Distributed Parameter Systems/MicroParticle Systems 

Hollow Micro-Particle Production Process

Production of Hollow Micro-particles

 

Design and Operation of a Spray Dryer for the Manufacture of Hollow Micro-particles

Authors: Vikram S. Shabde and Karlene A. Hoo*
* Department of Chemical Engineering, Texas Tech University

ABSTRACT

Spray-drying technology is used in a wide variety of processes ranging from manufacture of food products to pharmaceuticals. Most recently, spray-drying technology has also been explored to make hollow micro-particles. This work presents an approach that designs a spray-drying chamber using a rate-based description combined with a droplet size distribution model. The major criterion in the design is the desired moisture content of the final particle. The prediction of the final particle properties are compared to experimental data obtained from a laboratory spray-drying unit. The results show that the final spray-drying design is sensitive to the liquid feed flowrate, the inlet drying gas temperature, and heat loss.

 

Publication Information: Compt. & Chem. Engng., 45, p 8329-8337, 2006

Corresponding Author: Karlene A. Hoo

 

System Identification and Model-based Control for Distributed Parameter Systems

Authors: Daguang Zheng and Karlene A. Hoo*
* Department of Chemical Engineering, Texas Tech University

 

ABSTRACT

A linear input/output model is identified for a nonlinear distributed parameter system using a combination of singular-value decomposition and Karhunen Loeve expansion. The model captures the dominant behavior of the system around a nominal operating point. A Quadratic Dynamic Model-Based Controller (QDMC) is designed based on this input/output model. Sufficient conditions for closed-loop stability of the infinite dimensional process and finite order controller are presented and discussed. The system identification method and a QDMC controller are applied to the multiple-input multiple-output hydro-dealkylation (HDA) of toluene process in the presence of common disturbances.

 

Publication Information: Compt. & Chem. Engng., 28, No, 8, p 1361-1375, 2004

Corresponding Author: Karlene A. Hoo

 

Finite Dimensional Modeling and Control of Distributed Parameter Systems

Authors: Daguang Zheng and Karlene A. Hoo*
* Department of Chemical Engineering, Texas Tech University

ABSTRACT

Accurate solutions of the DPS may be represented as the sum of an infinite series. Control design however, requires low-order models primarily due to implementation limitations. As such, developing low-order models of high fidelity is important if the objective is accurate control of the DPS. When an exact model (system of partial differential equations) of the system is known, this work presents a method to develop a low-order model that assures convergent and consistent projection to a finite space. The resulting low-order model can then be used to design finite dimensional controllers. When there is no available first-principle model of the system, this work introduces a novel system identification method, that combines the characteristics of singular value decomposition (SVD) and the Karhunen-Loeve (KL) expansion for DPS to arrive at a low-order model that captures the dominant characteristics of the system. Here as well, the final model form allows for the synthesis of finite order controllers. Two nonlinear reactor systems that can be described by systems of partial differential equations (PDEs) are provided to demonstrate the model identification methods. Feedback controllers are then synthesized based on these models to demonstrate their performance for disturbance rejection.

Publication Information: Compt. & Chem. Engng., 26, No, 7-8, p 1049-1076, 2002

Corresponding Author: Karlene A. Hoo

 

Low-order Control Relevant Reduction of Distributed Parameter Systems

Authors: Daguang Zheng and Karlene A. Hoo*
* Department of Chemical Engineering, Texas Tech University

ABSTRACT

Accurate solutions of distributed parameter systems may be represented as the sum of an infinite series. Control however, requires low order models primarily for implementation. As such, developing low order models of high fidelity is important in the control of true distributed parameter systems . This work addresses this issue by employing and comparing methods that arrive at low order models either from input-output data or from exact descriptions of the process. Using these approximate low order models, linear and nonlinear feedback controllers are synthesized to address disturbance compensation and model parameter uncertainty. Two candidate processes are introduced and used to demonstrate these concepts.

Publication Information: Chemical Engineering Science, 56, pp 6683--6710, 2001

Corresponding Author: Karlene A. Hoo

 

Low-order Model Identification of Distributed Parameter Systems By a Combination of Singular Value Decomposition and the Karhunen-Loève Expansion

Authors: Daguang Zheng, Karlene A. Hoo* and Michael J. Piovoso;
* Department of Chemical Engineering, Texas Tech University
 Dupont Chemical Company, Wilmington, DE

ABSTRACT

In this work, a new system identification method, that combines the characteristics of singular value decomposition (SVD) and the Karhunen-Loève (KL) expansion for distributed parameter systems, is presented. This method is then demonstrated on two nonlinear reactor systems that can be described by systems of partial differential equations (PDEs). The results indicate that this new method provides satisfactory low-order models when compared to models developed using either the SVD or KL methods. In particular, it has the advantage of not requiring an exact PDE model, which is necessary for the KL solution and it captures the dynamics of the process in contrast to the SVD solution. This has important implications especially for applications such as control that require low-order models for implementable solutions.

Publication Information: Industrial & Engineering Chemistry Research, 41(6), pp 1545 -1556, 2002

Corresponding Author: Karlene A. Hoo

 

Wavelet-based Model Reduction of Distributed Parameter Systems

Authors: Nagabhushan Mahadevan and Karlene A. Hoo*
Department of Chemical Engineering, University of South Carolina
* Department of Chemical Engineering, Texas Tech University

ABSTRACT

Mathematical models that describe distributed parameter systems are composed of systems of partial differential and algebraic equations. The methods that solve these systems usually yield a high-order (infinite-dimensional) solution. However, for controller synthesis and practical considerations, a low-order model is preferred. This work addresses the development of model reduction through the use of multi-resolution methods that not only yield a finite low-order model but also a representation of the system's multiscale and local behavior such that scale-specific compensation can be realized. Two systems - heat transfer along a flat metal plate, and a packed bed reactor with axial dispersion are used to demonstrate the proposed approach.

Publication Information: Chemical Engineering Science, Vol. 55, pp 4271-5290, 2000.

Corresponding Author: Karlene A. Hoo

 

Finite Dimensional Modeling and Control of Distributed Parameter Systems

Authors: Daguang Zheng, Karlene A. Hoo* and Michael J. Piovoso;
* Department of Chemical Engineering, Texas Tech University
; School of Graduate Professional Studies, Penn State University, Malvern, PA 19355

ABSTRACT

Developing low-order models of high fidelity is important if the objective is accurate control of the DPS. This work presents a novel method to develop a low-order models when there is no available exact model of the system. The foundations for this method, SVD-KL, are singular value decomposition (SVD) theory and the Karhunen-Loève (KL) expansion. It is shown that satisfactory closed-loop performance of the nonlinear DPS can be obtained using a Dynamic Matrix Controller designed using the finite order model.

Publication Information: Proc. American Automatic Control Conference, paper FM12-2, Anchorage, AK, May 2002.

Corresponding Author: Karlene A. Hoo

 

Model Reduction & Controller Development for a Class of Distributed Parameter Systems

Authors: Nagabhushan Mahadevan;, Kuzman Adzievski; and Karlene A. Hoo*
* Department of Chemical Engineering, Texas Tech University
 ; Department of Chemical Engineering, University of South Carolina
; Department of Mathematics & Computer Science, South Carolina State University

ABSTRACT

Mathematical models that describe distributed parameter systems are composed of systems of partial differential and algebraic equations (PDAEs). The solution of these systems are usually a high order (infinite dimensional) model. For controller synthesis and due to practical considerations, a reduced-order model (finite) is preferred. This work addresses the development of reduced-order, finite dimensional models by proposing to use multi-resolution methods that not only provide a control-relevant model but also yields a representation of the system's multi-scale and local behavior.

A simple system - heat transfer along a flat metal plate, is used to demonstrate the proposed solution and is compared to the solution obtained using singular functions approach solutions.

Publication Information: Proc. of 1999 American Automatic Control Conference, San Diego, CA

Corresponding Author: Karlene A. Hoo