Rajagopalan Srinivasan

Chemical processes rely on several layers of protection to prevent accidents. One of the most important layers of protection is human operators. Human errors are a key contributor in a majority of accidents today. Estimation of human failure probabilities is a challenge due to the numerous drivers of human error, and still heavily dependent on expert judgment. In this paper, we propose a strategy to estimate the reliability of control room operators by measuring their control performance on a process simulator. The performance of the operator is translated to two metrics – margin-of-failure and available-time to respond to process events – which can be calculated using process operations data that can be generated from training simulator based studies. These metrics offer a qualitative estimate of operators’ reliability. We conducted a set of experiments involving 128 students of differing capabilities from two different institutions and tasked to control a simulated ethanol production plant. Our results demonstrate that differences in the performance of expert vs. novice student operators can be clearly distinguished using the metrics.

Liquefied natural gas has started to establish itself as the fuel of choice across the globe, evident from its sustained growth over the last three decades. As a result, more regasification terminals are coming up every year. Hence, it is critical to develop technologies which make sure the plants are operated efficiently. This paper studies the generation and management of Boil Off Gases (BOG) in LNG regasification terminals. The effect of time varying ambient temperature on the BOG generation is studied, and ways to improve the operational efficiency of BOG compressors are suggested.

Fouling degrades the overall efficiency of the heat exchanger networks (HENs), which results in a significant economic loss. The mitigation of fouling in an operational HEN is carried out by optimizing the cleaning schedules of the heat exchangers. Although such approach can save costs, it is subjected to the exact implementation of the optimal cleaning schedule. Usually, the small and medium-scale process industries face difficulties in implementing such solutions due to limited resources, which forces them to rely on suboptimal cleaning schedules, such as postponing or avoiding few cleaning tasks. This work addresses this gap by optimizing the cleaning schedule considering the maintenance resource limitation. Our approach considers a mixed-integer linear programming (MILP) based optimization considering groupings of heat exchangers based on their spatial locations for ease of maintenance The proposed formulation is applied on a HEN with linear and asymptotic fouling, with and without cleaning cost. The results show that the approach can prevent a considerable economic loss, which would incur due to suboptimal cleaning schedules due to resource limitations.

Safety in process plants is of paramount importance. With the predominant contributor to accidents in process industries being repeatedly identified as human error, it is necessary to have skilled operators to prevent accidents and minimise the impact of abnormal situations. Such knowledge and skills are imparted to operators using operator training simulator (OTS) which offer a simulated environment of the real process. However, these techniques emphasize on assessing operator's ability to follow standard guidelines – assessment of the operator's process knowledge and imparting an adequate mental model to the operator is not addressed. Further, understanding cognitive behavior of operators, identified as crucial to enhancing their skills and abilities is often neglected. In this work, we develop a systems engineering framework to operator training with emphasis on accounting for the cognitive abilities of the human-in-the-loop. The framework consists of three distinct components: (1) Design of suitable training tasks, (2) Measure and analyse operator's cognitive response while performing the tasks, and (3) Infer operator's mental model through knowledge and capability assessment. Consider the operator as a system whose input is information acquired from the process through the human machine interface (HMI) and output are actions taken on the process (such as manipulating valves). We demonstrate in this paper that the available input (from eye tracking) and output (operator actions) data when suitably analysed with respect to the process state can aid in inferring the operator's mental model at any given time. Based on the model, the operator's current knowledge can be deduced and gaps identified. New training tasks can then be designed to address these gaps. In this article, we describe the proposed framework for operator learning and illustrate it using experimental studies.

To cope with increasing vulnerability, global business especially chemical manufacturing companies need to actively manage (the risk of) disruptive events in their supply chains. This calls for systematic frameworks to guide their efforts. Further, due to the complexity of today's global supply chains, decision making tools are needed to provide support in different stages of the supply chain disruption management process. This paper presents an agent-oriented simulation framework for disruption management in supply chains. This simulation framework provides a flexible modelling and simulation environment for decision makers to experiment with different types of disruptions and disruption management strategies. The application of the simulation model to support decision-making in different steps of the pre- and post-disruption management processes is illustrated using a lube oil supply chain case study.

Operators' mental models play a central role in safety-critical domains like the chemical process industries. Accurate mental models, i.e., a correct understanding of the process and its causal linkages, are prerequisites for safe operation. Mental models are often defined and explained in abstract terms that make their interpretation subjective and prone to bias. In this work, we propose a Hidden Markov Model (HMM) based formalism to characterize control room operators' mental models while handling abnormal situations. We show that a suitable HMM representing the operator's mental model – including the states, state transition probabilities, and emission probability distributions – can be identified experimentally using data of the operator's control actions, eye gaze, and process variable values. This HMM can be used for the quantitative assessment of operators' mental models as illustrated using various case studies. We discuss the potential applications of the model in identifying various cognitive errors and human reliability assessment. In Part 2 of this paper, we use the proposed approach to assess operators' learning during training.

Learning from past incidents is critical to achieving and maintaining high process safety performance. Accident and incident records provide one way for learning; however, these are usually in the form of unstructured texts, which makes analysis difficult. Recently, text mining methods based on supervised learning have been proposed for analyzing accident reports; however, they require an impractically large number of labeled records as training examples. This paper proposes an automated, semi-supervised, domain-independent approach for analyzing accident reports. Given a set of user-defined classification topics and domain literature such as handbooks, glossaries, and Wikipedia articles, the method can identify domain-specific keywords and group them into topics with minimal expert involvement. These keywords and topics can then be used for various data mining purposes, including classification. The proposed approach is demonstrated using two different case studies across domains: (1) in aviation to identify the stage of flight when an accident occurs, and (2) in the process industry domain to identify the cause of pipeline accidents. The average classification accuracy of the proposed method was 80% which is comparable to that of supervised learning methods. The key benefits of this approach are that it can generate domain-specific predictive models with limited manual intervention.

Process plant operators rely on their knowledge of process cause-and-effect relationships during abnormal situation management. Novice operators develop such process knowledge during training. Hence, holistic assessment of operators’ training is essential to ensure process safety. Currently, during training, operators’ process understanding is evaluated using criteria such as successful completion, task based measures, and operator actions that ignore their cognitive behavior. In this work, we propose an eye-tracking-based approach that uses the operator's attention allocation during different pre-specified training scenarios along with process data, alarm information, and operator actions. Our approach is based on the precept that an operator would focus their attention on those variables on the human-machine interface that they believe have a direct causal relationship to the situation at hand. Also, expert operators seek time-based information for proactive monitoring. Accordingly, to quantify the progress of a novice operator's learning, we develop two metrics — association metric and salience metric — using correspondence analysis of operators' eye gaze. To evaluate the applicability of the metrics, we conducted experiments with ten participants who performed 486 tasks. Statistical studies reveal that the proposed metrics can quantify operators’ learning and thus can be used to objectively evaluate training effectiveness.

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal condition in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. However, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.

To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators’ performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators’ cognitive behavior. In addition, these cannot be used to predict operators’ expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator’s behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT’s ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT’s cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator’s mental models. Additionally, the HDT can also enhance operators’ decision-making during real-time operation.