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Enhancing the Performance of JADE Using Two-phase Parameter control Scheme and Its Application

International Journal of Automation and computing 2018年第4期15卷 462-473页

作者： Qin-Qin Fan Yi-Lian Zhang Xue-Feng Yan Zhi-Huan Wang Logistics Research Center Shanghai Maritime UniversityShanghai 201306China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237China Key Laboratory of Marine Technology and Control Engineering Ministry of Communications Shanghai Maritime UniversityShanghai 201306China

The search efficiency of differential evolution （DE） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on pop- ulation information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE （i.e., JADE） using two-phase parameter control scheme （TPC-JADE） is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites （i.e., IEEE CEC2005 and IEEE CEC2015）. Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.

关键词： Differential evolution（DE）algorithm evolutionary computation dynamic optimization control parameter adaptation chemical processes.

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Hybrid adaptive cuckoo algorithm based on local search strategy

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Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS 2019年第11期25卷 2788-2802页

作者： Zhang, Tao Wang, Xin Wang, Zhenlei Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Electrical & Electronic Experimental Teaching Center Shanghai Jiao Tong University Shanghai200240 China

To enhance the search accuracy and global convergence rate of Cuckoo Search (CS) algorithm, a Hybrid Adaptive Cuckoo algorithm based on Local search strategy (LHACS) was proposed. In this algorithm, a local population was randomly generated around each current solution, the local optimal solution was obtained by sine cosine operator and the current solution was replaced by local optimal solution, which had improved the local search accuracy actually. The adaptive discovery probability and factor of search step were substituted for the fixed ones in CS to enhance the global convergence speed of the algorithm. The experiment of 25 classical high-dimensional benchmark functions showed that the LHACS was superior to CS in convergence rate and search accuracy, and it was applied to engineering optimization design and 0-1 knapsack problems, which verified the validity of the algorithm. © 2019, Editorial Department of CIMS. All right reserved.

关键词： Optimal systems

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Plant-wide optimization for high value added products - A steam cracking case study 30

Plant-wide optimization for high value added products - A st...

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30th Ethylene Producers Conference 2018 - Topical Conference at the 2018 AIChE Spring Meeting and 14th Global Congress on Process Safety

作者： Van Geem, Kevin M. Marin, Guy B. Muñoz, Andrés E.G. Zhang, Yu Du, Wenli Qian, Feng Laboratory for Chemical Technology Ghent University Belgium AVGI Syria Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology China

ISBN: (纸本)9781510864375

Recent progress in accurate modeling of steam cracking plants has enabled global optimization to be performed at a plant-wide level. This paper presents a real time optimization (RTO) system developed by East China University of Science and Technology (ECUST) to maximize the net profit of the ethylene plant of Sinopec Shanghai Petrochemical Company (SSPC). The system has a three-layer structure consisting of the global RTO, the local RTO and the advanced process control (APC) layer from top to bottom, among which the APC layer has already been applied to SSPC and is currently in service. COILSIM1D-RTO is used to simulate the steam cracking reactors. A simplified radiant box model and a convection section model are developed to account for the fuel consumption and superheated high pressure steam generation. The downstream compression and separation units are modeled in Aspen Plus to evaluate the maximum allowed process gas load and energy consumption. As the plant-wide RTO system is currently under implementation and no real time optimization results can be obtained, a case study is performed using historical operating data from year 2017 to illustrate the benefit that this plantwide RTO system can deliver. Optimization results show that most of the crackers should be operated under higher cracking severity to improve yields of the high value added products. By optimizing the cracking severity and dilution steam ratio, the estimated increase of net profit of the ethylene plant in Case 1 and Case 2 is about 29.50 million and 30.75 million yuan per year, respectively (i.e. approx. 4.7 million USD and 4.9 million USD, at current exchange rates). Copyright © American Institute Of chemical Engineers. All rights reserved.

关键词： Global optimization

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Specific index-related process monitoring using a two-step information extraction method 30

Specific index-related process monitoring using a two-step i...

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30th Chinese control and Decision Conference, CCDC 2018

作者： Zhao, Bo Song, Bing Tan, Shuai Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9781538612439

Specific index-related process monitoring covers a wide range of requirements from industrial production. At present, it is still a challenge to divide into the specific index-related information and the specific index-unrelated information more accurately. In this paper, a two-step information extraction method is presented to solve this problem. In the first step, the overall process variables are separated into a strongly related variable set and a weakly related variable set. In the second step, the two sets are further divided into a specific index-related subset and a specific index-unrelated subset, respectively. Two specific index-related subsets form a new subspace related to the specific index. Two specific index-unrelated subsets are combined into a specific index-unrelated subspace. Two T2 statistics are built in the specific index-related subspace and the specific index-unrelated subspace, respectively. Finally, the proposed method is applied for Tennessee Eastman (TE). The results indicate the effectiveness of the proposed method. © 2018 IEEE.

关键词： Indexes Information retrieval Monitoring Principal component analysis Correlation coefficient Correlation Fault detection

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Improved RCSP and AdaBoost-based classification for Motor-Imagery BCI

Improved RCSP and AdaBoost-based classification for Motor-Im...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Yangyang Miao Feiyu Yin Cili Zuo Xingyu Wang Jing Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai P. R. China

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

One of the popular feature extraction algorithms for motor imagery (MI)-based brain-computer interface (BCI) is common spatial pattern (CSP). However, CSP is also very susceptive to the selection of the filter bands, the time windows, and the channels. In this paper, we proposed a novel regularized CSP (RCSP) method to optimize feature extraction in MI-BCI. Then, a robust classifier based on AdaBoost algorithm was presented to perform the classification of MI tasks. Finally, the framework was verified on two public BCI datasets (dataset 1 from the BCI Competition IV and dataset IVa from BCI Competition III). The results suggest the proposed approach achieved superior performance compared with classical CSP and other competing methods. Overall, this method not only improved classification performance, but also reduced the data requirements of other subjects.

关键词： Feature extraction Covariance matrices Support vector machines Electroencephalography Classification algorithms Matrix decomposition Optimization

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Quantum Go Machine

arXiv

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arXiv 2020年

作者： Qiao, Lu-Feng Gao, Jun Jiao, Zhi-Qiang Zhang, Zhe-Yong Cao, Zhu Ren, Ruo-Jing Zhang, Chao-Ni Hu, Cheng-Qiu Xu, Xiao-Yun Tang, Hao Ma, Zhi-Hao Jin, Xian-Min School of Physics and Astronomy State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China

Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, we excitedly find that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart. Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. We further verify its nondeterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state. Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Stationary bipartite consensus of second-order multi-agent systems: an impulsive approach

Stationary bipartite consensus of second-order multi-agent s...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zhen Li Wenqing Wang Yongqing Fan Wenle Zhang School of Automation Xi-an University of Posts & Telecommunications Xi-an China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

This paper studies the stationary bipartite consensus problem of a kind of multi-agent systems with second-order dynamics, where the impulsive control approach is utilized to design the control protocol. The impulsive control law is only considered by using position-based information, and the structure of control law is induced by a structurally balanced graph. Then, the stationary bipartite consensus problem has been converted to a convergence problem with respect to a finite product of stochastic matrices. By using the norm matrix and convex theory, this convergence problem is proven to be stability, which means that the stationary bipartite consensus problem is ensured. Subsequently, a numerical example is given to show the obtained result.

关键词： Multi-agent systems Convergence Lyapunov methods Stability analysis Numerical stability Topology Synchronization

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An improved UKPK-PSO algorithm inspired from block chain technology for flexible job shop scheduling problem

An improved UKPK-PSO algorithm inspired from block chain tec...

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第三十八届中国控制会议

作者： Lin Yao Yabin Liu Houbao Zhao Haojie Ding Shanghai Polytechnic University Shanghai Lixin University of Accounting and Finance Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

The paper proposes a new encoding method inspired from the block chain technology, by which an operation sequence information and the corresponding machine information is integrated in an operation node and links all operation nodes to form an operation list with pointer technology of C++ program language. Accordingly, the paper designs an individual unit ID for every possible cases. Subsequently, it describes the process of searching the optimal solution of FJSP by implementing an improved UKPK-PSO algorithm, which hybridizes a varied PSO algorithm by getting the best information from K informants and the method of keeping the elitists for next generation in iterations. With the improved algorithm we can more efficiently traverse the solution space. After obtained some better solutions with the improved PSO, we continuously search the optimal solutions by tentatively changing the chosen machines of the dominant operations. Finally, it outputs the optimal solution through some limited iterations again. By analyzing the experiment results for the instances, the paper furtherly proves that the whole algorithm is effective for FJSP.

关键词： Flexible job shop scheduling problems Particle Swarm Optimal Algorithm Block chain Technology Operations Research

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Robust L2 disturbance attenuation for a class of uncertain Lipschitz nonlinear systems with input delay

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International Journal of control 2019年第5期92卷 1015-1021页

作者： Zuo, Zongyu Wang, Chunyan Yang, Wen Ding, Zhengtao Beijing China Beijing China School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

In this paper, we study a robust L2 disturbance attenuation problem that arises when applying the Artstein–Kwon–Pearson reduction transformation for a class of uncertain Lipschitz nonlinear systems with input delay and external disturbances. A conventional predictor-based feedback controller is adopted with the control gain matrix carefully identified by solving a couple of sufficient conditions in terms of linear matrix inequalities. Lyapunov–Krasovskii functionals are constructed to guarantee that the robust L2 disturbance attenuation problem can be solved by the proposed controller. A numerical example is included to validate the performance of the proposed controller. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

关键词： controllers

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Predictive control for Continuous Stirred Tank Reactors

Predictive Control for Continuous Stirred Tank Reactors

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2019 International Conference on Informatics, control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

作者： Ioana NASCU Ioan NASCU Wen-li DU Sai GU Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Department of Chemical and Process Engineering University of Surrey Department of Automation Technical University of Cluj-Napoca

The paper focuses on the implementation of a model based predictive control(MBPC) method, for Continuous Stirred Tank Reactors. First, the modelling problem of a single irreversible exothermic reaction, taking place in a perfectly mixed continuously stirred tank reactor(CSTR) is presented. The dynamic model consists of differential material and energy balance equations. The control strategy is investigated and evaluated by performing simulations and analyzing the results. The disturbance rejection capacity of the control system(regulatory control performances) have been tested and compared with those obtained using a classical Proportional Integral Derivative(PID) based controller. The results show that this control strategy has good performances and can be efficiently used to control the CSTR.

关键词： Predictive control PID control chemical reactor Process model

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