In-line mixing states monitoring of suspensions using ultrasonic reflection technique

Zhan, Xueying; Yang, Yili; Jiang, Liang; Zou, Dajun; Zhang, Jiaqi; Feng, Luyi; Shi, Tielin; Li, Xiwen

doi:10.1016/j.ultras.2015.10.024

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Buurman et al [19] used non-invasive ultrasonic Doppler velocimetry to detect whether particles were suspended at the bottom of an opaque mixing vessel to monitor particle suspension. Zhan et al [20] used a non-invasive pulse-echo transducer attached to the base of the vessel to monitor particle suspension by measuring the acoustic impedance of the base-suspension interface.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Bowler

Bakalis

Watson

2020

Sensors

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Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.

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Section: Introductionmentioning

confidence: 99%