A Sparse Robust Model for a Linz–Donawitz Steel Converter

Steelmaking with a Linz-Donawitz converter is a complex industrial process, where, due to the lack of exact mathematical (physical-chemical) models, the construction of a black-box model based on noisy and imprecise data is required. To construct a good model, a large number of such input-output samples should be used, which calls for a method that is sparse, in the sense that the resulting model complexity is independent of the sample number, and robust to reduce the effects of noise. Lately, support vector machines (SVMs) have successfully been applied to a number of such problems. The main problem with traditional SVM is its high algorithmic complexity, which makes it infeasible for really large databases. The least-squares SVM (LS-SVM) solves this problem, but the resulting model is not sparse. Our solution uses a sparse and robust extension of LS-SVM that leads to good results compared to other methods (such as MLPs) applied to the same problem.