Automatic image-based estimation of texture analysis as a monitoring tool for crystal growth

Online monitoring and feedback control are crucial elements in a commercial crystallization operation because they ensure that key production variables are closely regulated so as to achieve specified textural and physical properties of the end-product. Digital image texture analysis is a promising method in monitoring and control systems, and is becoming increasingly more attractive due to availability of high speed imaging devices and equally powerful computers. This paper investigates the use of texture analyses in the form of fractal dimension (FD) and energy signatures as characteristic parameters to track the crystal growth. This methodology deals with issues such as touching and overlapping problem in crystal images which limit available off-line and on-line imaging techniques. The algorithm uses a combination of thresholding and wavelet-texture analysis. The thresholding method is used to identify crystal clusters and remove empty backgrounds. Wavelet–fractal and energy signatures are performed afterwards to estimate texture on crystal clusters. A series of images obtained at different crystal growth stages during a NaCl–water–ethanol anti-solvent crystallization system is investigated and their texture characteristics as well as transform tendency during the crystallization process are evaluated.

► Fractal dimension and energy signatures as parameters to track crystal growth. ► Uses combination of thresholding and wavelet-texture algorithms. ► Thresholding method identifies crystal clusters and removes empty backgrounds. ► Wavelet-fractal and energy signatures estimate texture on crystal clusters. ► Validated for anti-solvent crystallization.