A comparative study of deep and shallow predictive techniques for hot metal temperature prediction in blast furnace ironmaking
Zhang X, Kano M, Matsuzakib S
Computers & Chemical Engineering 130(2): 106-575.2019
Japan
This paper provides a comparative study on the deep and shallow predictive methods for the current time and multi-step-ahead Hot Metal Temprature predictions. Three advanced deep predictive methods and seven effective shallow predictive methods are investigated from the application point of view. Both the deep and shallow predictive methods were applied to an industrial blast furnace, where the prediction performance and computational time of ten methods were evaluated. The results demonstrated that (1) shallow neural network is preferred for current time HMT prediction; (2) Gaussian process regression and support vector regression are preferred for multi-step-ahead HMT predictions.
https://www.sciencedirect.com/science/article/abs/pii/S009813541930674X