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On-line fault detection and diagnosis in industrial processes using hidden Markov model

Developments in Chemical Engineering and Mineral processing 2005年第3-4期13卷 397-406页

作者： Zhou, Shao-Yuan Wang, Shu-Qing National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

For many on-line fault diagnosis schemes based on process data, a moving time window is an indispensable technique to track dynamic data. In this paper, a novel approach combining variable moving window and hidden Markov model (HMM) for on-line identification of abnormal operating conditions is proposed. The main feature of this method is that the window length can be changed with time. Before fault diagnosis, some process measurements are used for fault alarm. As a tool for feature extraction, principal component analysis (PCA) is employed in order to reduce the large number of correlated variables. The effectiveness of the approach is illustrated by case studies from the Tennessee Eastman process.

关键词： industrial management

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Blending Scheduling under Uncertainty Based on Particle Swarm Optimization Algorithm

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Chinese Journal of Chemical Engineering 2005年第4期13卷 535-541页

作者： ZHAO Xiaoqiang（赵小强） RONG Gang（荣冈） National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.

关键词： blending scheduling uncertainty gasoline blending particle swarm optimization algorithm nonlinear optimization

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Association Rules Mining Based on SVM and Its Application in Simulated Moving Bed PX Adsorption process

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Chinese Journal of Chemical Engineering 2005年第6期13卷 751-757页

作者：张英苏宏业褚健 National Laboratory of Industrial Control Technology Institute of Advanced Process ControlZhejiang UniversityHangzhou 310027China

In this paper, a novel data mining method is introduced to solve the multi-objective optimization problems of process industry. A hyperrectangle association rule mining （HARM） algorithm based on support vector machines （SVMs） is proposed. Hyperrectangles rules are constructed on the base of prototypes and support vectors （SVs） under some heuristic limitations. The proposed algorithm is applied to a simulated moving bed （SMB） paraxylene （PX） adsorption process. The relationships between the key process variables and some objective variables such as purity, recovery rate of PX are obtained. Using existing domain knowledge about PX adsorption process, most of the obtained association rules can be explained.

关键词： multi-object optimization simulated moving bed support vector machines rule extraction clustering

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Distribution population-based genetic algorithm for parameter optimization PID controller

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Zidonghua Xuebao/Acta Automatica Sinica 2005年第4期31卷 646-650页

作者： Chen, Qing-Geng Wang, Ning Huang, Shao-Feng National Key Laboratory of Industrial Control Technology Institute of Advanced Process Zhejiang University Hangzhou 310027 China

Enlightened by distribution of creatures in natural ecology environment, the distribution population-based genetic algorithm (DPGA) is presented in this paper. The searching capability of the algorithm is improved by competition between distribution populations to reduce the search zone. This method is applied to design of optimal parameters of PID controllers with examples, and the simulation results show that satisfactory performances are obtained.

关键词： Three term control systems

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A Robust Statistical Batch process Monitoring Framework and Its Application

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Chinese Journal of Chemical Engineering 2004年第5期12卷 682-687页

作者：谢磊张建明王树青 National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

关键词： robust statistical batch process monitoring robust principal componentanalysis streptomycin fermentation robust multi-way principal component analysis

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A neuron model-free controller with immune tuning gain for hydroelectric generating units

A neuron model-free controller with immune tuning gain for h...

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American control Conference (ACC)

作者： Jianming Zhang Ning Wang Shuqing Wang Institute of Advanced Process Control National Key Laboratory of Industrial Control Technology University of Zhejiang Hangzhou China National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

A neuron model-free control method with immune scheme tuning gain for hydroelectric generating units is presented in this paper. Under the operating conditions of various guide vane opening, the characteristics of a hydroelectric generating unit show much difference in parameters. The influences of the time-varying parameters and the disturbances on the control system can be reduced by using the neuron model-free control method with immune scheme turning gain. With an example, the simulation results show that the neuron model-free controller has good control performance, fast transient response and strong robustness.

关键词： Neurons Hydroelectric power generation control system synthesis process control industrial control Proportional control control systems Fuzzy control Laboratories Character generation

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A developed method of tuning PID controllers with fuzzy rules for integrating processes

A developed method of tuning PID controllers with fuzzy rule...

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American control Conference (ACC)

作者： Jianming Zhang Ning Wang Shuqing Wang National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

The proportional integral derivative (PID) controllers are widely applied in industrial process, and the tuning method of PID controller parameters is still a hot research area. A fuzzy tuning scheme for PID controller settings is developed for integrator plus time delay processes in this paper, in which a fuzzy rule base reasoning method are utilized on-line to determine a tuning parameter /spl alpha/ based on the error and the first change of the error of the process. Then this tuning parameter /spl alpha/ is used to calculate the PID controller parameters. Computer simulations are performed for an example of integrating plants. Comparing to some reported methods, the performance of the presented approach is shown to be satisfying.

关键词： Three-term control Fuzzy control PD control Pi control Proportional control Fuzzy reasoning Fuzzy sets Computer errors Error correction industrial control

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Neuron controller with self-tuning gain for pH processes

Neuron controller with self-tuning gain for pH processes

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World Congress on Intelligent control and Automation (WCICA)

作者： Ning Wang Guang Yang National Key Laboratory of Industrial Control Technology and Research Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780382730

The neuron control method with self-tuning gain is proposed for a pH neutralization process in this paper. In this control system, the fuzzy T-S model is used to predict the control signal. The neuron controller gain is calculated and tuned according to the parameter estimation and experience formulas. The simulation results show that the proposed control method can control the pH process with grave nonlinearity.

关键词： Neurons process control Laboratories industrial control control system synthesis Fuzzy control Fuzzy systems Predictive models Parameter estimation

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Fuzzy controller with neuron self-tuning parameters

Fuzzy controller with neuron self-tuning parameters

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World Congress on Intelligent control and Automation (WCICA)

作者： Guang Yang Ning Wang National Key Laboratory of Industrial Control Technology and Research Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780382730

The fuzzy control method with neuron tuning fuzzification factors is proposed for plants with uncertainties in this paper. In order to enhance the robustness of the control system, the adaptive neuron is used to tune the fuzzification factors of the fuzzy controller when the characteristics of the plant change. The simulation experiments of application of the proposed method for controlling a ship changeable pitch propeller are made. The results demonstrate that this fuzzy control method achieves the desired performance.

关键词： Fuzzy control Neurons control systems Tuning Uncertainty Robust control Adaptive systems Programmable control Adaptive control Fuzzy systems

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A PID controller with neuron tuning parameters for multi-model plants

A PID controller with neuron tuning parameters for multi-mod...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ya-Ping Du Ning Wang National Key Laboratory of Industrial Control Technology Research Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780384032

Because there exist uncertainties in practice, a real process often presents multi-model dynamic characteristics. Therefore, it is not easy to reach satisfaction performance by using a conventional PID controller. In this paper, the PID control method with neuron tuning parameters is proposed for multi-model plants. In this model-free control system, the PID controller is designed to control a multi-model plant and the adaptive neuron is used to regulate the parameters of the PID controller on line. By self-learning and associative searching, the adaptive neuron can modify the PID controller parameters according to the dynamic characteristics of the plant. Applying the proposed method to the basis weight control of a paper machine, the simulation experiments are made. The results illustrate that the model-free PID controller is available to multi-model plants.

关键词： Three-term control Neurons Programmable control Adaptive control control system synthesis control systems Weight control Paper making machines Tuning Uncertainty

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