Dr. M. A. A. Shoukat Choudhury
Department of chemical Engineering,
Bangladesh University of Engineering and Technology, Bangladesh.
Title: Detection and Diagnosis of Oscillations in Process Data - A Practical Approach
Abstract: Modern process plants, such as oil refineries, power plants, fertilizer factories and paper mills are complex integrated systems, containing thousands of measurements, hundreds of controllers and tens of recycle streams. The integration of energy and material flow results in the spread of fluctuations throughout a plant. The fluctuations force the plant to be operated further from the economic optimum that would otherwise be possible, and thus cause decreased efficiency, lost production and in some cases increased risk or safety hazards. Because of the scale of operation of such large process plants, a small percentage decrease in productivity has huge financial consequences. It can be extremely difficult to pinpoint the cause of these fluctuations. In many cases, these fluctuations appear in the form of oscillations. Such oscillations generally have no defined beginning and ending. Therefore, the cause of oscillations cannot be easily isolated. Finding the cause of oscillations is a tedious, labor-intensive, often a fruitless task. Once the cause is understood, removal of oscillations is usually straightforward. Therefore, it is important to detect and diagnose the causes of oscillations.
The increasing automation and implementation of digital control in chemical plants result in storage of large volume of data in process data historians. These data can serve as a huge information mine, if they are carefully analyzed and rightly interpreted. Data can be transformed into useful knowledge and valuable information, which can help the plant personnel operating the plant efficiently and profitably in an environmentally benign way. Recently, a huge attention has been drawn to utilize the methods of data science, mainly statistical and digital signal processing techniques, to monitor and control process plants and troubleshoot process problems. First, this study will present a brief review of the recent research work performed in the area of oscillation detection and diagnosis utilizing methods of data science. Then, a new method based on Fourier Series and Least Square Regression Analysis for detecting multiple oscillations at a time will be discussed. Finally, it will provide future directions to perform research in this area.
Prof. M. A. A. Shoukat Choudhury received his Bachelor and Master degree in Chemical Engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh. He obtained his PhD in process control from the University of Alberta, Canada. For his outstanding research performance, he has been honored with several awards namely University of Alberta PhD Dissertation Fellowship, Andrew Stewart Memorial Prize and ISA Educational Foundation Scholarship. He has published more than 50 papers in refereed journals and conference proceedings in the area of control valve stiction, control performance assessment, condition based monitoring, data compression and plantwide oscillations. He has an international patent on “Methods for Detection and Quantification of Control Valve Stiction”. He is the principal author of the book titled “Detection and Diagnosis of Process Nonlinearities and Valve Stiction – Data Driven Approaches” published in advanced industrial control series by Springer –Verlag, Germany in 2008. He applied his expertise extensively in industrial practice. His research contributions in control valve health monitoring have enjoyed wide applications in chemical, petrochemical, oil & gas, mineral processing, and pulp & paper industries throughout the world. He is currently working as a Professor in Department of Chemical Engineering, BUET. His main research interests include diagnosis of poor control performance, stiction in control valves, data compression, control loop performance assessment, condition based monitoring, troubleshooting plant wide oscillations, energy modeling, energy auditing and green house gas (GHG) inventory.