Ultrasonic attenuation spectroscopy for multivariate statistical process control in nanomaterial processing--颗粒学报PARTICUOLOGY

Boonkhao, B., & Wang, X. Z. (2012). Ultrasonic attenuation spectroscopy for multivariate statistical process control in nanomaterial processing. Particuology, 10(2), 196–202. https://doi.org/10.1016/j.partic.2011.11.009

Ultrasonic attenuation spectroscopy for multivariate statistical process control in nanomaterial processing

Bundit Boonkhao, Xue Z. Wang^*

Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds, LS2 9JT, UK

10.1016/j.partic.2011.11.009

Volume 10, Issue 2, April 2012, Pages 196-202

Received 6 August 2011, Revised 11 November 2011, Accepted 26 November 2011, Available online 15 March 2012.

E-mail: x.z.wang@leeds.ac.uk

Highlights

► Ultrasound attenuation spectroscopy (UAS) is used on-line for direct product quality control in nanomaterials processing.

► UAS raw spectra are used to derive multivariate statistical process control (MSPC) charts for monitoring nanoparticle quality.

► It avoids the difficulty associated with errors in estimating particle size distribution at high solid concentrations.

► The method is demonstrated using a wet milling process for size reduction of aluminum oxide particles.

Abstract

Ultrasonic attenuation spectroscopy (UAS) is an attractive process analytical technology (PAT) for on-line real-time characterisation of slurries for particle size distribution (PSD) estimation. It is however only applicable to relatively low solid concentrations since existing instrument process models still cannot fully take into account the phenomena of particle–particle interaction and multiple scattering, leading to errors in PSD estimation. This paper investigates an alternative use of the raw attenuation spectra for direct multivariate statistical process control (MSPC). The UAS raw spectra were processed using principal component analysis. The selected principal components were used to derive two MSPC statistics, the Hotelling's T² and square prediction error (SPE). The method is illustrated and demonstrated by reference to a wet milling process for processing nanoparticles.

Graphical abstract

Keywords

Ultrasonic attenuation spectra; Particle size; Multivariate statistical process control (MSPC); Wet milling process; Nanoparticle processing

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