Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process

Jian, Ling; Song, Yong; Shen, Shu-Qian; Wang, Yan; Yin, Haiqing

doi:10.2355/isijinternational.55.845

Cited by 25 publications

(7 citation statements)

References 19 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Table 3 and Figure 5 , the magnitudes of the variables have big difference clearly. In fact, the effect of the variables with a large magnitude on the modeling is larger than the one with a small magnitude, thus it is not appropriate to directly take the data to establish the model [ 47 ]. Thus, all the data are normalized into (0, 1) with the same magnitude to eliminate the influence of dimension among variables before applying in the experiments.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Zhang

Yin

et al. 2017

Sensors

View full text Add to dashboard Cite

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

show abstract

Section: Simulation Results and Analysismentioning

confidence: 99%

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Zhang

Yin

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…In this case study, we compare the proposed BOKRR algorithm with online learning algorithm SWOKRR, BOLSSVM and batch-mode learning algorithm TIKRR. Gaussian kernel is specified as the kernel function for the compared algorithms for its good performance in applications [29]. The first 400 samples of two datasets are selected to form the initial active set.…”

Section: B Evaluation On a Real-world Application Problemmentioning

confidence: 99%

Toward Budgeted Online Kernel Ridge Regression on Streaming Data

et al. 2019

Self Cite

View full text Add to dashboard Cite

''Concept drift'' makes learning from streaming data fundamentally different from traditional batch learning. Focusing on the regression task on streaming data, this paper presents an efficient online learning algorithm, i.e., budgeted online kernel ridge regression (BOKRR). It is a budget version kernel ridge regression algorithm coupled with minimum contribution criterion to maintain the budget of the active set. BOKRR employs low-rank correction technology and the Sherman-Morrison-Woodbury formula to update the dynamic KRR model with the computational complexity of only O(B 2) with B learning samples (Budget size of an active set). Limited storage burden and efficient computational ability make the proposed BOKRR algorithm an ideal candidate to process streaming data. The experimental results on benchmark and real-world datasets further demonstrate the validity and efficiency of the proposed algorithms. INDEX TERMS Budget, kernel ridge regression, online learning, streaming data.

show abstract

“…Table 1 illustrates the statistical properties of COD and BOD in respect of the divided training and testing sets, and Figure 2, it can be found that the data has the characteristics of violent fluctuation, and the magnitudes of the variables clearly display a big difference. In fact, the effect of the variables with a large magnitude on the modeling is larger than the one with a small magnitude, and thus it is not appropriate to directly take the data to establish the model [55]. Thus, all the data are normalized to (0, 1) with the same magnitude to eliminate the influence of the dimension among variables before applying them in the experiments.…”

Section: Data Setsmentioning

confidence: 99%

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Yang

Bai

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatment at a low cost. In this paper, a combined kernel principal component analysis (KPCA) and extreme learning machine (ELM) model is established to forecast the inlet water quality of sewage treatment. Specifically, KPCA is employed for feature extraction and dimensionality reduction of the inlet wastewater quality and ELM is utilized for the future inlet water quality forecasting. The experimental results indicated that the KPCA-ELM model has a higher accuracy than the other comparison PCA-ELM model, ELM model, and back propagation neural network (BPNN) model for forecasting COD and BOD concentration of the inlet wastewater, with mean absolute error (MAE) values of 2.322 mg/L and 1.125 mg/L, mean absolute percentage error (MAPE) values of 1.223% and 1.321%, and root mean square error (RMSE) values of 3.108 and 1.340, respectively. It is recommended from this research that the method may provide a reliable and effective reference for forecasting the water quality of sewage treatment.

show abstract

Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process

Cited by 25 publications

References 19 publications

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Toward Budgeted Online Kernel Ridge Regression on Streaming Data

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Contact Info

Product

Resources

About