Y. Yoo, Green Materials, H. Park, Y. Choi, J. Jung, H. Song, J. Kim, H. Cho
{"title":"利用图像处理确定微泡洗涤器最佳操作条件的方法","authors":"Y. Yoo, Green Materials, H. Park, Y. Choi, J. Jung, H. Song, J. Kim, H. Cho","doi":"10.3808/jei.202100457","DOIUrl":null,"url":null,"abstract":"This paper presents an image-processing-based model for calculating the interfacial-area concentration (IAC) of a low-pressure microbubble (LPMB) scrubber, which facilitates the determination of operational conditions of the scrubber via flow-pattern analysis. The LPMB scrubber maximizes the interfacial area of two-phase systems using the bubbly flow. Microbubbles have received attention due to their microscopic sizes, high residence time, and high mass-transfer efficiency. The LPMB scrubber maintains a negative outlet pressure to generate gas flow, which in turn generates microbubbles interrupting gas flow with three blocking plates in the atomizer. This gas flow generates a bubbly flux with different bubble sizes. To obtain bubble characteristics, we analyzed 20 atomizer images where this complex flux occurs. Bubble size, number of bubbles, gas void fraction, and IAC were calculated using an Open-CV Python algorithm. To validate the most appropriate bubble flow patterns, case studies were conducted at pressure difference of 240, 360, and 450 mmAq. The 360 mmAq condition had the lowest percentage of bubbles smaller than 50 µm, but the total number of bubbles, void fraction, and IAC were the highest. The results obtained in this study confirm that using an LPMB scrubber in an oxidizing solution facilitates reductions of 92.6, 93.9, and 99.9% in NOX, SOX, and dust, respectively. These results could be used to validate the bubble reactivity of other two-phase systems intended for commercial and practical applications.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Method for Determining Optimum Operational Conditions of Microbubble Scrubber Using Image Processing\",\"authors\":\"Y. Yoo, Green Materials, H. Park, Y. Choi, J. Jung, H. Song, J. Kim, H. Cho\",\"doi\":\"10.3808/jei.202100457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an image-processing-based model for calculating the interfacial-area concentration (IAC) of a low-pressure microbubble (LPMB) scrubber, which facilitates the determination of operational conditions of the scrubber via flow-pattern analysis. The LPMB scrubber maximizes the interfacial area of two-phase systems using the bubbly flow. Microbubbles have received attention due to their microscopic sizes, high residence time, and high mass-transfer efficiency. The LPMB scrubber maintains a negative outlet pressure to generate gas flow, which in turn generates microbubbles interrupting gas flow with three blocking plates in the atomizer. This gas flow generates a bubbly flux with different bubble sizes. To obtain bubble characteristics, we analyzed 20 atomizer images where this complex flux occurs. Bubble size, number of bubbles, gas void fraction, and IAC were calculated using an Open-CV Python algorithm. To validate the most appropriate bubble flow patterns, case studies were conducted at pressure difference of 240, 360, and 450 mmAq. The 360 mmAq condition had the lowest percentage of bubbles smaller than 50 µm, but the total number of bubbles, void fraction, and IAC were the highest. The results obtained in this study confirm that using an LPMB scrubber in an oxidizing solution facilitates reductions of 92.6, 93.9, and 99.9% in NOX, SOX, and dust, respectively. 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Method for Determining Optimum Operational Conditions of Microbubble Scrubber Using Image Processing
This paper presents an image-processing-based model for calculating the interfacial-area concentration (IAC) of a low-pressure microbubble (LPMB) scrubber, which facilitates the determination of operational conditions of the scrubber via flow-pattern analysis. The LPMB scrubber maximizes the interfacial area of two-phase systems using the bubbly flow. Microbubbles have received attention due to their microscopic sizes, high residence time, and high mass-transfer efficiency. The LPMB scrubber maintains a negative outlet pressure to generate gas flow, which in turn generates microbubbles interrupting gas flow with three blocking plates in the atomizer. This gas flow generates a bubbly flux with different bubble sizes. To obtain bubble characteristics, we analyzed 20 atomizer images where this complex flux occurs. Bubble size, number of bubbles, gas void fraction, and IAC were calculated using an Open-CV Python algorithm. To validate the most appropriate bubble flow patterns, case studies were conducted at pressure difference of 240, 360, and 450 mmAq. The 360 mmAq condition had the lowest percentage of bubbles smaller than 50 µm, but the total number of bubbles, void fraction, and IAC were the highest. The results obtained in this study confirm that using an LPMB scrubber in an oxidizing solution facilitates reductions of 92.6, 93.9, and 99.9% in NOX, SOX, and dust, respectively. These results could be used to validate the bubble reactivity of other two-phase systems intended for commercial and practical applications.
期刊介绍:
Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include:
- Planning of energy, environmental and ecological management systems
- Simulation, optimization and Environmental decision support
- Environmental geomatics - GIS, RS and other spatial information technologies
- Informatics for environmental chemistry and biochemistry
- Environmental applications of functional materials
- Environmental phenomena at atomic, molecular and macromolecular scales
- Modeling of chemical, biological and environmental processes
- Modeling of biotechnological systems for enhanced pollution mitigation
- Computer graphics and visualization for environmental decision support
- Artificial intelligence and expert systems for environmental applications
- Environmental statistics and risk analysis
- Climate modeling, downscaling, impact assessment, and adaptation planning
- Other areas of environmental systems science and information technology.