Optimization of coke ratio for the second proportioning phase in a sintering process base on a model of temperature field of material layer

Wu, Min; Ma, Jinfei; Hu, Jie; Chen, Xin; Cao, Weihua; She, Jinhua

doi:10.1016/j.neucom.2017.05.003

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…Subsequently, to reduce the energy consumption, an optimization model for the second proportioning phase was established, and PSO was used to obtain the optimal coke ratio. [ 94 ] Besides, Wang et al [ 46 ] proposed a novel cascade multi‐objective optimization model (CMOM) by introducing energy indicators into the sintering proportioning process. The other sintering proportioning studies are in these references.…”

Section: Review Of Data‐driven Methods In the Sintering Processmentioning

confidence: 99%

Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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The sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modelling methods in the sintering process have increasingly received the researchers' attention. But now, there is still no systematic review of the data‐driven modelling approaches in the sintering process. Therefore, in this article, we conduct a comprehensive overview and prospects on the data‐driven models for the purpose of intelligent sintering. First, the mechanism and characteristics of the sintering process are introduced and analyzed elaborately. Second, the detailed research status of the sintering process is illustrated from four aspects: key parameters prediction, control, optimization, and others. Finally, several challenges and promising modelling methods such as deep learning in the sintering process are outlined and discussed for future research.

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Section: Review Of Data‐driven Methods In the Sintering Processmentioning

confidence: 99%

Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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“…With the application of moisture meters developed based on infrared, microwave, and neutron principles in sintering plants, more refined data have led to the study of various optimization methods for sintering processes. For example, Wu et al added many independent variables, such as material layer thickness and combustion rate, in the process of modeling the temperature field and subsequently advanced a three-step optimization strategy to optimize the coke ratio [3]. Meanwhile, Liu et al emphasized the porous media heat transfer model in the process of building the sintering temperature field model and used a particle swarm optimization algorithm to solve the problem of parameter matching [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Analysis and modeling of the dynamic, non-linear, and other characteristics of the predicted object were performed for the two-stage mixing and water addition method commonly used in sintering plants. 3.…”

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Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

Ren

Huang

Jiang

et al. 2022

Metals

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The design and optimization of a sinter mixture moisture controlling system usually require complex process mechanisms and time-consuming field experimental simulations. Based on BP neural networks, a new KPCA-GA optimization method is proposed to predict the mixture moisture content sequential values with time more accurately so as to derive the optimal water addition to meet industrial requirements. Firstly, the normalized input variables affecting the output were dimensionalized using kernel principal component analysis (KPCA), and the contribution rates of the factors affecting the water content were analyzed. Then, a BP neural network model was established. In order to get rid of the randomness of the initial threshold and weights on the prediction accuracy of the model, a genetic algorithm is proposed to preferentially find the optimal initial threshold and weights for the model. Then, statistical indicators, such as the root mean square error, were used to evaluate the fit and prediction accuracy of the training and test data sets, respectively. The available experimental data show that the KPCA-GA model has high fitting and prediction accuracy, and the method has significant advantages over traditional neural network modeling methods when dealing with data sets with complex nonlinear characteristics, such as those from the sintering process.

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A blast furnace coke ratio prediction model based on fuzzy cluster and grid search optimized support vector regression

Chang

Chu

et al. 2022

Appl Intell

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Optimization of coke ratio for the second proportioning phase in a sintering process base on a model of temperature field of material layer

Cited by 11 publications

References 21 publications

Data‐driven modelling methods in sintering process: Current research status and perspectives

Data‐driven modelling methods in sintering process: Current research status and perspectives

Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

A blast furnace coke ratio prediction model based on fuzzy cluster and grid search optimized support vector regression

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