کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
672122 887486 2011 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی جریان سیال و فرایندهای انتقال
پیش نمایش صفحه اول مقاله
Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR
چکیده انگلیسی

Near infrared spectroscopy (NIR) is now probably the most popular process analytical technology (PAT) for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm (GA) and support vector machine (SVM). GA is used for wavelength selection and SVM for mode building. The new GA–SVM approach is shown to outperform other methods including GA–PLS (partial least squares) and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of l-glutamic acid.

Near infrared spectroscopy (NIR) spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was used to develop predictive models through combining genetic algorithm (GA) for wavelength selection and support vector machine (SVM) for mode building. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of l-glutamic acid.Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Particuology - Volume 9, Issue 6, December 2011, Pages 589–597
نویسندگان
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