Multi-model control of blast furnace burden surface based on fuzzy SVM

Liu, Yuanyuan; Liu, Dexin; Jia, Chao; Chen, Xianzhong

doi:10.1016/j.neucom.2013.09.067

Cited by 28 publications

(15 citation statements)

References 16 publications

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“…Specifically, in the first stage, in order to make full use of the advantages of different algorithms to get more comprehensive Pareto optimal solutions, NSGA-II and MOP-SO algorithms are firstly adopted to solve the optimization problem shown in Eqs. (12) and (13). All solutions obtained by the two algorithms are merged to find the final Pareto optimal solutions.…”

Section: Determing the Initial Setting Values Based On Multiobjectmentioning

confidence: 99%

“…For the MOP formulated as Eqs. (12) and (13), the two-stage intelligent optimization strategy is used to determine the initial setting values b. The related variables used in the NSGA-II algorithm are: population size is set to N pop = 200, the crossover probability is assumed to be 0.95 and the mutation probability is set as 0.1.…”

Section: B Determination Experiments For the Setting Values Of Burdementioning

confidence: 99%

“…To our best knowledge, researchers rarely attempted to determine the optimal setting of burden surface. Only Li et al [13] adopted the self-optimizing method to establish the reasoning mechanism and constructed multiple sets of burden surface by K-means clustering algorithm, which obtains the local optimum and cannot ensure that the production indicators are within their target ranges.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in energy-efficient state. In the proposed scheme, production indicators prediction models are firstly developed using kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multi-objective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which, an improved association rules mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

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Section: Determing the Initial Setting Values Based On Multiobjectmentioning

confidence: 99%

Section: B Determination Experiments For the Setting Values Of Burdementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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“…The most widely used method is the fuzzy clustering method based on the objective function. This method can transform the clustering problem into a mathematical optimization problem, and it has been widely used due to its simple structure [13]. Among the fuzzy clustering algorithms based on objective function, the fuzzy C mean clustering algorithm (FCM) created by Bezdek [14] and developed by Dunn is the most typical representative one.…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Plantar Pressure Data Based on Multimodel Method in Patients with Anterior Cruciate Ligament Deficiency during Walking

Huang

Wang

et al. 2016

BioMed Research International

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The movement information of the human body can be recorded in the plantar pressure data, and the analysis of plantar pressure data can be used to judge whether the human body motion function is normal or not. A two-meter footscan® system was used to collect the plantar pressure data, and the kinetic and dynamic gait characteristics were extracted. According to the different description of gait characteristics, a set of models was established according to various people to present the movement of lower limbs. By the introduction of algorithm in machine learning, the FCM clustering algorithm is used to cluster the sample set and create a set of models, and then the SVM algorithm was used to identify the new samples, so as to complete the normal and abnormal motion function identification. The multimodel presented in this paper was carried out into the analysis of the anterior cruciate ligament deficiency. This method demonstrated being effective and can provide auxiliary analysis for clinical diagnosis.

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“…In the research area of burden surface, inter-particle percolation segregation during burden descent has been studied (Yu and Westerlund, 2011).Control model of burden surface in BF has also been established (Liu and Li, 2012).The BF burden layer distribution model based on single-point radar data has proposed by Henrik (Henrik and Jan,2010).And, model of burden distribution in BF based on evolutionary neural network has been proposed (Frank and Jan,2003). But the prediction model of burden descent speed has never been addressed.Through the survey of working conditions, it is more important for operators to master the development tread of burden layer than monitor burden shape.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Burden Descent Speed Based on Improved Extreme Learning Machine

2016

Rev. Téc. Ing. Univ. Zulia

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The shape of burden surface is an important indicator that relates closely to working condition in blast furnace. It is the most important for the operators to master the position and descent speed of burden layer, and can guide the operators for the next burden charging. This paper utilizes an advanced predictive technique, called extreme learning machine, to develop a descent speed prediction model of burden layer according to radar data and status information in blast furnace.At the same time, in order to improve generalization ability of extreme learning machine algorithm, the parameters of network are optimized by using Fishing Strategy. In the simulation part, we have collected the raw production data in iron-making process, and have obtained the satisfied results by using the proposed approach.

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Multi-model control of blast furnace burden surface based on fuzzy SVM

Cited by 28 publications

References 16 publications

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

The Analysis of Plantar Pressure Data Based on Multimodel Method in Patients with Anterior Cruciate Ligament Deficiency during Walking

Prediction of Burden Descent Speed Based on Improved Extreme Learning Machine

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