Loughborough University
Browse
Aamir_et_al_2008_ISIC.pdf (349.82 kB)

Efficient crystal size distribution estimation approach for growth dominated crystallisation processes

Download (349.82 kB)
conference contribution
posted on 2009-06-01, 10:35 authored by Erum Aamir, Zoltan NagyZoltan Nagy, Chris RiellyChris Rielly, T. Kleinert, B. Judat
The paper presents a novel methodology for the estimation of the shape of crystal size distribution (CSD) during crystallization processes. The approach is based on the combination of quadrature method of moment (QMOM) and method of characteristics (MOCH). The computationally efficient solution of the population balance equation allows the fast prediction of the dynamic evolution of the CSD for the entire batch. Furthermore, under the assumption that for supersaturation controlled crystallization the main phenomenon is growth, an analytical CSD estimator is derived for generic size dependent growth kinetics. The approaches are evaluated for the crystallization of potassium alum in water. The model parameters are identified based on industrial experimental data, obtained using an efficient implementation of supersaturation control. The proposed approach is able to predict and reconstruct the dynamic evolution of the CSD during the batch. The technique can serve as a soft sensor for predicting CSD or as a computationally efficient algorithm for off-line design or on-line adaptation of operating policies based on full CSD data.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Citation

AAMIR, E. ... et al, 2008. Efficient crystal size distribution estimation approach for growth dominated crystallisation processes. 17th International Symposium on Industrial Crystallization, September 14th-17th, Maastricht, The Netherlands.

Publication date

2008

Notes

This is a conference paper. It was presented at ISIC 17.

Language

  • en

Usage metrics

    Loughborough Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC