Key Parameter Soft Sensing And Optimization Control For Piercing Processes

Nowadays, in order to improve the quality of seamless steel tubes, many domestic steel enterprises use skew rolling piercer with Diescher guide disc to pierce steel billet. But in the skew rolling piercer, the guide disc rev, which has significant effect on piercing production, can only be regulated offline. This has greatly reduced the production efficiency and quality of steel billet piercing, and thus become a bottleneck in steel tubes manufacturing. It has been an urgent problem with respect to how to online regulate the guide disc rev of piercer so as to improve the quality of steel tubes in piercing production and increase the production efficiency in seamless steel tubes.Based on the SWW skew rolling piercing process in certain Tube Branch of Steel Corporation, this dissertation establishes three types of models, i.e., soft sensing of guide disc rev, determination of optimal guide disc rev and soft sensing of load moment, and realizes the optimization control of guide disc rev. In addition, it builds piercing efficiency prediction and optimization model, which provides instructive significance for piercing producing. Finally, it realizes the effect verification of the above mentioned optimization control and online quality prediction of steel tube based on the construction of quality prediction model of steel tube. The main works of this dissertation are listed as follows:To overcome the problem that it is hard to online measure the guide disc rev, an improved PCA-ELM soft sensing method is proposed for the guide disc rev. In this method, PCA method is used to compress and filter process measurements. Moreover, the soft sensing model of guide disc rev is developed based on an improved PCA-ELM method, which overcomes the prediction speed deficiency of traditional ELM method when dealing with much modeling data and realizes the online prediction of guide disc rev. Meanwhile, the soft sensing model is adaptively updated using the combination of short-term and long-term adjustments.In order to improve production benefit, it is necessary to obtain the optimal guide disc rev under different conditions, which can not only upgrade steel tubes quality and piercing efficiency but also reduce energy cost. Focusing on the complexity of tube billet pierceing process and particularity of modeling data, ICR modeling method, which is suitable for modeling non-Gaussian data, is introduced to extract the independent components and build the optimal decision-making model of guide disc rev, in which, the number of independent components is determined by cross-validation. The development of above-mentioned optimal model can provide instructions and references for setting the optimal parameters of automatic control of guide disc rev.Focusing on the automatic control of guide disc rev, an optimization control system is developed. For the key parameter, the guide disc rev, which is difficult to online measure, the soft sensing model of guide disc rev realizes its online estimation, which establishes the foundation of direct control. The model of optimal guide disc rev determines the optimal setting parameters of guide disc rev according to tube billet raw materials, production requirement and operation status, which can be used to set the optimal setpoint of the guide disc rev automatic control. The soft sensing model of load torque realizes the realtime prediction of load torque, which is the primary disturbance in the system, and further realizes its feedforward compensation control, which, thus, greatly weakens the influence of this disturbance on guide disc rev.Considering that it is hard to maintain high pierceing efficiency during seamless steel tubes pierceing process, a pierceing efficiency prediction model is established using MPLS algorithm based on phase-specific average trajectory. Moreover, the optimal problem is solved under the process operation constrain, and the pierceing process parameters are obtained corresponding to the optimal pierceing efficiency. Meanwhile, to verify the optimal result of pierceing efficiency, and realize the online monitoring of tube quality, an MPLS algorithm based on phase-specific average trajectory is proposed for online tube quality prediction. The innovation of the proposed method lies in:(1) it proposes the idea of subphase; (2) it chooses the phase-specific secondary variables according to the different influences on prediction parameters during different process operation phases; and (3) it proposes the idea of average trajectory.Finally, this dissertation develops the overall framework of soft sensing of key pierceing parameters and optimization control system, detailedly introduces the human-computer interaction system, supervisor computer and background prediction and optimization system, and meanwhile presents their interrelationship.