Robust processes through latent variable modeling and optimization

A data‐based approach for developing robust processes is presented and illustrated with an application to an industrial membrane manufacturing process. Using historical process data, principal component analysis and partial least squares are used to extract models of the process and of the sensitivities of the process to various disturbances, including raw material variations, environmental conditions, and process equipment differences. Robustness measures are presented to quantify the robustness of the process to each of these disturbances. The process is then made robust (insensitive) to the disturbances over which one has some control (e.g., by modifying the equipment units to which the process is sensitive and imposing specification regions on sensitive raw materials). It is also made robust to disturbances over which one has little control (e.g., environmental variations) by optimizing the process operating conditions with respect to performance and robustness measures. The optimization is easily performed in the low‐dimensional space of the latent variables even though the number of process variables involved is very large. After applying the methodology to historical data from the membrane manufacturing process, results from several months of subsequent operation are used to demonstrate the large improvement achieved in the robustness of the process. © 2010 American Institute of Chemical Engineers AIChE J, 2011