Fault propagation path estimation in NGL fractionation process using principal component analysis

- PCA model is developed to detect the process fault in chemical process industries.
- Developed model is first validated and then applied to monitor the NGL fractionation process.
- Algorithm is developed and applied to identify the fault propagation path during various system malfunctions.

Multivariate statistical methods for process monitoring are attaining a lot of attention in chemical and process industries to enhance both the process performance and safety. The fault in one process variable readily affects the other variables which makes it difficult to identify the fault variable precisely. In this study, principal component analysis (PCA) model has been developed and applied to monitor the NGL (natural gas liquid) fractionation process. Normal and fault case scenarios are developed and compared statistically to identify the fault variable and to estimate the fault propagation path in the system. The simulated NGL plant is first validated against the design data and then the developed methodology is applied to predict the fault direction by projecting the samples on the residual subspace (RS). The RS of fault data is usually superimposed by normal variations which must be eliminated to amplify the fault magnitude. The RS is further transformed into co-variance matrix followed by Singular Value Decomposition (SVD) analysis to generate the fault direction matrix corresponding to the highest eigenvalue. The process variables are further analyzed according to their magnitude of contribution towards a particular fault that in turn can be used for the determination of fault propagation path in the system. Furthermore, the applied methodology can quickly detect the fault variable irrespective of using the fault detection indices where the variable showing highest variation is most likely to be the fault variable.