Laya Das

Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance

A novel data-driven technique for performance assessment of multivariate control loops that takes into account the interactions within the system is proposed. The technique merges the Hurst-exponent-based single-input single-output controller performance index with Mahalanobis distance to devise a multiple-input multiple-output (MIMO) controller performance index. The distinct advantage over the standard minimum variance index and novelty of the proposed approach lies in its ability to quantify the performance of MIMO controller without the knowledge of interactor matrix or system description, which leads to the technique being insensitive to model plant mismatch and easily applicable to nonlinear systems. Only closed-loop routine operating data are required. This new methodology is tested on benchmark systems from the literature and simulation results are presented. Comparison with minimum variance index-based techniques reveals excellent agreement in the trends of both approaches. The results establish the proposed approach as a promising tool for interactor-matrix-independent MIMO control loop performance assessment.

Data driven approach for performance assessment of linear and nonlinear Kalman filters

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.

On-line performance monitoring of PEM fuel cell using a fast EIS approach

The Polymer Electrolyte Membrane Fuel Cell is a widely researched fuel cell, and a highly potential candidate for alternate power generation. However, technical issues such as membrane flooding and drying prevent its deployment in many applications. Electrochemical Impedance Spectroscopy (EIS) is a very powerful technique that is used to isolate flooding and drying of the fuel cell from operating data. Such information about the state of operation of the cell is critical to deciding necessary control actions to maintain the health and performance of the cell. However, the time taken in obtaining measurements in EIS can be large enough to allow the cell to flood or dry beyond irreparable damage, rendering it a mere postmortem technique. Moreover, after long durations of perturbation, the cell takes a considerable amount of time to return to its regular operation. A new technique is proposed that uses the concept of EIS, but is computationally faster and gives results comparable with those of traditional EIS. This technique is based on perturbing the cell with a small chirp signal containing large number of frequencies instead of series of small sinusoids at different frequencies. Simulation results on isolation of flooding and drying based on Fast EIS are illustrated and future work directions are indicated.

A novel approach for benchmarking and assessing the performance of state estimators

State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system.

Cognitive behavior based framework for operator learning: knowledge and capability assessment through eye tracking

Abstract 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.

Fault diagnosis Of anaerobic digester system using nonlinear state estimator: application to India's largest dairy unit

The complex biochemical processes along with uncertain load disturbances in anaerobic digesters (AD) lead to frequent upsets and poor performance of the overall system. Several techniques have been proposed in the literature to address fault detection and isolation in the anaerobic digester. However, these methods do not provide information about the internal states of the system and use expert and heuristics for fault isolation. In this work, we first developed a first principle based model of the AD unit at one of India’s largest dairy and validated using experimental results. Subsequently, we used this model to demonstrate the applicability of nonlinear state estimation approaches for fault diagnosis.

Fiber interrogator for Bragg grating sensors based on cavity ring-down technique

We report the results of simulations as well as experiments to study the performance of a fiber interrogator based on cavity ring-down principle.