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2014 International Conference on Environmental Engineering and Computer Application (ICEECA 2014)

作者： R.R.Wang L.L.Cao M.D.Liu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The model of an Orbal oxidation ditch activated sludge process was set up based on ASM3 and Takacs' s double index settlement rate of secondary sedimentation tank model in this paper. According to the condition of the multi-objective output simulation model, multi-objective particle swarm optimization algorithm(MOPSO) is used to calibrate part of the high sensitivity parameters in the activated sludge process model. Simulation results show that the effectiveness of MOPSO for parameter calibration is obvious, which can further improve the accuracy of the model.

关键词： ASM MOPSO Activated sludge model parameters calibration based on multi-objective PSO

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Modeling and optimization for the Molecular Weight Distribution of Polyethylene based on the Multi-output Support Vector Machine

Modeling and Optimization for the Molecular Weight Distribut...

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第25届中国过程控制会议

作者： Ye Zhencheng Li Yun Luo Na Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

the Molecular weight distribution(MWD)is an important quality index for the polymer material,but detecting the MWD in real-time is still difficult by *** rapid real-time detection method for MWD with good accuracy is the current hotspot in the polymer *** from the method other literatures have described,in this work the reaction mechanism and the industrial information will be merged to build a hybrid model for the MWD to solve the prediction accuracy and real-time problems,using the weighted superposition of the distribution function on each active center of catalyst to fit the MWD and applying the multi-output support vector machine regression(MSVR)algorithm to describe the relationship between process conditions and the parameters of distribution *** the unconstrained nonlinear optimization method has been used to optimize the process conditions based on the hybrid ***,the application of the above-mentioned approach in the ethylene polymerization process has verified the feasibility.

关键词： polyethylene,molecular weight distribution,modeling,optimization,multi-output support vector machine

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A Fast Algorithm for Reducing the Computation Burden of Model Predictive control

A Fast Algorithm for Reducing the Computation Burden of Mode...

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第25届中国过程控制会议

作者： Chao Chen Mei Hua Rongbin Qi Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process （East China University of Science and Technology） Ministry of Education

Most of industrial model-based predictive control algorithms are suffering from heavy computation burden when solving QR optimizations and on-line matrixes multiplication within a limited sampling *** order to optimize this problem,a fast algorithm is proposed,which the real-time values are modulated into bit streams to simplify the multiplication as the bit based operation could extremely decrease the compute *** addition,the control variables are deduced from the prediction horizon to the current control actuation approximately by a recursive relation instead of figuring all of the control actuations out strictly to reduce the matrix dimension.

关键词： Fast MPC Algorithm Computation Burden Bit-Streams Processing Dimension Reducing

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Distributed Model Predictive control Based on Multi-agent Model for Electric Multiple Units

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Acta Automatica Sinica 2014年第11期40卷 2625-2631页

作者： Zhong-Qi LI Hui YANG Kun-Peng ZHANG Ya-Ting FU School of Mechanical and Electrical Engineer Nanchang University Nanchang 330031 China School of Electrical and Electronic Engineering East China Jiaotong University Nanchang 330013 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China

The distributed-power electric multiple units (EMUs) are widely used in high-speed railway. Due to the structural characteristic of mutual-coupled power units in EMUs, each power unit is set as an agent. Combining with the traction/brake characteristic curve and running data of EMUs, a subtractive clustering method and pattern classification algorithm are adopted to set up a multi-model set for every agent. Then, the multi-agent model is established according to the multi-agent network topology and mutual-coupled constraint relations. Finally, we adopt a smooth start switching control strategy and a multi-agent distributed coordination control algorithm to ensure the synchronous speed tracking control of each agent. Simulation results on the actual CRH380A running data show the effectiveness of the proposed approach.

关键词： Electric multiple units (EMUs) multi-agent nonlinear multi-models distributed model predictive control synchronous tracking

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Event-triggered model predictive control for wireless networked control system

Event-triggered model predictive control for wireless networ...

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International Conference on Mechatronics and control (ICMC)

作者： Quan Wang Yuanyuan Zou Yugang Niu Key Laboratory of Advanced Control and Optimization for Chemical Processes East China Univ. of Science and Technology Shanghai China

ISBN: (纸本)9781479925391

In order to save communication consumption in the wireless nodes of networked control systems, this paper investigates the stabilization problem of an event-triggered constrained model predictive control. A state-feedback predictive control law is designed by solving an infinite horizon performance objective and an event-triggered condition involving the norm of a measurement error is derived based on input-to-state stability. Under the proposed mechanism, the measurements are sent by wireless network and the predictive control optimization is implemented only when the triggering conditions are satisfied. This approach not only can alleviate the energy consumption but also achieves the desired control performance and constraints satisfaction. Finally, an example is given to illustrate the effectiveness of the proposed results.

关键词： Predictive control Wireless sensor networks Networked control systems Closed loop systems Wireless networks

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A Fast Global Group Search Optimizer algorithm

A Fast Global Group Search Optimizer algorithm

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International Conference on Information and Automation (ICIA)

作者： Kang Zhang Xingsheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai

The Group Search Optimizer(GSO) is a novel optimization algorithm, which is inspired by searching behavior of animals. In this paper, we proposed an improved GSO algorithm named Fast Global Group Search Optimizer(FGGSO) to increase searching speed and balance the exploitation and exploration of the algorithm, which is based on our previous works. At first time, considering the complexity and time-consuming design of the producer's angle searching strategy, a novel local search mechanism, named campaign strategy, is developed, which is inspired by competition and cooperation between candidates in an electoral process. After that, a reconstruction operation is applied in searching process to guarantee the avoidance of the local minimum. The algorithm is evaluated on a set of 11 numerical optimization problems and compared favorably with other version of GSOs. Experimental results indicate the remarkable improvement on the performance of these problems.

关键词： optimization Standards Animals Acceleration Biological system modeling Convergence Search problems

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Multi-time Scale Hierarchical Predictive control for Energy Management of Microgrid System with Smart Users

Multi-time Scale Hierarchical Predictive Control for Energy ...

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第26届中国控制与决策会议

作者： Jun Xu Yuanyuan Zou Yugang Niu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China Univ.of Science and Technology Key Laboratory of System Control and Information Processing Ministry of Education

In this paper, a multi-time scale hierarchal model predictive control strategy is proposed to optimize energy management problem of a microgrid with multiple smart users. According to the power flow among different energy modules, a hierarchical system model and a multi-time scale hierarchal energy optimization management problem are established. The centralized controller in the upper layer is to optimize the charge/discharge time and energy of storage devices, controllable supply power adjustment and dispatch of the aggregators. The optimization problem in the lower layer is to meet users' demands in real time. Meanwhile, in order to improve the disturbances caused by the randomness of renewable energy and variant loads, a multi-time scale optimization scheme is applied. At the slow scale, the upper optimization problem is solved, and the optimal energy scheduling in the long-term can be achieved. At the fast scale, the energy balance between supply and demand of smart users can be realized in the short-term. Finally, simulation results illustrate the effectiveness of proposed method.

关键词： Smart user,Microgrid,Energy management,Multi-time scale time-hierarchal,Model predictive control

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Dynamic optimization of chemical engineering problems using affinity propagation based estimation of distribution algorithm

Dynamic optimization of chemical engineering problems using ...

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World Congress on Intelligent control and Automation (WCICA)

作者： Na Luo Wei Feng Xiaoqiang Wang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Dynamic optimization has attracted much attention for its wide applications in engineering problems. However, it is still a challenge for high nonlinear, multi-dimensional and multimodal problems. Estimation of Distribution Algorithm was proposed in which probabilistic models extracted relevant features of the complex search space and then generated new individuals during optimization. In order to decrease the dependences among control variables in dynamic optimization, affinity propagation was applied to cluster the individuals in evolutionary iterations. In each cluster, the probabilistic density function of Gaussian mixture model refined the promising spaces with high quality solutions and avoided the random combination of different control variables. To evaluate the performance of the new approach, three dynamic optimization problems of chemical process are used as cases comparing with three state-of-the-art global optimization methods. The results showed that the new approach could achieve the best solution in most cases with less computational effort and higher efficiency.

关键词： Statistics Probabilistic logic optimization Sociology Algorithm design and analysis Robustness Genetic algorithms

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A synthesis approach to predictive control for networked control systems 33

A synthesis approach to predictive control for networked con...

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Proceedings of the 33rd Chinese control Conference, CCC 2014

作者： Zou, Yuanyuan James, Lam Niu, Yugang Li, Dewei Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Mechanical Engineering University of Hong Kong Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789881563842

This paper studies a synthesis approach to predictive control for networked control systems with data loss and quantization. An augmented Markov jump linear model with polytopic uncertainties is modeled to describe the quantization errors and possible data loss. Based on this model, a predictive control synthesis approach is developed, which involves online optimization of a infinite horizon objective and conditions to deal with system constraints. The proposed MPC algorithm guarantees closed-loop mean-square stability and constraints satisfaction. © 2014 TCCT, CAA.

关键词： Networked control systems

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Statistical process monitoring based on improved principal component analysis and its application to chemical processes

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第7期14卷 520-534页

作者： Chu-dong TONG Xue-feng YAN Yu-xin MA Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

关键词： Fault detection Principal component analysis (PCA) Correlative principal components (CPCs) Tennessee Eastman process

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