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第29届中国控制与决策会议

作者： Sun Maozhun Liu Ji Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology School of Information Science and Engineering East China University of Science and Technology

ISBN: (纸本)9781509046584

The evaluation of input factors of complex system is a hot and difficult point in the sensitivity analysis. In this paper, the Garson algorithm based on artificial intelligence is studied and the original Garson algorithm accuracy is not high. Therefore, an improved Garson algorithm is proposed and the input factors are introduced into the Garson algorithm. At the same time, the original local sensitivity analysis algorithm is improved as the global sensitivity analysis algorithm and it increases the accuracy and stability of the Garson algorithm. Through the typical benchmark test function simulation, the experimental results show that the improved Garson algorithm has higher accuracy and stability in the evaluation of sensitivity coefficient. Finally, the improved Garson algorithm is applied to evaluate the input factors of the plate-fin heat exchangers. It shows that the IGarson algorithm is more feasibility and effectiveness.

关键词： sensitivity analysis artificial intelligence neural network local sensitivity analysis global sensitivity analysis

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Improved performance of process monitoring based on selection of key principal components

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Chinese Journal of chemical Engineering 2015年第12期23卷 1951-1957页

作者：宋冰马玉鑫侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be *** the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

关键词： Principal component analysis Information loss Fault detection key principal component

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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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Adaptive Local Outlier Probability for Dynamic Process Monitoring

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Chinese Journal of chemical Engineering 2014年第7期22卷 820-827页

作者：马玉鑫侍洪波王梦灵 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

关键词： Time-varying Complex data distribution Local outlier probability Multi-mode Fault detection

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Gradient-based immune algorithm for optimization of dynamic environments

Gradient-based immune algorithm for optimization of dynamic ...

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International Conference on Natural Computation

作者： Shi, Xuhua Qian, Feng Research Institute of Electric Automatic Control NingBo University NingBo China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ecust China University of Science and Technology ShangHai China

ISBN: (纸本)9781424459612

A novel immune algorithm suitable for dynamic environments (GIDE) is proposed based on a biological immune mechanism. GIDE models the dynamic process of artificial immune response with gradient-based diversity operators. Unlike traditional artificial immune algorithms, which require that randomly generated cells be added to the current population to explore its fitness landscape, GIDE uses a gradient-based diversity operator to speed up optimization in dynamic environments. Other immune algorithms are compared to GIDE by using Moving Peaks Benchmarks. Preliminary experiments showed that GIDE can maintain high population diversity during the search process, while simultaneously speeding up optimization. Thus, GIDE is useful for optimization of dynamic environments. © 2010 IEEE.

关键词： Bioinformatics

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Estimation of distribution algorithm sampling under gaussian and cauchy distribution in continuous domain

Estimation of distribution algorithm sampling under gaussian...

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2010 8th IEEE International Conference on control and Automation, ICCA 2010

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

ISBN: (纸本)9781424451951

Estimation of Distribution Algorithm is a new population based evolutionary optimization method and it generates new population from probability distribution model. Like most evolutionary algorithms, it is easy to trap into local optimums. In order to avoid this shortcoming, Gaussian and Cauchy probability density function are mixed as probability distribution model. For continuous problems, a new estimation of distribution algorithm sampling under the mixed model is presented. New individuals are generated not only from Gaussian distribution but sometimes from Cauchy distribution in order to keep diversity. The selection strategy of Gaussian and Cauchy distribution are also discussed. The new algorithm is tested on five benchmark functions and results are compared with basic and estimation of distribution algorithm with Cauchy mutation. © 2010 IEEE.

关键词： Gaussian distribution

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Immune agent-based neural networks assembly for soft-sensor

Immune agent-based neural networks assembly for soft-sensor

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2nd International Workshop on Intelligent Systems and Applications, ISA2010

ISBN: (纸本)9781424458745

Aiming at difficulty modeling of large amounts of industrial process data, a novel soft sensor model based on artificial immune agent-based multiple model Radial Basis Function (RBF) networks is proposed in this paper. The method is to predict the qualities of manufactured products of Crude Oil Tower. In the IMMST-Team system, some biological immune based operation and learning rules which can efficiently cluster the large amounts of data samples and adapt RBF submodels structure and parameter are established. Individuals in the immune system work cooperatively to accomplish the goal of model training. Meenwhile, immune memory and pattern recognition provide high efficiency of predicting. The prediction of dry point of naphtha produced in a practical industrial process is carried out as a case study. The results obtained indicate that the proposed method provides quality prediction with high efficiency and accuracy, which is capable of learning the relationships between process variables measured during the production cycle. ©2010 IEEE.

关键词： Crude oil

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A Knowledge Base System for Operation optimization: Design and Implementation Practice for the Polyethylene Process

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Engineering 2019年第6期5卷 1041-1048页

作者： Weimin Zhong Chaoyuan Li Xin Peng Feng Wan Xufeng An Zhou Tian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.

关键词： Ontology Operation optimization Knowledge base system Polyethylene process

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Plant-wide process monitoring based on mutual information-multiblock principal component analysis

Plant-wide process monitoring based on mutual information-mu...

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作者： Jiang, Qingchao Yan, Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology MeiLong Road No. 130 Shanghai200237 China

Multiblock principal component analysis (MBPCA) methods are gaining increasing attentions in monitoring plant-wide processes. Generally, MBPCA assumes that some process knowledge is incorporated for block division;however, process knowledge is not always available. A new totally data-driven MBPCA method, which employs mutual information (MI) to divide the blocks automatically, has been proposed. By constructing sub-blocks using MI, the division not only considers linear correlations between variables, but also takes into account non-linear relations thereby involving more statistical information. The PCA models in sub-blocks reflect more local behaviors of process, and the results in all blocks are combined together by support vector data description. The proposed method is implemented on a numerical process and the Tennessee Eastman process. Monitoring results demonstrate the feasibility and efficiency. © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Process monitoring

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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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