Soft sensing of calcination zone temperature of lime rotary kiln based on principal component analysis and stochastic configuration networks

The combustion state of the lime rotary kiln is mainly reflected in the temperature of the calcination zone. However, due to the high temperature in the calcination zone during the production process, it is not possible to directly measure using instruments. This paper proposes a soft sensing strategy to address the issue of difficulty in measuring the temperature of the calcination zone of a lime rotary kiln. Firstly, due to significant disturbances and coupling of production process variables, principal component analysis is used to reduce the input dimension and computational complexity of the data, thereby improving the efficiency of the soft sensing model. Then, stochastic configuration networks with good predictive performance is used to model and predict the principal components. Meanwhile, an improved Hammersley-based Salp swarm algorithm is proposed to optimize the key parameter regularization factor of the stochastic configuration networks. Finally, the validity of the soft sensing model is verified by the actual collected production data of the lime rotary kiln. Multiple evaluation indicators and comparison results indicate that the proposed soft sensing model for the calcination zone temperature of lime rotary kilns has higher prediction accuracy and modeling quality compared to other models.