Multivariate SPC of Emulsion and Nanoparticle Slurry Processes Based on Process Tomography, Dynamic Light Scattering and Acoustic Spectroscopic Data
This paper describes the use of multiple on-line sensors including electrical resistance tomography (ERT), dynamic light scattering (DLS) and ultrasound spectroscopy (USS) for real-time characterization of process operations processing emulsions and nanoparticle slurries. The focus is on making novel use of the spectroscopic data to develop multivariate statistical process control (MSPC) strategies. The ERT data at different normal operating conditions was processed using principal component analysis and used to derive two MSPC statistics, T2 and SPE (squared prediction error) for detecting abnormal changes in mixing conditions. The corresponding particle size distribution was monitored using DLS and USS. Two case studies, a sunflower oil-water emulsion system and a silica suspension system, were examined.
Dynamic light scattering Acoustic spectroscopy Process tomography Multivariate statistical process control Nanoparticle slurry Emulsion
Rui F.Li Lande Liu Xue Z.Wang Richard Tweedie Ken Primrose Jason Corbett Fraser McNeil-Watson
Institute of Particle Science and Engineering, University of Leeds, Leeds LS2 9JT, UK Industrial Tomography Systems Ltd, Speakers House, 39 Deansgate, Manchester M3 2BA, UK Malvern Instruments Ltd, Enigma Business Park, Malvern, Worcestershire WR14 1XZ, UK
国际会议
沈阳
英文
77-80
2009-08-27（万方平台首次上网日期，不代表论文的发表时间）