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Based on a closed-loop step response test, a model identification method is proposed for integrating processes with time delay in this paper. By introducing a damping factor to the closed-loop step response for realization of the Laplace... more
Based on a closed-loop step response test, a model identification method is proposed for integrating processes with time delay in this paper. By introducing a damping factor to the closed-loop step response for realization of the Laplace transform, a frequency response estimation algorithm is developed in terms of the closed-loop control structure used for identification. Correspondingly, two model identification algorithms
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Human history has been defined in terms of materials categories: the Stone Age, the Bronze Age, and the Iron Age. It is well accepted that we are now living in a polymer age. Since the 20th century, polymer materials, including plastics,... more
Human history has been defined in terms of materials categories: the Stone Age, the Bronze Age, and the Iron Age. It is well accepted that we are now living in a polymer age. Since the 20th century, polymer materials, including plastics, fibers, elastomers, and proteins, have gradually appeared in almost every area of people’s everyday life, and there are a variety of applications in agriculture, industry, and even the defense industry. In all of the polymer materials, plastic is a major class.
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Research Interests: Mechanical Engineering, Mathematics, Applied Mathematics, Computer Science, Parameter estimation, and 10 moreModel Identification, Identification, Noise, Regression Analysis, Instrumental Variable, Linear Regression, Transient Response, Process Model, Initial Condition, and Electrical And Electronic Engineering
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ABSTRACT A control scheme is proposed for integrator processes with dominant time delay. It contains a local proportional feedback to prestabilize the process, a proportional controller for set-point tracking, and a... more
ABSTRACT A control scheme is proposed for integrator processes with dominant time delay. It contains a local proportional feedback to prestabilize the process, a proportional controller for set-point tracking, and a proportional−derivative controller for load disturbance rejection. The control allows a decoupled design of the load and set-point tracking responses. The tuning of the scheme is simple. Simulations show that the proposed scheme has fast set-point tracking and efficient load disturbance rejection.
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ABSTRACT For dynamic batch process monitoring, a two-dimensional dynamic modeling framework has recently been formulated, which is based on a two-dimensional autoregressive model and the principal component analysis (PCA) method.... more
ABSTRACT For dynamic batch process monitoring, a two-dimensional dynamic modeling framework has recently been formulated, which is based on a two-dimensional autoregressive model and the principal component analysis (PCA) method. Different from traditional dynamic batch process monitoring, the two-dimensional method can monitor both within batch and batch-to-batch dynamic information of the process data. However, this PCA-related method has two main restrictions, which may render poor monitoring performance in practice. First, it is under the assumption that the distribution of the process data is Gaussian. Second, the correlations between different process variables are assumed to be linear with each other. Unfortunately, both of these two assumptions are difficult to satisfy in batch processes. In this paper, support vector data description (SVDD) is incorporated into the two-dimensional modeling framework, which has no Gaussian limitation of the data, and can also model the nonlinear relationship between process variables. For dynamic batch process monitoring, a distance based statistic is proposed. Based on results of a simulation case study, the monitoring performance has been improved.
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Injection velocity during filling stage was experimentally controlled using different control algorithms, from simple open-loop control and Proportional-Integral (PI) closed-loop control, to more advanced strategies such as Self-Tuning... more
Injection velocity during filling stage was experimentally controlled using different control algorithms, from simple open-loop control and Proportional-Integral (PI) closed-loop control, to more advanced strategies such as Self-Tuning Regulator (STR), fuzzy control and Generalized Predictive Control (GPC). It is shown that the advanced control can effectively overcome the nonlinear and time-varying characteristics of the filling velocity. The advantages and drawbacks of each strategy are presented and experimentally illustrated. The use of advanced control strategies is shown to be necessary for accurate control of the injection velocity with good repeatability.
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ABSTRACT A batch process can be treated as a 2-dimentional (2D) system with a time dimension within each batch and a batch dimension from batch to batch. This paper integrates the learning ability of iterative learning control (ILC) into... more
ABSTRACT A batch process can be treated as a 2-dimentional (2D) system with a time dimension within each batch and a batch dimension from batch to batch. This paper integrates the learning ability of iterative learning control (ILC) into the prediction model of model predictive control (MPC). Based on this integrated model, a 2D dynamic matrix control (2D-DMC) algorithm with a feedback control and an optimal feed-forward control is proposed. The sufficient conditions for exponentially asymptotic and monotonic convergence of the proposed 2D-DMC are established with proof under certain assumptions, in the presence of not only the completely repeatable uncertainties but also the non-repeatable interval uncertainties. The effectiveness of the proposed control scheme is tested through simulation and experimental implementation in the context of injection molding, a typical batch process. The results show that the batch process control performance is significantly improved.
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Abstract This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer's rule solution to linear... more
Abstract This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer's rule solution to linear equations which provides a balance between model complexity and control system design, and then the derived MISO process based extended state space predictive functional control is presented. The overall design of the controller enables the controller to consider both the process state dynamics and the output dynamics, thus improved control performance for tracking set-points and disturbance rejection is resulted. The proposed controller is tested on both model match and model mismatch cases to demonstrate its superiority. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided to give further insight into the proposed method.
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The surface dislocation model is used to obtain a method of evaluating the non-local stresses of screw dislocation in a bi-medium. The exact solutions of non-local stresses of screw dislocation are given for two particular cases. All... more
The surface dislocation model is used to obtain a method of evaluating the non-local stresses of screw dislocation in a bi-medium. The exact solutions of non-local stresses of screw dislocation are given for two particular cases. All classical singularities are eliminated. Estimates are provided for the critical shear which will produce a single dislocation and for the maximum force on the screw dislocation due to the existing interface.
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... Department of Chemical and Biomolecular Engineering, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong. Ind. Eng. ... Acknowledgment. This work is supported in part by Hong Kong Research... more
... Department of Chemical and Biomolecular Engineering, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong. Ind. Eng. ... Acknowledgment. This work is supported in part by Hong Kong Research Grant Council under project number 613107. ...
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ABSTRACT According to the literature statistics, only less than 10% of reported iterative learning control (ILC) methods have been devoted to the indirect approach. Motivated by the full potential of research opportunities in this field,... more
ABSTRACT According to the literature statistics, only less than 10% of reported iterative learning control (ILC) methods have been devoted to the indirect approach. Motivated by the full potential of research opportunities in this field, a number of studies on indirect ILC were proposed recently, where ILC-based P-type control and learning-type model predictive control (L-MPC) are two successful stories. All indirect ILC algorithms consist of two loops: an ILC in the outer loop and a local controller in the inner loop. The local controllers are, respectively, a P-type controller in the ILC-based P-type control and a model predictive control (MPC) in the L-MPC. Logically, this leads to the question of what type of ILC should be chosen respectively for the two above-mentioned indirect ILC methods. In this study, P-type ILC and anticipatory P-type (A-P-type) ILC are studied and compared, because they are typical and widely implemented. Based on mathematical analysis and simulation test, it has been proved that the A-P-type ILC should be used in the ILC-based P-type control and while the P-type ILC should be used in the L-MPC. Furthermore, an improved L-MPC with batch-varying learning gain was proposed to handle the trade-off between convergence rate and robustness performance. The simulation results on injection molding process and a nonlinear batch process validated the feasibility and effectiveness of the proposed algorithm.
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ABSTRACT Identification and control of a nonlinear process in the presence of unmeasured load disturbances is important, because most chemical processes are perturbed by load disturbances that are often not measured. In this paper, the... more
ABSTRACT Identification and control of a nonlinear process in the presence of unmeasured load disturbances is important, because most chemical processes are perturbed by load disturbances that are often not measured. In this paper, the absorption principle is first extended to develop an effective identification strategy for a feedforward neural network representation of the process input−output relation in the presence of an unmeasured load disturbance. This developed model can provide an accurate output prediction, irrespective of the load disturbances, as long as the disturbances can be reasonably approximated by piecewise polynomials. Second, a predictive control scheme is developed on the basis of genetic algorithm optimization, using the above-identified model, for the nonlinear process under the influence of unmeasured loads. Finally, simulations are provided to illustrate the effectiveness of the proposed identification and control scheme.
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ABSTRACT Because of expensive cost or large time delay, quality data are difficult to obtain in many batch processes, while the ordinary process variables are measured online and recorded frequently. This paper intends to build a... more
ABSTRACT Because of expensive cost or large time delay, quality data are difficult to obtain in many batch processes, while the ordinary process variables are measured online and recorded frequently. This paper intends to build a statistical quality prediction model for batch processes under limited quality data. Particularly, the self-training strategy is introduced and combined with the partial least-squares regression model. For multiphase batch processes, a phase-based self-training PLS model is developed for quality prediction in each phase of the process. The feasibility and effectiveness of the developed method is evaluated by an industrial injection molding process.
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ABSTRACT Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different... more
ABSTRACT Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.
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Abstract: A simultaneous heat and mass transfer model of the dielectric material–assisted microwave freeze drying was derived in this study considering the vapor sublimation-desublimation in the frozen region. The mathematical model was... more
Abstract: A simultaneous heat and mass transfer model of the dielectric material–assisted microwave freeze drying was derived in this study considering the vapor sublimation-desublimation in the frozen region. The mathematical model was solved numerically by using the finite-difference technique with two moving boundaries. Silicon carbide (SiC) was selected as the dielectric material, and the skim milk was used as the representative solid material in the aqueous solution to be freeze-dried. The results show that the dielectric material can significantly enhance the microwave freeze drying process. The drying time is greatly reduced compared to cases without the aid of the dielectric material. Profiles of the temperature, ice saturation, vapor concentration, and pressure during freeze drying were presented. Mechanisms of the heat and mass transfer inside the material sphere were analyzed. For an initially unsaturated frozen sample of 16 mm in diameter with a 4-mm-diameter dielectric material core, the drying time is 288.2 min, much shorter than 380.1 min of ordinary microwave freeze drying and 455.0 min of conventional vacuum freeze drying, respectively, under typical operating conditions.
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In the present work, a multiphase calibration modeling and statistical analysis strategy is developed for the improvement of process understanding and quality prediction. Having realized the phase-wise local and cumulative effects on... more
In the present work, a multiphase calibration modeling and statistical analysis strategy is developed for the improvement of process understanding and quality prediction. Having realized the phase-wise local and cumulative effects on quality interpretation and prediction, the major task lies in how to qualify and quantify them among multiple phases. The proposed scheme is presented on two different levels: On the first level, phase-specific variable selection and O2-PLS are designed focusing on revealing the local effects of individual phases on quality variations, e.g. within the current phase, which part of process variations are responsible for quality variations and which quality variations are dominated. Moreover, bootstrapping technique is employed during the procedure of variable selection and O2-PLS, which can enhance the reliability and robustness of calibration analysis. On the second level, conventional PLS is used to model the quantitative relationship between multiple phases and the final qualities so that the cumulative effects on quality variations are apprehended by additively stacking the local contributions of various phases. The proposed strategy highlights such an idea that in real multiphase processes, each phase may only explain one part of quality variations and the final qualities can only be additively and jointly defined by multiple phases. A benchmark simulation of fed-batch penicillin fermentation production is considered and put into illustration, which demonstrates the efficiency of the proposed algorithm for better process understanding and quality interpretation in multiphase processes.
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ABSTRACT In the present work, multiple data spaces, in which the same variables are measured on different sources of objects, are related with each other by a two-step analysis strategy, which focuses on finding their common structure in... more
ABSTRACT In the present work, multiple data spaces, in which the same variables are measured on different sources of objects, are related with each other by a two-step analysis strategy, which focuses on finding their common structure in variable correlations. Common basis vectors, which are closely related with each other over sets, are extracted and deemed to enclose the cross-set similar correlations. Therefore, two different subspaces are separated from each other in each dataset. One is the common subspace driven by the common bases, in which, variable correlations are deemed to be consistent over sets; and the residual is the specific subspace, in which, variable correlations are unique to each definite data table. This is achieved by solving a mathematical optimization problem, in which, theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with the laboratory experiment data from the literatures. The proposed approach provides an insight into the inherent variable correlations of multiple-set data with further application potential.
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ABSTRACT Based on the multivariable description of processes in industry, a non-zero–pole cancellation decoupling strategy is first proposed and then used for state space predictive functional control (PFC) design. The proposed decoupling... more
ABSTRACT Based on the multivariable description of processes in industry, a non-zero–pole cancellation decoupling strategy is first proposed and then used for state space predictive functional control (PFC) design. The proposed decoupling guarantees realization and enables the control system design to be based on single-input single-output (SISO) process formulations. To facilitate the closed-loop control performance improvement, the subsequent controller design adopts an extended non-minimal structure that can regulate the process state dynamics, which provides more degrees compared with traditional state space methods. By interpretations of the proposed performance through process transfer function formulations, relationship with and superiority to traditional state space PFC are further revealed. Finally, the effectiveness and merits of the proposed are illustrated by application to a typical industrial chamber pressure process, in comparison with a typical non-minimal state space PFC method recently developed.
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Research Interests: Mechanical Engineering, Chemical Engineering, Design, Modeling, Stability, and 14 moreAlgorithm, Optimization, Safety, Conception, Model Identification, Laplace Transform, First-Order Logic, Closed Loop Control, Second Order, Chemical Engineering Science, Process Model, Frequency Response, Frequency Domain, and Higher order
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... SHAO Cheng,GAO Fu-Rong,YANG Yi. Research Center of Information and control,Dalian University of Technology,Dalian;Department of Chemical Engineering,Hong Kong University of Science and Technology,Hong Kong. Received 2001-5-22 Revised... more
... SHAO Cheng,GAO Fu-Rong,YANG Yi. Research Center of Information and control,Dalian University of Technology,Dalian;Department of Chemical Engineering,Hong Kong University of Science and Technology,Hong Kong. Received 2001-5-22 Revised Online Accepted. ...
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Owing to the natures of batch processes, such as high nonlinearity, time-varying, and limited batch time duration, their control remains as a challenge to modern industries. This paper takes a typical batch process, injection molding, as... more
Owing to the natures of batch processes, such as high nonlinearity, time-varying, and limited batch time duration, their control remains as a challenge to modern industries. This paper takes a typical batch process, injection molding, as an example to present a set of control schemes for batch processes. Advanced control algorithms such as adaptive control and model predictive control have
Research Interests: Model Predictive Control, Iterative Methods, Process Control, Adaptive Control, Iterative Learning Control, and 11 moreLearning Model, Injection Molding, Predictive models, Learning Control, Chemical Industry, Injection moulding, Batch Process, D, Predictive Control, Time varying, and Control Algorithm
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ABSTRACT