A New CO/CO2 Prediction Model Based on Labeled and Unlabeled Process Data for Sintering Process

Kailong Zhou, Xin Chen*, Min Wu, Sheng Du, Jie Hu, Yosuke Nakanishi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


To reduce energy consumption and harmful emission, it is of great significance to improve carbon efficiency in sintering process, which is able to be achieved if the carbon efficiency can be accurately predicted. In this article, the ratio of CO and CO$_2$ (CO/CO$_2$) is taken as a measurement of the carbon efficiency. As CO/CO$_2$ is hard to measure, and there exist multiple working conditions, multiple variables, and nonlinearity, a hybrid CO/CO$_2$ prediction model is devised based on the aforementioned characteristics. First, the sintering process is analyzed, and the key characteristics to predict the CO/CO$_2$ are extracted. Next, the configuration of the prediction model is given based on the analysis. The model consists by two submodels, one is to predict the state variables by an improved just-in-time learning model, combining three neural network (NN) models. The other is to predict CO/CO$_2$ with semisupervised algorithm, based on deep belief network with a combination of the three NN regression methods. Then, the configurations of the two submodels are introduced in detail. The test results based on actual running data exhibit the good performance of the model.

Original languageEnglish
Article number9061027
Pages (from-to)333-345
Number of pages13
JournalIEEE Transactions on Industrial Informatics
Issue number1
Publication statusPublished - 2021 Jan


  • CO/CO2
  • deep belief network (DBN)
  • improved just-in-time learning (JITL)
  • neural network (NN)
  • sintering process

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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