Syed Imtiaz

Estimation of States of Nonlinear Systems using a Particle Filter

Particle filters can estimate the states of nonlinear and non-Gaussian systems without any approximation when the number of particles tends to infinity. However, the method is not popular in industry because the implementation details are missing in the literature. In this paper we discuss several implementation issues and propose novel techniques for tuning the particle filter and dealing with multi-rate data. The performance of the proposed methodologies are demonstrated using a simulated non-linear CSTR and an experimental four tank system.

A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis

Bayesian network (BN) is a powerful tool for human reliability analysis (HRA) as it can characterize the dependency among different human performance shaping factors (PSFs) and associated actions. It can also quantify the importance of different PSFs that may cause a human error. Data required to fully quantify BN for HRA in offshore emergency situations are not readily available. For many situations, there is little or no appropriate data. This presents significant challenges to assign the prior and conditional probabilities that are required by the BN approach. To handle the data scarcity problem, this paper presents a data collection methodology using a virtual environment for a simplified BN model of offshore emergency evacuation. A two-level, three-factor experiment is used to collect human performance data under different mustering conditions. Collected data are integrated in the BN model and results are compared with a previous study. The work demonstrates that the BN model can assess the human failure likelihood effectively. Besides, the BN model provides the opportunities to incorporate new evidence and handle complex interactions among PSFs and associated actions.

Development of Economic Consequence Methodology for Process Risk Analysis

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies.

Dynamic risk assessment and fault detection using a multivariate technique

In the context of process safety, significant improvements are needed in fault detection methods, especially, in the areas of early detection and warning. In this article, a multivariate risk‐based fault detection and diagnosis technique is proposed. The key elements of this technique are to eliminate faults that are not serious and to provide a dynamic process risk indication at each sampling instant. A multivariable residual generation process based on the Kalman filter has been combined with a risk assessment procedure. The use of the Kalman filter makes the method more robust to false alarms, which is an important aspect of any fault detection algorithm that targets the safety of a process. In addition, we consider significant differences in the severity of the faults associated with different process variables. We also take into account the varying intensity of damage caused by the increasing and decreasing rates of fault and the need to treat those cases differently.

Human Factor Risk Assessment During Emergency Condition in Harsh Environment

This paper presents a quantitative approach to human factors risk analysis during emergency conditions on an offshore petroleum facility located in a harsh environment. Due to the lack of human factors data for emergency conditions, most of the available human factors risk assessment methodologies are based on expert judgment techniques. Expert judgment is a valuable technique, however, it suffers from vagueness, subjectivity and incompleteness due to a lack of supporting empirical evidence. These weaknesses are often not accounted for in conventional human factors risk assessment. The available approaches also suffer from the unrealistic assumption of independence of the human performance shaping (HPS) factors and actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments and to account for the dependency among the HPS factors and actions. These outcomes are achieved by integrating Bayesian Networks with Fuzzy and Evidence theories to estimate human error probabilities during different phases of an emergency. To test the applicability of the approach, results are compared with an analytical approach. The study demonstrates that the proposed approach is effective in assessing human error probability, which in turn improves reliability and auditability of human factors risk assessment.

A Hybrid Method for Process Fault Detection and Diagnosis

Abstract For process fault detection and diagnosis, a real time hybrid method based on Principle component analysis (PCA) and Bayesian belief network (BBN) is described. Upon successful identification of fault from PCA residual plot and Q statistics, information from the PCA contribution of each variable is passed to the BBN for root cause analysis. Pearls message passing algorithm is used for belief updating. Early detection of fault, makes the methodology more reliable and robust during the process fault occurrence. The aim of this monitoring tool is to incorporate prior process knowledge along with the present observed evidence to come up with most plausible explanation of how the process is behaving. The effectiveness of the proposed method is demonstrated for a Dissolution tank model for different simulated scenarios by detecting and diagnosing the fault accurately.

Nonlinear model predictive control of managed pressure drilling

A new design of nonlinear model predictive controller (NMPC) is proposed for managed pressure drilling (MPD) system. The NMPC is based on output feedback control architecture and employs offset-free formulation proposed in [1]. NMPC uses active set method for computing control inputs. The controller implements an automatic switching from constant bottom hole pressure (CBHP) regulation to flow control mode in the event of a reservoir kick. In the flow control mode the controller automatically raises the bottom hole pressure setpoint, and thereby keeps the reservoir fluid flow to the surface within a tunable threshold. This is achieved by exploiting constraint handling capability of NMPC. In addition to kick mitigation the controller demonstrated good performance in containing the bottom hole pressure (BHP) during the pipe connection sequence. The controller also delivered satisfactory performance in the presence of measurement noise and uncertainty in the system.

Robust control of managed pressure drilling

Managed pressure drilling technology is gaining in popularity because of the necessity to mitigate drilling risks while drilling off-shore and also to make it possible to drill in challenging reservoirs. In this paper we have investigated the contribution of various drilling parameters to the variations in simple first order model of drilling. We also present a robust SISO controller for a particular variant of managed pressure drilling in which a choke valve is manipulated to achieve a bottom hole pressure set point. The presented controller can tolerate significant plant model mismatches, can function well under different well conditions and can also handle noisy measurements. A strategy for practical implementation of this controller is also proposed.

Identification of integrating processes with time delay

Abstract A set of methods for identification of continuous-time transfer function models for integrating processes with time delay is proposed. The step, piecewise constant and piecewise linear inputs are considered which indeed cover most of the input signals commonly used in industries. For all of the three types of input signals, estimation equations to simultaneously obtain model parameters and the time delay are derived. The final parameter estimation equations are in a form suitable for the least-squares solution. Mathematical formulation of the methods is presented using the example of an integrating process with a first order lag dynamics and a zero which can be extended for other structures. An instrumental variable method to deal with the bias issue in least-squares solutions is used. Simulation results are presented to demonstrate the efficacy of the algorithms and their relative performance.

A MPC based fault tolerant control strategy for actuator fault

A Model Predictive Control (MPC) based fault tolerant control application has been described. The controller was applied to control the concentration and level of a solid crystal dissolution tank. The system was frequently suffering process upset due to difficulty in solid discharge. The controller used an error calculated from an intermittent signal as a feedforward to the controller. The intermittent signal posed challenges in model development and also for control. An iterative technique was used to develop the model. The developed controller successfully eliminated the operational problem.