Encapsulated inorganic particles with high melting points (>300°C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric and thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.
Material flow detection on conveyor based on machine vision is the research topic of this paper. A belt conveyor system equipped with a camera and micro-controller is used as the test apparatus. The purpose of this experiment is to obtain the quantity of material on the conveyor belt using machine vision and then develop an intelligent speed adjustment system for belt conveyor according to the quantity, so as to avoid waste of energy and reduce the wear of the conveyor. Three image processing algorithms that developed, applied and compared were: 1) Background Subtraction; 2) Canny edge detection and morphological operations; 3) Particle analysis using. It is observed that all three methods perform well for material detection on the conveyor belt. However, the particle analysis method resulted in higher reliability and accuracy with faster processing speed. The research provides new developmental ideas for intelligent conveyor systems.
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