Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Perot interferometer

TALANTA

卷:224

Article

Avila, Claudio¹  Mantzaridis, Christos²  Ferre, Joan³  Rocha de Oliveira, Rodrigo⁴  Kantojarvi, Uula⁵  Rissanen, Anna⁶  Krassa, Poppy²  de Juan, Anna⁴  Muller, Frans L.¹  Hunter, Timothy N.¹  Bourne, Richard A.¹ 

[1] Univ Leeds, Sch Chem & Proc Engn, Leeds LS2 9JT, W Yorkshire, England

[2] Megara Resins, 38th Km New Natl Rd, Athens 19100, Megara, Greece

[3] Univ Rovira & Virgili, Dept Analyt Chem & Organ Chem, Tarragona 43007, Spain

[4] Univ Barcelona, Dept Chem Engn & Analyt Chem, Barcelona 08028, Spain

[5] Spectral Engines Oy, Kutomotie 16, Helsinki, Finland

[6] VTT Tech Res Ctr Finland, Tietotie 3, Espoo, Finland

关键词: Near infrared spectroscopy;    MEMS Fabry-Perot interferometer;    Online process monitoring;    High temperature polymerisation;    Saturated polyester resin;    Chemometrics;   

DOI  :  10.1016/j.talanta.2020.121735

来源: Elsevier

【 摘 要 】

Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Perot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 degrees C), using an immersion NIR transmission probe. Because of the multiphase nature of the reaction, strong interference produced by process disturbances such as temperature variations and the presence of solid particles and bubbles in the online spectra required robust preprocessing algorithms and a good long-term stability of the probe. These allowed partial least squares (PLS) regression models to be built for the key analytical parameters acid number and viscosity. In parallel, spectra were also used to build an end-point detection model based on principal component analysis (PCA) for multivariate statistical process control (MSPC). The novel MEMS-FPI sensor combined with robust chemometric analysis proved to be a suitable and affordable alternative for online process monitoring, contributing to sustainability in the process industry.

【 授权许可】

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