Abstract
In this work, improvements to a model predictive controller in an industrial facility is presented. A depropaniser column used to separate propane and propylene from heavier components was experiencing sporadic process instabilities which was impacting product purities. A soft sensor to measure C4 hydrocarbons was inaccurately predicting the actual composition, which caused the controller to behave erratically. A simple linear regression implementation improved the accuracy of the soft sensor significantly, which allowed further optimisation of the controller. Large benefits in process stability and propylene recovery were observed.
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