Soft-sensor is the most common strategy to estimate the hard-to-measure variables in the chemical processes. Recent research has shown that accurate prediction of hard-to-measure variables can significantly improve system performance. However, deterioration of predictive ability resulting from dramatic changes in the operation conditions always renders a generic soft-sensor inadequate. This study developed an adaptive soft-sensor with Moving Window and Time Differencing technique accounting for both of long-term and short-term information for modeling. At each step of model update, the most insensitive variables were removed by VIP (Variable importance in projection). With further integrating Variational Bayesian PLS (VBPLS) as predictive model, not just prediction values are obtained but also the credibility of information for hard-to-measure quantities can be generated. The proposed methodology was first demonstrated by applying the design algorithm to a WWTP simulated with the well-established model, BSM1, then extended to a real WWTP with data collecting from the field. Results showed that the proposed strategy significantly improved the prediction performance.