Zhizhong Mao

Recursive parameter identification of Hammerstein–Wiener systems with measurement noise

Abstract A recursive algorithm is proposed in this paper to identify Hammerstein–Wiener systems with heteroscedastic measurement noise. Based on the parameterization model of Hammerstein–Wiener systems, the algorithm is derived by minimizing the expectation of the sum of squared parameter estimation errors. By replacing the immeasurable internal variables with their estimations, the need for the commonly used invertibility assumption on the output block can be eliminated. The convergence of the proposed algorithm is also studied and conditions for achieving the uniform convergence of the parameter estimation are determined. The validity of this algorithm is demonstrated with three simulation examples, including a practical electric arc furnace system case.

Model Predictive Control Synthesis Approach of Electrode Regulator System for Electric Arc Furnace

In electric arc furnace smelting, electrode regulator system is a key link. A good electrode control algorithm will reduce energy consumption effectively and shorten smelting time greatly. The offline design online synthesis model predictive control algorithm is proposed for electrode regulator system with input and output constraints. On the offline computation, the continuum of terminal constraint sets will be constructed. On the online synthesis, the timevarying terminal constraint sets will be adopted and at least one free control variable will be introduced to solve the min-max optimization control problem. Then Lyapunov method will be adopted to analyze closed-loop system stability. Simulation and field trial results show that the proposed offline design online synthesis model predictive control method is effective.

Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace

In this study, a novel prediction model, hybrid of mechanism method, Takagi-Sugeno (T-S) fuzzy modeling, regularization network technique and Multi-kernel learning algorithm, is proposed for accurately forecasting the liquid steel temperature in ladle furnace (LF). By virtue of mechanism method and TS fuzzy modeling technique, a partial linear structured mechanism model is firstly obtained, which contains a parametric linear part with unknown coefficients and a non-parametric part with unknown functional expression. Thereafter, it is parameterized and implemented by a modified regularization network, called partial linear regularization network (PLRN), which introduces a parametric linear part into the traditional regularization network. Furthermore, to optimally design the kernel of PLRN and thereby further improve the prediction performance, Multi-kernel learning approach is employed to obtain the so called Multi-kernel learnt PLRN. The principal innovation behind the proposed method is the embedding of the prior knowledge into the model, instead of directly predicting the steel temperature using machine learning techniques which is commonly used in the previous steel temperature prediction models. This innovation leads to better final results in reducing the model complexity, improving the generalization performance and consequently promoting the prediction precise. The experiment results demonstrate that the novel predictor is superior in prediction performance over other black-box based predictors. Furthermore, the prediction accuracy is boosted via Multi-kernel learning.

Identification of Hammerstein-Wiener Models Based on Bias Compensation Recursive Least Squares: Identification of Hammerstein-Wiener Models Based on Bias Compensation Recursive Least Squares

Many actual systems can be represented by the Hammerstein-Wiener model, where a linear dynamic system is surrounded by two static nonlinearities at its input and output. An improved on-line two stage identification algorithm is proposed to identify the Hammerstein-Wiener model with process noise. Firstly, the bias compensation recursive least squares is adopted to identify the parameter vector containing the product of the original system parameters. The estimation bias is compensated by introducing a correction term in the recursive least squares estimate. Secondly, the singular value decomposition method based on the tensor product approach is adopted to separate each parameter value from the original system. The accuracy of parameter separation is improved by introducing the tensor product of two matrixes to approach the weight coefficient of the weighted least squares. Theoretical analysis and computer simulation validate the effectiveness of the proposed algorithm.

Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging

For accurately forecasting the liquid steel temperature in ladle furnace (LF), a novel temperature prediction model based on optimally pruned Bagging combined with modified extreme learning machine (ELM) is proposed. By analyzing the mechanism of LF thermal system, a thermal model with partial linear structure is obtained. Subsequently, modified ELM, named as partial linear extreme learning machine (PLELM), is developed to estimate the unknown coefficients and undefined function of the thermal model. Finally, a pruning Bagging method is proposed to establish the aggregated prediction model for the sake of overcoming the limitation of individual predictor and further improving the prediction performance. In the pruning procedure, AdaBoost is adopted to modify the aggregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggregation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging’s robustness against highly influential points, reduce the storage needs as well as speed up the computing time. The proposed prediction model is examined by practical data, and comparisons with other methods demonstrate that the new ensemble predictor can improve prediction accuracy, and is usually consisted compactly.

Modeling and Optimization for Piercing Energy Consumption

Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square method. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the optimization method based on the energy consumption prediction model can obtain the optimum process parameters effectively and also provide reliable evidences for practical production.

Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm

This paper focuses on the recursive parameter estimation for the single input single output Hammerstein-Wiener system model, and the study is then extended to a rarely mentioned multiple input single output Hammerstein-Wiener system. Inspired by the extended Kalman filter algorithm, two basic recursive algorithms are derived from the first and the second order Taylor approximation. Based on the form of the first order approximation algorithm, a modified algorithm with larger parameter convergence domain is proposed to cope with the problem of small parameter convergence domain of the first order one and the application limit of the second order one. The validity of the modification on the expansion of convergence domain is shown from the convergence analysis and is demonstrated with two simulation cases.

Hybrid modelling for real-time prediction of the sulphur content during ladle furnace steel refining with embedding prior knowledge

Abstract Real-time prediction of the sulphur content of steel is of great importance for operation guidance during ladle furnace (LF) steel refining. For seeking an accurate prediction, this paper proposes to establish sulphur content prediction model in a hybrid way, where a simplified first principle model is introduced and fine tuned by data-driven modelling methods. The derived hybrid model employs optimization approach to optimize its data representation part, while prior knowledge is embedded in the form of linear constraints. An innovation of the proposed methodology is the full exploitation of prior knowledge about the process for determining reasonable process parameters. Moreover, a novel optimization approach is developed for ensuring accuracy and improving solution efficiency by the integration of genetic algorithm and successive approximation method. The proposed hybrid model possesses flexible interpretable structure and adaptive learning ability. As a result, it ensures the extrapolation property for real-time prediction and is able to provide an in-depth understanding of practical desulphurization process, making it very suitable for process monitoring and operations optimization during LF steel refining. Finally, this hybrid model is validated on recorded data from an industrial LF plant.

Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF

Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM (extreme learning machine) for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.

A Modeling Method for Time Series in Complex Industrial System

The data of complex industrial system were usually arrayed in the form of time series. This paper put forward the multivariate time-delayed principal component regression (MTPCR) method, which utilized the historical time series in the production process so as to establish a systematic prediction model. This method can calculate the delayed time of each input and output tunnel by which the modeling data were selected. The model established can predict the production outcome and product quality accurately in accordance with real-time input. With the aid of Simulink data and Matlab arithmetic, this paper concludes that MTPCR method possesses higher precision compared with other method