Modeling of steelmaking process with effective machine learning techniques

Laha, Dipak; Ren, Yongmao; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.eswa.2015.01.030

Cited by 75 publications

(35 citation statements)

References 41 publications

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“…In the future, we will consider other model combination strategies together with other types of base learners, such as neural networks with the fuzzy system [12] [16]. Meanwhile, with the massive data sources, some machine learning strategies, such as deep learning, will be incorporated in the next stage.…”

Section: Discussionmentioning

confidence: 99%

“…The information systems of steel companies generate and accumulate a significant amount of data, which can be considered as input variables [11]. Moreover, the input variables collected from the steelmaking processes are quite noisy [12]. The data cleaning and prepossessing should be undertaken to deal with inaccurate data and the specific use.…”

Section: A Steel Production Processmentioning

confidence: 99%

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Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Dong

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.

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Section: Discussionmentioning

confidence: 99%

Section: A Steel Production Processmentioning

confidence: 99%

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Dong

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…It is developed as the extension of decision trees [35]. This algorithm applies random binary trees that use a subset of the observations through bootstrapping techniques.…”

Section: Methodsmentioning

confidence: 99%

A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence

et al. 2017

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Abstract:Fire occurrence, which is examined in terms of fire density (number of fire/km 2 ) in this paper, has a close correlation with multiple spatiotemporal factors that include environmental, physical, and other socioeconomic predictors. Spatial autocorrelation exists widely and should be considered seriously for modeling the occurrence of fire in urban areas. Therefore, spatial econometric models (SE) were employed for modeling fire occurrence accordingly. Moreover, Random Forest (RF), which can manage the nonlinear correlation between predictors and shows steady predictive ability, was adopted. The performance of RF and SE models is discussed. Based on historical fire records of Hefei City as a case study in China, the results indicate that SE models have better predictive ability and among which the spatial autocorrelation model (SAC) is the best. Road density influences fire occurrence the most for SAC, while network distance to fire stations is the most important predictor for RF; they are selected in both models. Semivariograms are employed to explore their abilities to explain the spatial structure of fire occurrence, and the result shows that SAC works much better than RF. We give a further explanation for the generation of residuals between fire density and the common predictors in both models. Therefore, decision makers can make use of our conclusions to manage fire safety at the city scale.

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“…Han and Liu [17] proposed an ELM with optimized parameters to predict the endpoint carbon content and temperature of liquid steel. Laha et al [18] compared a number of machine learning models for predicting yield steel in a steelmaking work, and verified that the support vector regression (SVR) is the most powerful one. However, the data-driven model is a black-box lack of interpretation.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process-Based Hybrid Model for Predicting Oxygen Consumption in the Converter Steelmaking Process

2019

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Oxygen is one of the most important energies used in converter steelmaking processes of integrated iron and steel works. Precisely forecasting oxygen consumption before processing can benefit process control and energy optimization. This paper assumes there is a linear relationship between the oxygen consumption and input materials, and random noises are caused by other unmeasurable materials and unobserved reactions. Then, a novel hybrid prediction model integrating multiple linear regression (MLR) and Gaussian process regression (GPR) is introduced. In the hybrid model, the MLR method is developed to figure the global trend of the oxygen consumption, and the GPR method is applied to explore the local fluctuation caused by noise. Additionally, to accelerate the computational speed on the practical data set, a K-means clustering method is devised to respectively train a number of GPR models. The proposed hybrid model is validated with the actual data collected from an integrated iron and steel work in China, and compared with benchmark prediction models including MLR, artificial neural network, support vector machine and standard GPR. The forecasting results indicate that the suggested model is able to not only produce satisfactory point forecasts, but also estimate accurate probabilistic intervals.

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Modeling of steelmaking process with effective machine learning techniques

Cited by 75 publications

References 41 publications

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence

Gaussian Process-Based Hybrid Model for Predicting Oxygen Consumption in the Converter Steelmaking Process

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