A novel backlight fiber optical probe and image algorithms for real time size-shape analysis during crystallization

Arnaout, Toufic El; Cullen, Patrick J.; Sullivan, Carl

doi:10.1016/j.ces.2016.04.025

Cited by 31 publications

(22 citation statements)

References 31 publications

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“…for example and the preceding figures were reasonable. In previous studies , , the sensors (Fig. ) and image analysis principle proved successful in determining sizes and shapes based on specific size‐shape calculations of commercial particle size standards of known shapes, up to certain concentrations suitable for automated image analysis.…”

Section: Resultsmentioning

confidence: 99%

“…(b) An upgraded design incorporating a relief contrast optical method for the enhanced visualization of texture and thin/transparent particles. Some figures were adapted , .…”

Section: Introductionmentioning

confidence: 99%

“…Having tested several open‐access functions for background correction or normalization for this study and in previous ones , , one of the most useful methods that was identified is called the ‘rolling ball' method . The approach can auto‐adjust to gradients within the same image, random depths, and large variations in the 2D baseline.…”

Section: Introductionmentioning

confidence: 99%

“…2D images for moving particles can be obtained directly from the process (Fig. ) by employing engineered, immersible microscopy sensors using several optical methods , , while the full 3D morphology is possible via offline controlled setups . In one of the in situ sensors (Fig.…”

Section: Introductionmentioning

confidence: 99%

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In situ Image Processing and Data Binning Strategy for Particle Engineering Applications

Arnaout

Cullen

2020

Chem Eng & Technol

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Crystallization control can be improved through real-time monitoring technologies. Here, a workflow is demonstrated on rapid batch cooling crystallization of L-glutamic acid. First, in situ images were generated using video microscopy sensors and analyzed, by employing a single, rapid macro code to extract particle data descriptors. A binning procedure (over time) was performed, where every data point represented the counts of particles within a specific size or shape range per 100 images. This binning method was found more informative in tracking of the populations compared to whole image averages or individual particle datapoints. This study provides a step-by-step guide towards improving mechanistic modeling, control via feedback, automation, and continuous manufacturing for Industry 4.0.

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Section: Resultsmentioning

confidence: 99%

“…(b) An upgraded design incorporating a relief contrast optical method for the enhanced visualization of texture and thin/transparent particles. Some figures were adapted , .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In situ Image Processing and Data Binning Strategy for Particle Engineering Applications

Arnaout

Cullen

2020

Chem Eng & Technol

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“…R. Baratti made use of the fractal dimension (FD) in the feature analysis and energy features as input to the artificial neural network, using threshold method and wavelet algorithm to remove the image background [11]. T. E. Arnaout designed a rolling ball adaptive threshold algorithm combined with a sliding paraboloid of rotation to segment particles from the background, the algorithm is robust without using Canny edge detection and watershed algorithm [12]. B. Zhang proposed a novel image texture method based on threshold and wavelet energy analysis.…”

Section: Introductionmentioning

confidence: 99%

Crystal Morphology Monitoring based on In-situ Image Analysis of L-glutamic Acid Crystallization

Lu¹,

Zhang²,

Yong-ming³

et al. 2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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In the process of chemical industry and biological pharmacy, the morphology of the crystal plays a vital role; different morphological characteristics such as shape and size have a direct impact on production. Therefore, this paper proposes a synthetic in-situ image analysis method for monitoring the morphology of crystals based on using an invasive imaging system. The proposed method includes image preprocessing, feature extraction, morphological identification and agglomeration re-segmentation. The in-situ image is first pre-processed to eliminate the effects of water droplets, particle shadows, and uneven illumination. Then, texture features are extracted by using Improved-Basic Gray Level Aura Matrix (I-BGLAM) for different types of particles, and shape features are extracted by using image descriptors. Afterwards, the extracted features are used to morphologically identify the different particles. At last, the salient corners of the particles are detected, and a segmentation algorithm that separates individual crystals from the agglomerates is constructed by clustering the same type of salient corners. The case study and experimental results of L-glutamic acid cooling crystallization show that the image analysis method can be effectively used for morphology analysis of in-situ crystals with good precision.

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Predictive control of particlesize distribution of crystallization process using deep learning based image analysis

2022

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The challenges to regulate the particle‐size distribution (PSD) stem from on‐line measurement of the full distribution and the distributed nature of crystallization process. In this article, a novel nonlinear model predictive control method of PSD for crystallization process is proposed. Radial basis function neural network is adopted to approximate the PSD such that the population balance model with distributed nature can be transformed into the ordinary differential equation (ODE) models. Data driven nonlinear prediction model of the crystallization process is then constructed from the input and output data and further be used in the proposed nonlinear model predictive control algorithm. A deep learning based image analysis technology is developed for online measurement of the PSD. The proposed PSD control method is experimentally implemented on a jacketed batch crystallizer. The results of crystallization experiments demonstrate the effectiveness of the proposed control method.

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A novel backlight fiber optical probe and image algorithms for real time size-shape analysis during crystallization

Cited by 31 publications

References 31 publications

In situ Image Processing and Data Binning Strategy for Particle Engineering Applications

In situ Image Processing and Data Binning Strategy for Particle Engineering Applications

Crystal Morphology Monitoring based on In-situ Image Analysis of L-glutamic Acid Crystallization

Predictive control of particlesize distribution of crystallization process using deep learning based image analysis

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