title: Particle Morphology Analysis of Biomass Material Based on Improved Image Processing Method.
abstract: Particle morphology, including size and shape, is an important factor that significantly influences the physical and chemical properties of biomass material. Based on image processing technology, a method was developed to process sample images, measure particle dimensions, and analyse the particle size and shape distributions of knife-milled wheat straw, which had been preclassified into five nominal size groups using mechanical sieving approach. Considering the great variation of particle size from micrometer to millimeter, the powders greater than 250μm were photographed by a flatbed scanner without zoom function, and the others were photographed using a scanning electron microscopy (SEM) with high-image resolution. Actual imaging tests confirmed the excellent effect of backscattered electron (BSE) imaging mode of SEM. Particle aggregation is an important factor that affects the recognition accuracy of the image processing method. In sample preparation, the singulated arrangement and ultrasonic dispersion methods were used to separate powders into particles that were larger and smaller than the nominal size of 250μm. In addition, an image segmentation algorithm based on particle geometrical information was proposed to recognise the finer clustered powders. Experimental results demonstrated that the improved image processing method was suitable to analyse the particle size and shape distributions of ground biomass materials and solve the size inconsistencies in sieving analysis.
authors.affiliation: Advanced Analysis and Computation Center, China University of Mining and Technology, Xuzhou 221116, China.
authors.forename: Zhaolin
authors.initials: Z
authors.lastname: Lu
authors.affiliation: School of Sciences, China University of Mining and Technology, Xuzhou 221116, China.
authors.forename: Xiaojuan
authors.identifier: 0000-0001-5037-4615
authors.initials: X
authors.lastname: Hu
authors.affiliation: Advanced Analysis and Computation Center, China University of Mining and Technology, Xuzhou 221116, China.
authors.forename: Yao
authors.identifier: 0000-0003-4883-3733
authors.initials: Y
authors.lastname: Lu