Process Systems Engineering
Research in the area of process systems engineering at Texas A&M University at Qatar primarily focuses on optimizing the design and enhancing the operation of various chemical and environmental processes. On the process design side, most efforts are directed towards optimizing the designs of various energy and environment related processes. On the process operation side, however, research focuses on developing new modeling, control, estimation, and monitoring techniques and algorithms aiming to improve the operation of chemical, biological, and environmental systems.
Improved Nonlinear Modeling using Multiscale Estimation
Many chemical processes, can be represented by nonlinear models, which are used in many operations, such as process control. In the absence of fundamental models, it is usually relied on deriving empirical models from measurement of the process variables. Unfortunately, measured data are usually contaminated with errors that can largely degrade the accuracy of estimated models. Also, most real data are multiscale in nature, which means that they contain features and noise with varying contributions over time and frequency. Modeling such data using conventional single-scale modeling techniques usually does not provide accurate models because they do not account for this multiscale nature of the data.
Wavelet-based multiscale representation has been shown to be a powerful data filtering tool. Therefore, the main objective of this work is to utilize multiscale representation of data to improve the estimation and prediction accuracy of various classes of nonlinear models, which include fuzzy models, linear-in-the-parameter nonlinear models, adsorption isotherms, and others. For these model structures, multiscale techniques that integrate model estimation and data filtering will be developed to enhance the robustness of these models to measurement noise and to account for the multiscale nature of the data.
The research method for this project will start by investigating the effect of measurement noise on the accuracy and prediction of these nonlinear models. Then, the effect of pre-filtering the input and output data on the accuracy of estimated models will be assessed. Then, multiscale nonlinear modeling methods that integrate modeling and feature extraction will be developed for improved estimation accuracy. Finally, the developed multiscale modeling techniques will be applied on practical process data.
Lead Principal Investigator / Coordinator
Nounou, Mohamed
Professor
Professor
Principal Investigators
Hazem Nounou
Texas A&M University at Qatar
Texas A&M University at Qatar
Enhanced Inferential Modeling of Distillation Compositions using Multiscale Latent Variable Regression
The control of distillation column composition usually requires the availability of inferential models that can accurately predict the composition from easily measured process variables, such as temperature and pressure. Unfortunately, measurements are usually contaminated with errors that degrade the accuracy of the inferential models. Therefore, such errors need to be filtered for improved model's prediction. Wavelet-based multiscale representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this project, multiscale representation will be utilized to improve the prediction accuracy of some of the latent variable regression models used in inferential modeling of distillation column compositions, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression modeling algorithm. The developed algorithm will combine the advantages of latent variable regression models with those of multiscale representation of data. Also, most latent variable regression modeling methods assume a linear input-output relationship. Therefore, another objective of this project is to extend the results to nonlinear processes using fuzzy models, which have been successfully used in nonlinear system identification and control. The advantages of the developed algorithms will be demonstrated and compared to those of the conventional methods using industrial distillation column data.
Lead Principal Investigator / Coordinator
Nounou, Mohamed
Professor
Professor
Principal Investigators
Hazem Nounou
Texas A&M University at Qatar
Texas A&M University at Qatar
Model-free, Data-based Design of Adaptive Control Systems
This project deals with the design of adaptive control systems taking into account the practical constraints of real world engineering systems. These can be summarized as the following: 1) the lack of analytical models for complex processes which occur for instance in process control and the petrochemical industries 2) the need for simple controllers such as the typical Proportional Integral Derivative (PID) controller which can be deployed at various points in a multivariable process 3) the desirability of designing controller tuning strategies (adaptation) that are based on measured data and can provide guarantees of stability and performance, as the process set points change and 4) effective techniques of dealing with and overcoming the adverse effects on performance, of system time-delays.
In the project, we will first describe some recent results on data-based design of fixed (non-adaptive) PID controllers which would then serve as a launching point of our investigation into design of adaptive data based PID and more general controllers, for systems with time delays. The goal of the project would be to research a new generation of fixed order data based adaptive controllers which are well suited to address the realistic concerns of the process industry.
Lead Principal Investigator / Coordinator
Hazem Nounou
Associate Professor
Associate Professor
Texas A&M University at Qatar
Principal Investigators
Shankar Bhattacharyya
Texas A&M University
Texas A&M University
Training Program in Genomic Signal Processing
The long term goal of the lead PI's research program is to use engineering methods to increase the low success rate of current cancer therapies. Specifically, the PIs will seek to use and adapt ideas from Control Theory to develop improved methods for treating cancer. Supported primarily by a number of NSF grants, the P.I.s has made significant strides in this direction during the last several years. However, several open challenges remain and the main goal of the proposed project is to take on some of these challenges and develop additional techniques that facilitate actual experimental validation of the theoretically predicted improvements in cancer therapy. The aim of the proposed project will be to continue to make cutting edge advances in the application of engineering principles to cancer diagnosis and therapy while simultaneously training the Qatar-based PIs to emerge in this fascinating research area. The team does have a proven track record of accomplishing similar tasks since the lead PI himself underwent similar training under the guidance of co-PI Edward Dougherty before emerging as a leading researcher in this area.
Lead Principal Investigator / Coordinator
Aniruddha Datta
Professor
Professor
Texas A&M University
Principal Investigators
Hazem Nounou
Texas A&M University at Qatar
Texas A&M University at Qatar
Advanced Production Process for Hazardous Waste Treatment
Our work concerns the hazardous chlorinated organics found in industrial waste discharges that are a potential threat to the environment. It is well known that a chemical reduction/oxidation process can convert chemically hazardous contaminants to non-hazardous or less toxic compounds that are more stable, less mobile, and/or inert. We, however, introduce a new class of a treatment process – named the ARP or Advanced Reduction Process - which is a combination of chemical reduction and activation. It is our contention that this approach will lead to the production of highly reactive reducing free radicals for rapid and effective reductive dechlorination and that the ARP will be cost-effective and competitive. We are thus setting up and developing the appropriate analytical and experimental procedures. We address a task specific to Qatar; we screen potential ARPs and identify those that are most effective in degrading target compounds detected at Qatar industrial sites (most probably 1, 2-dichloroethane and vinyl chloride). We also look at the fundamental data on the stoichiometry and kinetics of contaminant degradation in general and hence suggest the optimal conditions for applying promising ARPs.
Lead Principal Investigator / Coordinator
Abdel-Wahab, Ahmed
Professor
Professor
Principal Investigators
.JPG)
Linke, Patrick
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Mohsin Al-Ansi
Qatar University
Qatar University
Techno Economic Assessment of Seawater Dechlorination and Impact of Dechlorination on Marine Environment
Dechlorination process is carried out to ensure that the free residual chlorine levels in the outfalls meet the Ministry of Environment standards. The objectives of this study are: 1) Conduct detailed toxicity analysis on Marine species found in Qatar Waters, 2) Conduct Techno-economic comparative study of dechlorination chemicals alternatives, 3) Perform hydrodynamic transport modeling of dechlorination chemicals discharge in seawater, and 4) Identify the most cost-effective and environmentally benign dechlorination chemical.
Lead Principal Investigator / Coordinator
Abdel-Wahab, Ahmed
Professor
Professor
Principal Investigators
Linke, Patrick
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
A Systems approach to the development of Sustainable water strategies for Qatar
This research focuses on developing a framework for macroscopic water integration in complex industrial regions. The framework will offer decision-support to stakeholders to identify sustainable ways of maximizing water resource conservation and pollution prevention. In particular, this research will focus on developing an optimization-based framework to aid multi-level and multi-stakeholder decision-making in water systems design, operations, capacity expansion planning and retrofit in order to support short, medium and long term design decisions.
Lead Principal Investigator / Coordinator
Abdel-Wahab, Ahmed
Professor
Professor
Principal Investigators
Linke, Patrick
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Mahmoud El-Halwagi
Texas A&M University
Texas A&M University
Removal of Mercury from Wastewater Using Reactive Absorbent/Membrane (RAM) Hybrid Filtration Process
Conventional adsorbents are widely used to remove heavy metals from water and wastewater, but it can be released when conditions change (e.g. pH, competing ions). The focus of this project is to employ reactive adsorbents that not only remove heavy metals, but react with them to produce stable forms that will not be released.
Lead Principal Investigator / Coordinator
Abdel-Wahab, Ahmed
Professor
Professor
Principal Investigators
Linke, Patrick
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Program Chair and professor of Chemical Engineering, holder of the Qatar Shell professorship /Executive Director of Graduate Studies
Bill Batchelor
Texas A&M University
Texas A&M University