A novel soft sensing modelling approach for sulphur content estimation in hot metal pretreatment process

Abstract

In the hot metal pretreatment desulphurisation process, the sulphur content needs to be controlled precisely. A novel soft sensing modelling method is proposed to address the problem that sulphur content cannot be directly measured online. Firstly, the main desulphurisation behaviour is represented with the proposed simplified mechanism model for boosting computational efficiency. Moreover, the complex influence of the changing operation condition on the parameters of the simplified mechanism model is described with neural networks to compose a hybrid structured hz. Then, for training this hybrid model, a conjoint learning scheme is designed to ensure stability and reliablity. The experimental results show that the proposed hybrid model obtains higher prediction accuracy and better robustness compared with the pure mechanism modelling technique and the existing benchmark data-driven modelling methods. In addition, the proposed method is able to provide real-time prediction ability, which is substantial to the optimisation control of the hot metal pretreatment process.