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arXiv 2018年

作者： Qiu, Zhaoyang Chen, Shugeng Allison, Brendan Z. Jia, Jie Wang, Xingyu Jin, Jing Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Department of Neurology Huashan hospital Fudan University Shanghai China Department of Cognitive Science University of California at San Diego San DiegoCA United States

Background: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor function after rehabilitation therapy. Some studies have shown that motor imagery (MI) based BCI has a positive effect in poststroke rehabilitation. It could help patients promote the reorganization processes in the damaged brain regions. However, offline motor imagery and conventional online motor imagery with feedback (such as rewarding sounds and movements of an avatar) could not reflect the true intention of the patients. Method: In this study, both virtual limbs and functional electrical stimulation (FES) were used as feedback to provide patients a closed-loop sensorimotor integration for motor rehabilitation. Motor imagery based BCI system acquired, analyzed and recognized the EEG data of motor imagery from patients. The FES system would activate if the user was imagining hand movement of instructed side. Ten stroke patients (7 male, aged 22-70 years, mean 49.5±15.1) were involved in this study. All of them participated in BCI-FES rehabilitation training for 4 weeks. Results: The average motor imagery accuracies of the ten patients in the last week were 71.3%, which has improved 3% than that in the first week. Five patients' Fugl-Meyer Assessment (FMA) scores have been raised. Patient 6, who has have suffered from stroke over two years, achieved the greatest improvement after rehabilitation training (pre FMA: 20, post FMA: 35). In the aspect of brain patterns, the active patterns of the five patients gradually became centralized and shifted to sensorimotor areas (channel C3 and C4) and premotor area (channel FC3 and FC4). Conclusions: In this study, motor imagery based BCI and FES system were combined to provided stoke patients with a closed-loop sensorimotor integration for motor rehabilitation. Result showed evidences that the BCI-FES system is effective in restoring upper extremities motor function in stroke. In future work, more cases are

关键词： Brain computer interface

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Soft sensor based on recursive kernel partial least squares for 4-carboxybenzaldehyde of an industrial terephthalic acid hydropurification process

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Chemical Engineering Transactions 2017年 61卷 463-468页

作者： Li, Zhi Zhong, Weimin Peng, Xin Du, Wenli Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Terephthalic acid is a raw material for polyester and textile industry. However, the by-product 4-Carboxybenzaldehyde in TA is harmful to the polymer process since it can lower the polymerization rate and the average molecular weight. Thus, a hydropurification process is built to decrease 4-CBA. In this process, 4-CBA in TA is purified by hydrogen in water at 270 °C-290 °C under 7.9 MPa pressure over 0.5 wt. % carboncoated palladium catalyst in a fixed-bed reactor. The activity of the catalyst will gradually decrease with the process running. The most important quality index for this process is the content of 4-CBA in the product. However, in real plant, the content of 4-CBA is analysed every two hours in a laboratory. It is a very large delay for control system so the operation conditions of the process could not be adjusted in time. These may results in a maximum two hours product failure. In this paper, a first principle model of the process is developed based on Aspen Plus. The accuracy of the model is verified by the model results and the actual plant results. Based on this model, a series of sensitive analysis are performed. Six variables include 4-CBA content in TA, react temperature, react pressure, hydrogen flow rate, catalyst activity and Slurry concentration are the main factors influencing the 4-CBA content in the product. Though the aspen model is accurate, these parameters in aspen model are not easy to adjust quickly. In order to predict 4-CBA content quickly, a soft sensor for 4-CBA is developed using recursive kernel partial least squares considering the nonlinearity and slow time-varying of the process. Results show that the prediction accuracy of this method is very high and it is easy for engineers to handle. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Chemical reactors

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Teaching-Learning-Based optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

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Journal of Applied Mathematics 2018年第none期2018卷 1-19页

作者： Chen, Xu Xu, Bin Yu, Kunjie Du, Wenli School of Electrical and Information Engineering Jiangsu University Zhenjiang Jiangsu212013 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China School of Mechanical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Electrical Engineering Zhengzhou University Zhengzhou450001 China

Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner's learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others;by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems. © 2018 Xu Chen et al.

关键词： Computational efficiency

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A new lumped kinetic model of an industrial hydrocracking process

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Chemical Engineering Transactions 2017年 61卷 673-678页

作者： Song, Wenjiang Zhong, Weimin Yang, Minglei Du, Wenli Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Hydrocracking is a catalytic cracking process for the conversion of heavy oil fractions, such as vacuum gas oil (VGO), into valuable products with lower boiling point, such as diesel, kerosene and gasoline. Due to the flexibility of the process, high yields and good quality of liquid products, hydrocracking has become an important unit in modern refinery. However, the study of kinetic modelling towards hydrocracking reactor (HCR) is limited due to its complicated reaction mechanism. In this study, the reactor model is established based on a new six-lump model considering the velocity change along the reactor, different thermal effects and kinetic differences between hydrotreater (HT) and hydrocracker (HC). Compared with the previously reported lumped kinetic models, the fine-tuned model (calibrated by differential evolution algorithm) can predict products yields more accurately, in particular outlet temperature of each beds and the detailed yields and temperature distribution along with the reactor beds. The proposed lump based kinetic model can be applied to the optimization and control of industrial hydrocracking process. © 2017, AIDIC Servizi S.r.l.

关键词： Kinetic theory

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Smart and Optimal Manufacturing： The key for the Transformation andDevelopment of the Process Industry

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Engineering 2017年第2期3卷 151-151页

作者： Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and TechnologyShanghai 200237 China

In recent years, smart manufactur-ing has been the focus of competitionand collaboration between countriesaround the world. The major developedand developing countries are speedingup their strategic planning and layoutfor smart manufacturing. Germany, theUnited States, and China have releasedthe Industry 4.0, Smart Process Man-ufacturing, and Made in China 2025initiatives, respectively, which aim ata deep integration of information andcommunication technology and man-ufacturing technology, thus achieving seamless integration of prod-ucts, equipment, humans, and organizations.

关键词：期刊编辑工作人物介绍工程

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Modeling Method of Data-driven Model Combined with A Priori Knowledge and Its Application in Average Particle Size Estimation of Composite Colloidal Sols

Modeling Method of Data-driven Model Combined with A Priori ...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Yang Zhou Wenxi Xu Shaojun Li Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

We present a universally applicable hybrid modeling method for nonlinear industrial processes that combine the a priori process knowledge with a data-driven *** method constructs a unified framework for the modeling process by integrating a data-driven modeling technique,sampling detection technique,constraint optimization problem,and an evolutionary *** the modeling process,a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori *** adding the constraints of process a priori knowledge,we can obtain more information about the actual process and avoid the over-fitting problem to some extent,especially when modeling a system with a small quantity of *** order to show the effectiveness of the method proposed in this paper,two general data-driven models,the polynomial regression model and radial basis function network model,are used as case ***,a function simulation experiment is designed to test effectiveness,and applied to estimate average particle size of ZrO2-TiO2 composite colloidal sols.

关键词： Data-driven modeling A priori knowledge Constraint handling Intelligence algorithm Hybrid model

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Non-Gaussian dynamic batch process monitoring based on ICA-JITL-MSSVDD

Non-Gaussian dynamic batch process monitoring based on ICA-J...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Zhaomin Lv Xuefeng Yan Qingchao Jiang .Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

To solve the non-Gaussian and time-varying dynamics problems of batch process,a new monitoring method called Independent component analysis and Just-in-time learning multiple subspace support vector data description(ICA-JITL-MSSVDD) is *** offline,a new multiple variable subspaces division method used for batch process based on Independent component analysis(ICA) is *** proposed multiple subspace division method is used as a global non-Gaussian modeling *** online,Just-in-time learning multiple(JITL) is employed to extract similar samples,and multiple subspace support vector data description(MSSVDD) models are *** method can solve the time-varying dynamics problem and MSSVDD is used as a local non-Gaussian modeling *** the integration of global and local non-Gaussian modeling methods,the non-Gaussian and time-varying dynamics problems of batch process can be *** fault detection effectiveness of ICA-JITL-MSSVDD can be demonstrated by simulating the fed-batch penicillin fermentation process.

关键词： Batch process monitoring Independent component analysis Just in time learning Multiple subspace Support vector data description

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A novel fault diagnosis approach using hidden Markov model for a multimode process

A novel fault diagnosis approach using hidden Markov model f...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Fan Wang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Chemical processes with multiple operational modes are common in modern process *** fault diagnosis methods meet challenges when facing multimodal complex data *** order to tackle this challenge,a new fault diagnosis approach utilizing hidden Markov model(HMM) is developed in this paper.A HMM trained by normal process data with multimodal feature has several hidden *** operating mode is represented by a hidden state which should be described via a finite Gaussian mixture *** multivariate Gaussian mixture distribution targets complex data distribution within each individual ***,for a monitored sample,the Mahalanobis distance(MD) inside each Gaussian component is constructed as monitoring *** contribution index is obtained through the differential decomposition along every variable direction to MD-based monitoring *** last,the hidden state Gaussian component probability strategy is used to integrate local monitoring index and local variable contribution index across all Gaussian components into a global monitoring indicator and a global variable contribution *** derived variable contribution index with probabilistic feature is employed to locate faulty *** to a numerical simulation and the Tennessee Eastman(TE) process display the fault detection and diagnosis performance of our proposed method.

关键词： Hidden Markov model Fault diagnosis Multimode process Fault detection

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Early Stopping Criterion Combining Probability Density Function with Validation Error for Improving the Generalization Capability of the Backpropagation Neural Network

Early Stopping Criterion Combining Probability Density Funct...

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2017 2nd International Conference on Applied Mechanics and Mechatronics Engineering(AMME 2017)

作者： Wei WANG Xue-feng YAN The Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

A new early stopping criterion that combines the probability density function error and the validation error was designed to evaluate a training process. In this new criterion, the real status of the backpropagation neural network(BPNN) was jointly evaluated by the validation error, the probability density error of the traditional training data, and the unlabeled data that was not considered in the traditional training process to determine the appropriate stopping time. The proposed approach was tested on three classes of automatic stopping criteria. In addition, it was compared with the stopping criteria based solely on the validation error and the probability density error with the use of four different data sets selected from the UCI Machine Learning *** showed that the new early stopping criterion is beneficial for enhancing the generalization capability of the BPNN, demonstrating an improved forecasting precision by 2%-3%.

关键词： Backpropagation neural network Early stopping criterion Generalization capability Probability density function

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Total Plant Performance Evaluation based on Big Data:Visualization Analysis of TE Process

Total Plant Performance Evaluation based on Big Data:Visuali...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Li Mengyao Du Wenli Qian Feng Zhong Weiming Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The performance evaluation of the process industry,which has been a popular topic nowadays,can not only find the weakness and verify the resilience and reliability of the process,but also provide some suggestions to improve the process benefits and ***,the performance assessment principally concentrate upon some parts of the entire system at present,for example the controller *** some researches focus on the whole process,they aim at discovering the relationships between profit,society,policies and so forth,instead of relations between overall performance and some manipulated variables,that is,the total plant *** this basis,this paper propose a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the *** variables related to safety and profits are regarded as key factors to performance *** better monitor the process state and observe the performance variation trend of the process,a classification-visualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is *** monitoring method is applied to Tennessee Eastman *** results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

关键词： performance evaluation structured logic rules hierarchical framework multidimensional visualization KPCA-SOM

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