making, the cost-benefit analysis must be coupled to the reliability, or availability, analysis of the plant. Integrated Dynamic Decision Analysis is used for this purpose as it allows obtaining the full set of possible sequences of events that could result in plant unavailability, and, for each of them, the probability of occurrence is calculated. Benefits are thus expressed in terms of out-of-services times, that have to be minimized, while the costs are expressed in terms of extra-cost for maintenance activities and recovery actions. These variable costs must be considered together with the capital (fixed) cost required for building the plant. Results evidenced the pros and cons of the two plants. The “traditional” plant ensures a higher continuity of services, but also higher operational costs. The reverse-flow reactor-based plant exhibits lower operational costs, but a higher number of protection levels are needed to obtain a similar level of out-of-service. The quantification of risks and benefits allows the stakeholders to deal with a complete picture of the behavior of the plants, fostering a more effective decision-making process. With reference to the case under study and the relevant operational conditions, the regenerative system was demonstrated to be more suitable to treat lean mixtures: in terms of time losses following potential failures the two technologies are comparable (Fixed bed plant: 0.35 h/year and Reverse flow plant: 0.56 h/year), while in terms of operational costs, despite its higher complexity, the regenerative system shows lower costs (1200 €/year)