Babji Srinivasan

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.

Human operator performance is a key factor for proper operation of safety critical industries like process control, nuclear power plant and aviation. Several incident reports identify human error as one of the causes responsible for accidents. One way to reduce human error is to impart proper operator training which requires understanding how operators learn during training. Traditionally, learning is evaluated on the basis of subjective assessments and outcomes of the task execution. It obscures the cognitive aspect of the learning process like understanding how operator gives attention to various information sources and develops the process of decision making during the training program. In this study, we use pupil size measurement from eye tracking system to study and understand learning as participants undergo repetitive trials of simulated flight operations tasks. A derived measure based on time-frequency analysis of pupil size variation is observed to be sensitive to the process of learning during task repetition. We observe improvement in the task performance accompanied by the decrease in the proposed measure.

Human errors are a root cause of majority of accidents occurring in the process industry. These errors are often a result of excessive workload on operators, especially during abnormal situations. Understanding and measurement of cognitive workload (overload), experienced by human operators while performing key safety critical tasks, is thus important to the understanding of human errors. Subjective measurements of workload are often not reliable and there is a need for physiological based parameters of workload. In this work, we propose a methodology to measure cognitive load of a control room operator in terms of a biomarker, specifically theta/alpha ratio, obtained from a single electrode EEG signal. Real-time detection of the biomarker can enable minimize errors and improve safety.

A combined cycle power plant (CCPP) is a complex system with a Gas Turbine, Steam Turbine and a Heat Recovery Steam Generator (HRSG) working together. These three units work together and make the process highly interdependent. The onset of any fault in one of the above units would results in a significant reduction in overall efficiency and potentially lead to catastrophic accidents. Such failures can occur due to process faults because of large abrupt variations of operating conditions and structural faults due to corrosion, uneven stresses due to frequent cyclic operations. Conventionally, the identification of such leakage locations is made via visual inspection which is a time consuming and tedious. In the present work, we discuss a fault diagnosis strategy for an actual industrial HRSG present in a CCPP. Various steady state models at different loads of CCPP as well as a dynamic model are developed. Various structural faults in the form of leakages are incorporated in the heat exchangers. An Artificial Neural Network (ANN) model is developed based on data from the above simulations to detect the leaking heat exchangers.

A combined cycle gas turbine (CCGT) is a complex interconnected system that uses gas turbine's hot exhaust to power the steam power plant for achieving higher thermal efficiency. Since performance of CCGT power plants is governed by huge number of parameter linked to various components (such as compressor, combustion unit and turbine), alarms are configured to inform system operators of the abnormal operating conditions (Wong et al., 2013). Alarm systems also play a vital role in ensuring safe operation of power plants. However poor management of alarm systems result in alarm flooding, nuisance alarms, chattering alarms and false alarms that can distract the operator from determining the true state of the process. Studies indicate poor alarm system to be one of the major causes for incidents (such as BP Texas refinery and Texaco's Oil Refinery, Milford Haven) in process industries (Shu et al., 2016). Therefore, alarm management has received much attention over the past with guidelines available for alarm rationalization, prioritization and elimination of nuisance alarms. In this work, we studied the alarm data collected from Dhuvaran CCGT power plant in Gujarat, India. Event log obtained for 6 days from the plant indicates that 12186 alarms are flagged per day (compared to 144 alarms as per EEMUA-191 guidelines) with approximately 8 alarms every minute (EEMUA-191,2013). We implemented univariate (such as chattering index) and multivariate data analytic tools (such as correspondence analysis) on this alarm data obtained from combined cycle gas turbine power plant to perform alarm rationalization. We removed chattering alarms (by varying threshold based on guidelines in the literature), reduced the replicated alarms and grouped multiple alarms based on correspondence analysis to introduce unit level alarms. After performing these tasks, the average alarm count reduced to 698 per day, a reduction of 94.3% alarms. We will discuss the details of the proposed alarm management system and demonstrate the results obtained from its applicability to CCGT power plant.

The dairy sector in India is the largest milk producer in the world. Substantial amounts of freshwater and energy are consumed during milk processing with concomitant impacts on sustainability. In this chapter, we study the energy efficiency at India’s largest milk processing plant and propose retrofits for improving the plant’s sustainability. Specifically, we report on exergy analysis of a milk powder manufacturing unit. Exergy of a system at a certain thermodynamic state is the maximum amount of work that can be obtained when the system moves from that state to one of equilibrium with its surroundings. In contrast to a conventional energy analysis, which maps the energy flows of the system and suggests opportunities for process integration, an exergy analysis pinpoints the locations, causes, and magnitudes of thermodynamic losses. The milk powder plant that is the focus of the current study consists of two sections—an evaporation section and a drying section. Our results reveal that exergy efficiency of certain units is very low (<20%). Significant improvements in energy efficiencies can be achieved through simple, low-cost retrofits to these units.

The increasing demand of residential consumption and the integration of renewable energy sources have motivated researchers to develop grid simulations for testing energy management strategies. Agent-based modelling is one such methodology with the capability of mimicking the emergent and complex behaviour of grids over time. Thus, we have utilized this concept to model and predict the energy consumption of a house. Results from the simulation indicate the proposed approach closely mimics the fine-grained energy data obtained from the residential unit in India. This model possesses the flexibility to be extended to estimate the electricity demand of different localities in India and, in step, to understand the behaviour of the agent with the integration of low carbon technology.

To minimize human errors (principal reasons for accidents in process industries) it is imperative to understand their cognitive workload, the excess of which is often a preliminary state leading to human errors. In this work, we have devised a methodology based on an eye tracking parameter—gaze entropy—to gauge the variation of cognitive work load on a control room operator. The study highlights the potential of gaze entropy in observing the variation of cognitive workload with learning. The patterns observed have a potential to minimize human errors and improve safety in process industries.