Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed

and soft sensor systems exhibit multi-dynamic characteristics. Thus

the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way

and the stochastic Newton recursive (SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems

the proposed model cannot be identified directly. Thus

two auxiliary models are first proposed to offer the intersample estimations at each update period

based on which the improved algorithm (DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm

and the identification procedure is then presented. Finally

the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.