End‐Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Abstract

A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.

Continuous Prediction Model of Carbon Content in 120 t Converter Blowing Process

“…A simple statistical model is difficult to apply to converter smelting with so many influencing factors. In recent years, researchers have widely used neural network modeling methods to obtain high prediction accuracy [14][15][16][17][18]. Neural network model needs a large number of complete and accurate data samples for training in order to ensure the prediction accuracy.…”

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid‐Coded HLO

“…Nowadays, combustion furnaces have been widely applied in different fields of industry [1], such as coal-fired power plants [2], steelmaking [3], waste incineration [4], and cement production [5]. Since the combustion flame is one of the most direct characteristics that reflect the combustion status inside the industrial furnace, the accurate detection of combustion flame can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation.…”

Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

“…e curve fitting method has high accuracy, but there are difficulties in selecting fitting points. e wavelet transform (WT), that developed rapidly from the 1980s, can fully highlight the characteristics of some aspects of problem, which has been widely used in capillary electrophoresis (CE) signal denoising [20,21]. Furthermore, the wavelet threshold denoising developed from WT has better performance [22].…”