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Pian J.-X.,Shenyang Jianzhu University | Pian J.-X.,CAS Shenyang Institute of Automation | Chai T.-Y.,Northeastern University China | Chai T.-Y.,State Key Laboratory of Integrated Automation for Process Industry | Li J.-J.,Shenyang Jianzhu University
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2012

In some previous study, the strip coiling temperature prediction compensator and batch to batch compensator cannot obtain good compensating results due to the manually adjusted weight parameters for index feature of the case-based reasoning (CBR) system. And exact match and effective iteration cannot be done for the lack of initial operating condition matching algorithm. For this reason, a method based on neural network technology is proposed to learn the weights parameters of the index features of CBR system, with an initial operating condition matching algorithm that uses iterative learning technique to improve prediction compensator and the batch to batch compensator. The proposed hybrid intelligent control method is applied to a large domestic steel plant, and the results show that the strip coiling temperature control error decrease 1.63°C and the hit rate increased 14.5 % where the coiling temperature errors are controlled in the range of ±10°C. Source


Pian J.-X.,Shenyang Jianzhu University | Chai T.-Y.,Northeastern University China | Chai T.-Y.,State Key Laboratory of Integrated Automation for Process Industry | Li J.-J.,Shenyang Jianzhu University
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2012

The existing cooling process models lack the methods to compute the heat transfer parameter and the position that strip reaches and cannot be used to compute the strip coiling temperature directly. So a strip coiling temperature model is proposed, which consists of the status of cooling unit valves calculating model, the strip segment tracking model, and the top surface temperature model under different heat transfer conditions. What is more, a rule and data driven hybrid intelligent identification algorithm is developed combining the case-based reasoning, rule-reasoning with the neural network. The tests using real industrial data of a steel plant have been conducted and indicated that the proposed strip coiling temperature model has made a great contribution to the prediction precision of the strip coiling temperature during the laminar cooling process. © 2012 Acta Automatica Sinica. Source


Pian J.,Shenyang Jianzhu University | Pian J.,CAS Shenyang Institute of Automation | Chai T.,Northeastern University China | Chai T.,State Key Laboratory of Integrated Automation for Process Industry | Li J.,Shenyang Jianzhu University
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2013

The hot rolled strip laminar cooling process has the features of strong nonlinearity and severely changed operating conditions, and these complex industrial features are difficult to describe with an accurate mathematic model. As a result, a new control technology is needed to ensure the final quality of the strip. The repeat characteristics of the laminar cooling process make the iterative learning method suitable for the learning between the strips (between the batches). So, the iterative learning method based on variable-structure PI is proposed to carry out the preset and control of the system in this paper, in which the parameters of PI are adjusted with the CBR (cased-based reasoning) technology according to the varying working conditions. The experiment research results show that the proposed method can find the right operating point quickly for the strips with similar working condition, and make the coiling temperature of the strips after the cooling process controlled within the target range. Source


Zhao D.-Y.,State Key Laboratory of Integrated Automation for Process Industry | Chai T.-Y.,State Key Laboratory of Integrated Automation for Process Industry | Chai T.-Y.,Northeastern University China
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2013

In the mineral regrinding process of hematite beneficiation, the sump level (SL) fluctuates frequently due to some large disturbances. The pump speed inevitably changes in a wide range by adopting the existing setpoint control method for sump level, and the oscillations of hydrocyclone pressure (HP) can hardly be restricted in its desired range consequently. Hence, the classification efficiency of hydrocyclone is reduced remarkablely. In this paper, a two-layer hierarchical control structure based on fuzzy switching control method is proposed, which includes a switching controller of SL interval and a feedback controller of HP. By switching between a retainer of HP setpoint and a fuzzy compensator for HP setpoint, the controller of SL interval can guarantee the variations of HP setpoint within its desired range. In addition, the PI controller of HP can track its setpoint. Therefore, the sump level and the oscillations of HP can be limited in their target ranges respectively. The proposed method has been successfully applied to a large-scale domestic hematite beneficiation plant. The application results demonstrated that the fluctuations of SL and HP were significantly reduced. As a result, the safety operation of the regrinding process has been realized, and the improved classification efficiency of hydrocyclone has been achieved. Copyright © 2013 Acta Automatica Sinica. All rights reserved. Source


Jun Z.,Northeastern University China | Zhi-zhong M.,Northeastern University China | Zhi-zhong M.,State Key Laboratory of Integrated Automation for Process Industry | Run-da J.,Northeastern University China | Run-da J.,State Key Laboratory of Integrated Automation for Process Industry
Chemical Engineering Science | Year: 2015

The real time optimization (RTO) of gold cyanidation leaching process has been investigated in this paper. To solve inevitable plant-model mismatch and process disturbance in practice, an RTO strategy based on SCFO (Sufficient Conditions for Feasibility and Optimality) for gold cyanidation leaching process is proposed, where the SCFO are used to enforce the desirable properties (both feasibility and optimality) of the solutions resulted from the traditional RTO methods. The simulation results show that compared with the traditional RTO methods, there is a significant improvement of production cost by enforcing the SCFO under model-parameter uncertainty, model-structure uncertainty and unknown process disturbance, which has laid an important foundation for the successful implementation of the plant-wide optimization and control for hydrometallurgy process. © 2015 Elsevier Ltd. Source

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