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.

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.

In an attempt to improve process safety, today's plants deploy sophisticated automation and control strategies. Despite these, accidents continue to occur. Statistics indicate that human error is the predominant contributor to accidents today. Traditionally, human error is only considered during process hazard analysis. However, this discounts the role of operators in abnormal situation management. Recently, with the goal to develop proactive strategies to prevent human error, we utilized eye tracking to understand the situation awareness of control room operators. Our previous studies reveal the existence of specific eye gaze patterns that reveal operators’ cognitive processes. This paper further develops this cognitive engineering based approach and proposes novel quantitative measures of operators’ situation awareness. The proposed measures are based on eye gaze dynamics and have been evaluated using experimental studies. Results demonstrate that the proposed measures reliably identify the situation awareness of the participants during various phases of abnormal situation management.

A variety of methods have been developed to identify the presence of stiction in linear closed-loop systems. One of the commonly used approaches is based on identification of a Hammerstein model (linear dynamic model preceded by static nonlinear element) between the controller output and process output. These techniques utilize the fact that control valve stiction introduces nonlinearities in the otherwise linear feedback system. However, the present work shows that these techniques could provide ambiguous results depending on the frequency response of the controller and the process of interest. Therefore, in this work, a reliability measure to validate the results from Hammerstein model-based stiction detection approaches is proposed. This measure of reliability is important from an industrial perspective because (i) it helps in reducing false alarms and (ii) it improves the computational speed by guiding the selection of search space in the linear model identification step. The applicability of this reliability measure in industrial setting is demonstrated using various simulation and industrial case 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.

A new technique is developed for assessing the performance of linear and nonlinear Kalman filter based state estimators. The proposed metric will indicate the performance of these state estimators which will be primarily influenced by: (i) difference between the model dynamics and process dynamics and, (ii) various approximations of the nonlinear plant dynamics used in nonlinear Kalman filters. Currently, there exists no such quantification method to analyze the performance of linear and nonlinear Kalman filters, a key requirement for improvement and a practical benchmark for comparison of these state estimation algorithms. The proposed technique uses the generalized Hurst exponent of the prediction errors (difference in measured output and a posteriori estimates) obtained from the state estimators to quantify the performance. This technique could be implemented on-line as it requires only plant operating data and the predicted outputs (from the linear and nonlinear Kalman filters) to assess the performance. Several simulation studies demonstrate the applicability of the proposed performance metric to both linear and non-linear Kalman filters. © 2014 American Automatic Control Council.

This work presents a new look at the existing data-based and non-intrusive PI (proportional-integral) controller tuning assessment methods for SISO (single-input single-output) systems under regulatory control. Poorly tuned controllers are a major contributor to performance deterioration in process industries both directly and indirectly, as in the case of actuator cycling and eventual failure due to aggressive tuning. In this paper, an extensive review and classification of performance assessment and automated retuning algorithms, both classical and recent is provided. A subset of more recent algorithms that rely upon classification of poor tuning into the general categories of sluggish tuning and aggressive tuning are compared by their diagnostic performance. The Hurst exponent is introduced as a method for diagnosis of sluggish and aggressive control loop tuning. Also, a framework for more rigorous definitions than previously available of the terms "sluggish tuning" and "aggressive tuning" are provided herein. The performance of several tuning diagnosis methods are compared, and new algorithms for using these tuning diagnosis methods for iterative retuning of PI controllers are proposed and investigated using simulation studies. The results of these latter studies highlight the possible problem of loop instability when retuning based upon the diagnoses provided by data-based measures. © 2014 Elsevier Ltd.

Oscillation is a phenomenon very commonly observed in systems, ranging from simple ones to complex distributed network. Several techniques have been proposed in the literature for detecting oscillations to study their importance in domains ranging from physiology to climate studies. However, there is a lack of a common framework accommodative of important features of data such as non-stationarity, intermittent oscillations, measurement noise, multimodal oscillations, and the like. In this article, we outline a framework that addresses these challenges, the results of which can then be analyzed along with appropriate knowledge about the underlying system. We present results of an extensive simulation study that establishes the robustness and reliability of the proposed technique and demonstrate its applicability to real datasets in climate and in industrial datasets.

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.

Control loop performance assessment (CLPA) techniques assume that the data being analyzed is generated during steady state operation with fixed plant dynamics and controller parameters. However, in industrial settings one often encounters environmental and feedstock variations which can induce significant changes in the plant dynamics. Availability of data sets corresponding to fixed configurations is therefore questionable in industrial scenarios, in which case it becomes imperative to extract the same from routine plant operating data. This article proposes a technique for segmenting multivariate control loop data into portions corresponding to fixed steady state operation of the system. The proposed technique exploits the fact that changes in the operating region of the system lead to changes in variance-covariance matrix of multivariate control loop data. The univariate interval halving technique is fused with Mahalanobis distance to develop a multivariate tool that accounts for interactions between variables. The resulting data segments can be used for reliable CLPA and/or for user defined benchmarking of control loops. A multivariate control loop performance index is also proposed that requires significantly less data as compared to one of the previously proposed techniques. The proposed technique requires only routine operating data from the plant, and is tested on benchmark systems in the literature with simulations. Experimental validation on a model predictive control system aimed at maintaining the temperature profile of a metal plate demonstrates applicability of the technique to industrial systems. The proposed technique acts as a tool for preprocessing data relevant to CLPA and can be applied to large scale interacting multivariate systems. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3311–3328, 2017.