Multivariate statistical process control charts for batch monitoring of transesterification reactions for biodiesel production based on near-infrared spectroscopy. (2nd November 2016)

• Record Type: Journal Article
• Authors: Sales, Rafaella Figueiredo; Vitale, Raffaele; de Lima, Suzana Moreira; Pimentel, Maria Fernanda; Stragevitch, Luiz; Ferrer, Alberto
• Is Part Of: Computers & chemical engineering. Volume 94(2016)
• Page Start: 343
• Page End: 353
• Publication Date: 2016-11-02
• Subjects: Biodiesel -- Batch process monitoring -- Near infrared spectroscopy (NIRS) -- Multivariate statistical process control (MSPC) -- Soybean oil methanolysis; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.08.013
• Languages: English
• ISSNs: 0098-1354

Abstract

Graphical abstract: Highlights: Transesterification reactions monitored using in-line NIR spectroscopy. Multivariate control charts for batch monitoring developed using spectroscopy data. Control charts constructed based on two techniques: NM and WKFH. Monitoring schemes applied for off- and on-line fault detection. Abstract: This work describes an application of Multivariate Statistical Process Control to monitor soybean oil transesterification. For the development of multivariate control charts, near infrared spectra were acquired in-line during the evolution of ten batches under Normal Operating Conditions. They were then organized in a three-way array (batch í spectral variable í time). This structure was analysed by the two most commonly used approaches to develop batch monitoring schemes for handling such kind of data, referred to as Nomikos-MacGregor (NM) and Wold-Kettaneh-Friden-Holmberg (WKFH), respectively. To assess the performance of the approaches, eight test batches, during which specific interferences were induced, were manufactured. When applied for off-line monitoring, both NM and WKFH correctly pointed out such intentionally produced failures. On the other hand, concerning on-line monitoring, NM exhibited a better fault detection capability than WKFH. Contribution plots were found to highlight the spectral region mostly affected by the disturbances regardless of the modelling strategy resorted to.