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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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A Typical Power Allocation for Distributed Filtering

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IFAC-PapersOnLine 2017年第1期50卷 10550-10555页

作者： Zhang Y. Yang W. Yang C. Wang X. Sha F. Tian Y. Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China Department of Automation Shanghai Jiaotong University Shanghai 200240 China

In this paper, the problem of power allocation is considered for distributed estimation over a sensor network with limited power. An online power allocation scheme is introduced to optimize the power consumption, where the power distributed to each communication channel depends on the innovation value of the sender. First, an optimal estimator gain is designed for each sensor by minimizing the mean-squared estimation error covariance. Second, a sufficient condition is provided to guarantee the stability of networked system with online power allocation scheme. Finally, we compare the estimation performance of the proposed online power scheduling scheme with some typical offline power scheduling schemes. © 2017

关键词： Distributed estimation Modified algebraic Riccati equation (MARE) Power allocation Transmission probability

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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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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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Improved Garson Algorithm based on Neural Network Model 29

Improved Garson Algorithm based on Neural Network Model

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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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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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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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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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