Application of artificial neural networks for prediction of sinter quality based on process parameters control

Shao, Huijun; Yi, Zhengming; Chen, Zhuo; Zhou, Zheng; Deng, Zhaoqun

doi:10.1177/0142331219883501

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…For the prediction of the comprehensive indicators of sinter ore, Shao et al [ 59 ] established a BP neural network with momentum and a variable learning rate to predict the TFe content, FeO content, drum strength, and basicity. In addition, a hybrid ensemble model combining with the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm was developed to predict four indicators: solid fuel consumption, gas fuel consumption, BTP, and tumbler index (TI).…”

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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“…Neural networks that compose a large number of highly interconnected processing neurons are designed to emulate human brain (Aalizadeh, 2019; Han et al, 2018; Jiang et al, 2018; Mao et al, 2018; Shao et al, 2018; Su et al, 2020; Wang et al, 2017; Yi et al, 2019). Recurrent neural networks (RNNs) are a class of artificial neural networks and a powerful model for sequential data since their hidden state is a function of all previous hidden states (Graves et al, 2013).…”

Section: Design and Implementationmentioning

confidence: 99%

Road surface real-time detection based on Raspberry Pi and recurrent neural networks

Wang

Meng

Shang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.

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“…The prediction accuracy of the classification and regression models was observed to have good generalization ability, and accurate sinter quality indices were realized. Shao et al 20 noted the strong input–output relation in the sintering process parameters. The characteristic nonlinearity and considerable time delays in the overall sintering processes upon variations with process parameters led to the development of neural network models with high prediction accuracy and more vital self-learning ability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sinter Properties Using a Hyper-Parameter-Tuned Artificial Neural Network

et al. 2023

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The present work aims at performing prediction validation for the physical properties of coke layered and nonlayered hybrid pelletized sinter (HPS) using artificial neural networks (ANNs). Physical property analyses were experimentally performed on the two HPS products. The ANN model was then trained to obtain the best prediction results with the grid-search hyper-parameter tuning method. The learning rate, momentum constant, and the number of neurons varied over specified ranges. The binary variable conversion was utilized to assess the two sintering processes. The nonlayered HPS product of 4 mm micropellets at basicity 1.75 and using 8% coke shows a good combination of physical properties, whereas HPS of 4 mm micropellets at 1.5 basicity using 4% coke as fuel and 2% coke as layering gives a radical improvement in physical properties. The yield of the HPS product is 96.07%, with the shatter index (SI), tumbler index (TI), and abrasion index (AI) values being 86.12, 79.60, and 5.74%, respectively. Hence, HPS can be preferred by implementing the layering of coke powder. The prediction analyses showed that the multilayer perceptron model (MLP) network with a 4-29-5 structure showed prediction accuracies of over 99.99% and a mean squared error (MSE) of 2.87 × 10–4. It verifies the accuracy and prediction effectiveness of the hyper-parameter-tuned ANN model.

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Application of artificial neural networks for prediction of sinter quality based on process parameters control

Cited by 12 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

Road surface real-time detection based on Raspberry Pi and recurrent neural networks

Prediction of Sinter Properties Using a Hyper-Parameter-Tuned Artificial Neural Network

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