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2012 8th International Conference on Natural Computation, ICNC 2012

作者： Cui, Wen-Tong Yan, Xue-Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781457721311

Considering that outliers can disrupt the correlation structure of least square support vector machine (LS-SVM), and that the parameters of LS-SVM play an important role in the performance, a novel weighted least square support vector machine integrated with parameter optimization is proposed to obtain the optimal parameters and to eliminate the effect of outliers. Several LS-SVM variants are applied in simulation experimentation and chemical process respectively to demonstrate the satisfactory performance of the proposed method. © 2012 IEEE.

关键词： Vectors

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Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization

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Chinese Journal of chemical Engineering 2012年第6期20卷 1089-1093页

作者：李绍军李凤 Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education

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.

关键词： compressor characteristic curve neural-network modeling

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An improved particle swarm algorithm based on cultural algorithm for constrained optimization

An improved particle swarm algorithm based on cultural algor...

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作者： Wang, Lina Cao, Cuiwen Xu, Zhenhao Gu, Xingsheng Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai 200237 China

ISBN: (纸本)9783642277078

This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm in population space to assure the searching breadth, and evolves with standard particle swarm optimization (PSO). Secondly, fixed proportion elites are selected from population space to construct the swarm of belief space through acceptance function. Then, the belief space updates its normative knowledge and situational knowledge according to the elite particles, and the elite-swarm in the belief space performs PSO operation according to the update knowledge and generates new particles. After that, the belief space renews the knowledge again, and passes down the new knowledge which has been updated twice to give better guidance to all the particles in the population space. The efficiency of the initialization strategy and the double evolving knowledge strategy are verified in six constrained optimization problems. © 2012 Springer-Verlag GmbH Berlin Heidelberg.

关键词： Constrained optimization

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Process monitoring of multimode processes using kernel independent component analysis

Process monitoring of multimode processes using kernel indep...

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8th International Symposium on advanced control of chemical processes, ADCHEM 2012

作者： Zhang, Yingwei An, Jiayu Ma, Chi State Key Laboratory of Synthetical Automation of Process Industry Northeastern University Shenyang Liaoning 110819 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 China

ISBN: (纸本)9783902823052

In the paper, a new process monitoring approach is proposed for handling the multimode problem in the industrial processes. The original space can be separated into two different parts, which are the common part and the specific part. There are both similarity and dissimilarity in the underlying correlations of different modes, which play different roles in the industrial processes. Because the industrial processes have the non-Gaussian and nonlinear characteristics, modified kernel independent component analysis is used to monitor the multimode processes in this paper. The global multimode basis vector and the multimode sub-basis vector are obtained based on the modified KICA. Then, the common part and specific part in one mode are respectively analyzed. The proposed method is applied to monitor the continuous annealing process. The proposed approach effectively captures the non-Gaussian and nonlinear relationship in different modes in the industrial processes. © 2012 IFAC.

关键词： Process monitoring

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Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved group search optimization and BP neural network

Soft sensor for ammonia concentration at the ammonia convert...

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第23届过程控制会议

作者： Xingdi Yan Wen Yang Hehe Ma Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education

The ammonia synthesis section is the core during the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects the production efficiency directly. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied to compare with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

关键词： Ammonia synthesis Ammonia concentration Soft sensor Group search optimization.

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The optimal model reduction method for spatially distributed system based on simulated annealing algorithm

The optimal model reduction method for spatially distributed...

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2012 International Conference on Computer Science and Information Processing, CSIP 2012

作者： Wang, Mengling Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology 130 Meilong Road Shanghai 200237 China

ISBN: (纸本)9781467314114

For partial differential equation description unknown spatially distributed systems, the number of local models determines the dimension of the model. So far, there is no mature method about how to obtain the optimal region division. Usually, the local region division is related with the location of sensors. It may affect the accuracy and computational complexityH of the modeling directly. This paper presents an optimal model reduction approach for spatially distributed systems based on simulated annealing algorithm. At first, the optimality criterion is presented. And then, the simulated annealing based iterative optimizing method is proposed to solve the optimal model reduction. The simulations demonstrated show the accuracy and efficiency of the proposed methodologies. © 2012 IEEE.

关键词： Simulated annealing

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An Improved PLS (IPLS) Method Utilizing Local Standardization Strategy for Multimode Process Monitoring

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Journal of Donghua University(English Edition) 2012年第4期29卷 288-294页

作者：马贺贺胡益阎兴頔侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.

关键词： fault detection multimode process partial least squares (PLS) local standardization data preprocessing

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Consensus based distributed estimation with sensor selection strategies in energy constrained wireless sensor networks

Consensus based distributed estimation with sensor selection...

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The 24th Chinese control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Wen Yang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of

In the applications of wireless sensor networks(WSNs), sensor energy saving is essential to increase the life of sensor networks. In this paper, we consider the problem of performing consensus based estimation over energy constrained WSNs, in which energy is conserved by selecting only a subset of sensors to observe the state of the dynamical system at each time step. First, we derive an sufficient condition for the convergence of the state estimation covariance. Second, we propose a sensor selection strategy to schedule sensors to measure the system state for next step with the goal of minimizing the state estimation error subject to sensor energy constraint. Finally, we provide some numerical examples to illustrate the performance and effectiveness of the proposal strategy.

关键词： consensus problem Kalman filter state estimation convex optimization

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Parameter estimation of industrial PET reactor using multi-objective kernel density estimation of distribution algorithm

Parameter estimation of industrial PET reactor using multi-o...

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作者： Luo, Na Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai 200237 China State-Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai 200237 China

With a multitude of reaction pathways, poly (ethylene-terephthalate) (PET) polymerization of industrial practice is complex, and the quality of PET is normally described in terms of several experimentally measured indices. In this paper, parameters estimation of industrial PET reactors is presented as a multi-objective problem to make the mathematic model consistent with the actual industrial process. Considering the interrelation among parameters and the failure of general optimization algorithms, a new multi-objective estimation of distribution algorithm is proposed. Kernel density estimation is used to make the new population more suitable for real-life problems instead of Gaussian model during the evolution of the algorithm. Strategies including selection of kernel width, sampling method and Pareto domination selection are used to explore and exploit the search space more efficiently. With industrial operating data identified in steady state and eliminated from gross error, kinetic parameters are estimated by minimizing carboxyl end group concentration and degree of polymerization simultaneously using the proposed algorithm. The simulation results show that the model with estimated parameters has better predictive performance compared with the experimental parameters. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.

关键词： Parameter estimation

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Research on Optimized Fuzzy controller based on GA for Precise Aeration System

Research on Optimized Fuzzy Controller based on GA for Preci...

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The 24th Chinese control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Wenyong Wang Ziqiang Sun Huanlai Cao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education E Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education E

It is known that the characteristic of Dissolved Oxygen (DO) control system is non-linear, variability and uncertainty, etc. This paper presents Genetic Algorithm (GA) to optimize the fuzzy controller, which adjusts the membership function of fuzzy controller parameters by GA. The results show that the optimized fuzzy controller based on GA is well up for the shortage of the traditional fuzzy controller. With this controller we can get target such as: obtaining better control result, improving the efficiency of sewage treatment, having significant effect for stable and secure running and reducing energy consumption.

关键词： Sewage treatment Dissolved oxygen Fuzzy control Genetic algorithm

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