Abstract: It is well known that to measure and estimate the chemical process variables in time has vital significance in ensuring process stabilization and effectively controlling its product quality.In this study, a soft sensor model of a chemical process was established by partial least squares method based on its time series data, and the model could be adjusted in the block-wise recursive way in the presence of new sample data. With a view to the time series data characteristics,a strategy of allotting different weight coefficients to the time series data was introduced, and an approach of how to ascertain the weight coefficients was provided in the meantime.Subsequently, the weighted block-wise recursive partial least squares regression (WBRPLSR) algorithm was developed and used to model the water content of solvent dehydration tower bottom drainage in a commercial purified terephthalic acid (PTA)unit.The experimental result showed that the algorithm was rapid and effective.Compared with some other methods, the WBRPLSR method increased modeling efficiency and improved prediction performance.