实验技术与管理2025，Vol.42Issue(4)：14-19,6.DOI:10.16791/j.cnki.sjg.2025.04.002
结合人工智能图像识别的微塑料运移与滞留微观可视化实验方法
Experimental method for the microscopic visualization of microplastic transport and retention with artificial intelligence image recognition
摘要
Abstract
[Objective]Traditional experimental methods cannot facilitate the direct observation of the migration of microplastics within porous media.To address this issue,this study developed a microscopic visualization experimental system to investigate the migration and retention of microplastics and integrated artificial intelligence for the efficient identification and calculation of microplastics.The aim was to quantify the impact of the porous media structure on the migration behavior of microplastics and provide an intuitive,accurate,simple,and scalable experimental system suitable for innovative teaching and research in environmental and related disciplines.[Methods]This study developed a microscopic visualization experimental system to deeply investigate the migration and retention behavior of microplastics in porous media by constructing pore-scale single-channel models.In the experiment,five pore-scale single-channel models with different size parameters were developed to simulate the retention of microplastics under different porosity conditions.The experimental setup included a microsyringe,a micro-infusion pump,an optical microscope,and a high-resolution camera.The microsyringe and micro-infusion pump were used to control the injection of fluids,while the optical microscope and high-resolution camera were employed to capture the migration process of microplastics in the channels.Before the experiment,ethanol was used to expel air from the channels,followed by saturation with deionized water.Then,a microplastic suspension treated with an ultrasonic processor was injected into the channels at a rate of 10 µL/h,with images captured at a rate of 2 frames per second.The obtained images were corrected and preprocessed using the ImageJ software to eliminate halos and speckles.Aiming at the small size and large quantity of microplastic particles,this study employed machine learning algorithms for image recognition and counting and developed custom script codes,which were combined with the ImageJ's macro function,to perform the automated batch analysis of data,significantly improving the efficiency and accuracy of microplastic identification,especially with accuracy rates of over 98%for dispersed individual particles and over 95%for particles aggregated in porous media.This method,which combined microscopic visualization technology with artificial intelligence image recognition,provided a novel and efficient experimental method for the study of microplastic environmental behavior.[Results]The experimental results of the microscopic visualization experiment on microplastic transport and retention provided the following conclusions.(1)The results revealed the impact of the porous media structure on the migration behavior of microplastics,with an increase in the media particle size and channel width leading to a significant increase in microplastic retention by 29.8%-56.0%and 14.5%-37.6%,respectively.These findings confirmed the key role of the porous media structure in the retention behavior of microplastics.(2)The experiment visually demonstrated the deposition patterns of microplastics in porous media,which were consistent with existing research findings,further validating the effectiveness of the experimental method.(3)By integrating artificial intelligence image recognition technology,this study developed an efficient method for the identification and counting of microplastics,significantly improving the accuracy and efficiency of data processing.This method not only provided new experimental means for environmental research and microplastic teaching but also showcased the potential application of artificial intelligence technology in the field of environmental science.[Conclusions]This study effectively quantified the impact of the porous media structure on the microplastic retention behavior through a microscopic visualization experimental system and confirmed the consistency of experimental results with existing research.By integrating artificial intelligence image recognition technology,the accuracy of microplastic identification and efficiency of data processing were significantly improved,providing an innovative experimental method for environmental science teaching and research.关键词
微塑料/孔隙尺度可视化/多孔介质/人工智能Key words
microplastics/pore-scale visualization/porous media/artificial intelligence分类
资源环境引用本文复制引用
王晓璞,白英睿,赵海龙,任玲玲,高岩岩,薛东兴,山河,王斌,姚传进,赵建..结合人工智能图像识别的微塑料运移与滞留微观可视化实验方法[J].实验技术与管理,2025,42(4):14-19,6.基金项目
国家重点研发计划(2022YFE0203400) （2022YFE0203400）
山东省自然科学基金项目(ZR2021ME108) （ZR2021ME108）
山东省本科教学改革重点项目(Z2021015) （Z2021015）
山东省本科教学改革面上项目(M2021305) （M2021305）
中国石油大学(华东)青年教师教学改革项目(QN-202003) （华东）
