Key Laboratory of Advanced Control and Optimization for Chemical Processes

Shanghai, China

Key Laboratory of Advanced Control and Optimization for Chemical Processes

Shanghai, China
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Yang W.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Chen G.,City University of Hong Kong | Wang X.,Shanghai JiaoTong University | Shi L.,Hong Kong University of Science and Technology
Automatica | Year: 2014

We consider distributed state estimation over a resource-limited wireless sensor network. A stochastic sensor activation scheme is introduced to reduce the sensor energy consumption in communications, under which each sensor is activated with a certain probability. When the sensor is activated, it observes the target state and exchanges its estimate of the target state with its neighbors; otherwise, it only receives the estimates from its neighbors. An optimal estimator is designed for each sensor by minimizing its mean-squared estimation error. An upper and a lower bound of the limiting estimation error covariance are obtained. A method of selecting the consensus gain and a lower bound of the activating probability is also provided.


Yang W.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Wang X.,Shanghai JiaoTong University | Shi H.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
Systems and Control Letters | Year: 2013

This paper considers the problem of finding the optimal network topology and consensus gain for the fastest second-order consensus with time delay. By using the root locus method in the frequency domain, the problem can be decomposed into two convex optimization problems. In the case that the network topology is fixed, a multi-hop relay scheme is introduced for fast consensus seeking. Each agent can receive information from its multi-hop neighbors with a certain delay. The optimal number of hops for the fastest convergence speed can be derived from the largest generalized eigenvalue of a pair of extension matrices. Finally, some examples are supplied to verify the theoretical results. © 2012 Elsevier B.V. All rights reserved.


Yang W.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Wang X.,Shanghai JiaoTong University
Chinese Control Conference, CCC | Year: 2012

In this work, we consider the controlled discrete-time consensus problem in multi-agent systems, where a subset of agents take on leader roles while the remaining agents perform general consensus protocols with control inputs provided by the leaders. First, we derive necessary and sufficient conditions for the controllability under fixed and switched leaders, respectively. Further, we consider the problem of selecting a subset of followers to connect with leader for yielding fastest converging controlled consensus which can be cast as a convex optimization problem. © 2012 Chinese Assoc of Automati.


Xu Y.,Shanghai Maritime University | Xu Y.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Zhang Y.,Shanghai Maritime University | Wang J.,Shanghai JiaoTong University | Yuan J.,Shanghai JiaoTong University
Computers and Chemical Engineering | Year: 2013

Selective catalytic reduction (SCR) with ammonia or urea is regarded as one of the most important technologies to reduce the NOx emissions from coal-fired power plants. However, the design and development of SCR-DeNOx systems are a complicated process involving the optimization of several parameters such as the ammonia/urea injection strategy, the installment of the gate leaf and the hybrid grid, as well as the thickness of straightener. These parameters determine the velocity and concentration distributions at the entrance of catalyst layers, which are key factors to affect the efficiency of flue gas denitrification and ammonia slip. In this work, CFD simulations are carried out to portray the performance of the SCR-DeNOx facility in a 300MW coal-fired power plant. The influences of the gate leaf, hybrid grid and straightener on the distributions of the velocity and concentration are investigated. And then the corresponding experiments are performed to qualitatively confirm the simulation results. © 2012 Elsevier Ltd.


Yang W.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Shi H.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
International Journal of Control, Automation and Systems | Year: 2012

Motivated by navigation and tracking applications within sensor networks, we consider the distributed estimation problem over wireless sensor network. We propose a consensus based Kalman filtering algorithm based on optimal Linear Quadratic Gaussian control, in which each sensor can observe the dynamical system state, process the information data individually and communicate with each other within a sensing range. We provide a sufficient condition for the convergence of the proposed algorithm, and also give an upper bound for the estimation error covariance. Further, we find an optimal consensus gain for minimizing the network estimation error. Considering the occasional sensor fault and limited sensor energy, we investigate the proposed algorithm using only a subset of sensors to observe the dynamical system. With the assistance of the simulations, we verify the effectiveness of the proposed algorithms and present some interesting examples. © 2012 Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg.


Xie X.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Shi H.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
Industrial and Engineering Chemistry Research | Year: 2012

For multimode processes, it is inevitable to encounter disturbances, such as equipment aging, catalyst deactivation, sensor drifting, reaction kinetics drifting, or adding new operating modes. The existing monitoring algorithms are established either for coping with multimode feature under time-invariant circumstance or for handling the time-varying problem of processes with single operating mode. The purpose of this article is to develop an effective modeling and monitoring approach for complex processes with both multimode and time-varying properties. We propose a novel adaptive monitoring scheme based on Gaussian Mixture Model (GMM). The new method is able to model different operating modes as well as trace process variations. The effectiveness and efficiency of the new method are validated by a numerical example and the Tennessee Eastman (TE) simulation platform in different scenarios. © 2012 American Chemical Society.


Qian F.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Tao L.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Sun W.,East China University of Science and Technology | Du W.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
Industrial and Engineering Chemistry Research | Year: 2012

A novel kinetic model based on the free radical mechanism is used to simulate the oxidation of p-xylene (PX) in a continuous stirred-tank reactor (CSTR) under industrial operating conditions. Because this kinetic model cannot provide appropriate prediction of the influence of the reaction factors, such as catalyst concentrations, water concentrations, and temperatures, on the kinetic parameters for oxidation of PX in the laboratory semibatch reactor (SBR), the kinetic parameters that are highly nonlinear of the reaction factors are estimated by a back-propagation neural network (BPNN). Furthermore, correction coefficients are introduced to accurately evaluate the kinetic parameters based on Adaptive Immune Genetic Algorithm (AIGA) due to the significant difference between the nature of PX oxidation conducted in the laboratory SBR and in the industrial CSTR. The model with the evaluated optimum kinetic parameters is obtained, and its efficiency is validated via comparison with industrial data. © 2011 American Chemical Society.


Li S.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Huang D.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
Chinese Journal of Chemical Engineering | Year: 2011

In this work, an industrial acetic acid dehydration system via heterogeneous azeotropic distillation is simulated by Aspen Plus software. Residue curves are used to analyze the distillating behavior, and appropriate operating region of the system is determined. Based on steady states simulation, a sensitivity analysis is carried out to detect the output multiple steady states in the system. Different solution branches are observered when the flow rates of the feed stream and the organic reflux stream are selected as manipulated variables. The performance of the column under different steady states is different. A method is proposed to achieve the desired steady state. © 2011 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).


Chen B.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Niu Y.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Zou Y.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
Automatica | Year: 2013

This paper investigates the problem of sliding mode control for stochastic Markovian jumping systems, in which there may happen actuator degradation. By on-line estimating the loss of effectiveness of actuators, an adaptive sliding mode controller is designed such that the effect of the actuator degradation can be effectively attenuated. Besides, both the reachability of the specified sliding surfaces and the stability of sliding mode dynamics are ensured despite the actuator degradation and Markovian jumping. Finally, theoretical results are supported by numerical simulations. © 2013 Elsevier Ltd. All rights reserved.


Li S.,Key Laboratory of Advanced Control and Optimization for Chemical Processes | Li F.,Key Laboratory of Advanced Control and Optimization for Chemical Processes
Chinese Journal of Chemical Engineering | Year: 2012

Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compressor. To solve this problem, two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer. The input data of the model under other conditions should be corrected according to the similarity theory. The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system. © 2012 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).

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