This study investigates flux decline in ultrafiltration as a capacity measure for the process. A continuous ultrafiltration is a multistage process where a considerable coupling between the stages is expected due to similar settings on the subsequent recirculation loops and recirculation of parts of the process streams. To explore the flux decline issue from an engineering perspective, two ways of organizing process signals into logical blocks are identified and used in a multiblock partial least-squares regression: (1) the "physical location" of the sensors on the process layout and (2) "engineering type of tags". Abnormal runs are removed iteratively from the original data set, and then the multiblock parameters are calculated based on the optimized regression model to determine the role of the different data building units in flux prediction. Both blocking alternatives are interpreted alongside offering a compact overview of the most important sections related to the flux decline. This way one can zoom in on the smaller sections of the process, which gives an optimization potential.