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- W2783199868 abstract "In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this study proposes a data-based predictive optimization (DPO) method to carry out real-time adjusting for the gas system. Two stages of the method, namely the prediction modeling and real-time optimization, are involved. At the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation and the tank levels, are forecasted based on a proposed mixed Gaussian kernel-based prediction intervals (PIs) construction model. The Jacobian matrix of this model is represented by a kernel matrix through derivation, which greatly facilitates the subsequent calculation. At the second stage, a rolling optimization based on a mathematical programming technique involving continuous and integer decision-making variables is developed via the prediction intervals." @default.
- W2783199868 created "2018-01-26" @default.
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- W2783199868 date "2017-08-01" @default.
- W2783199868 modified "2023-09-23" @default.
- W2783199868 title "Data-based predictive optimization for by product gas system in steel industry" @default.
- W2783199868 doi "https://doi.org/10.1109/coase.2017.8256081" @default.
- W2783199868 hasPublicationYear "2017" @default.
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