Process monitoring and troubleshooting of a local refinery's hydrogen plant using multivariate methods
by Mohamed Bin Shams; Abdalrahman Abdulla; Osama Khalaf; Saed Al-Tamimi
International Journal of Oil, Gas and Coal Technology (IJOGCT), Vol. 22, No. 3, 2019

Abstract: Modern refineries are equipped with distributed control systems (DCS) for monitoring and regulation. These systems generate large amount of process measurements which are acquired and stored in data historian. These measurements are characterised by being cross-correlated, with low signal to noise ratio and many missing observations in almost all variables. Multivariate-based monitoring and troubleshooting techniques provides an appropriate tool to address the aforementioned challenges. This paper demonstrates the use of principle component analysis (PCA) as an analytics tool for process monitoring and troubleshooting. A real case study from a local refinery in Bahrain was used to prove the proficiency of PCA. To demonstrate the ease and flexibility of the proposed scheme, all model buildings and testing were done using specialised commercial software, namely Aspen ProMV™ (AspenTech, 2018). The aim is to assist operators and engineers in refineries and petrochemical plants to realise the advantages of multivariate methods for troubleshooting plant and equipment upsets. The latter is necessary to assure safe, reliable and profitable operation. [Received: November 9, 2017; Accepted: May 9, 2018]

Online publication date: Tue, 15-Oct-2019

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