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.
|Number of pages||10|
|Journal||Journal of Environmental Informatics|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
Acknowledgments. This work was supported by Korea Institute of Industrial Technology as two projects titled “Development and applica-tionof AI based microbubble-scrubbersystem for simultaneous removal of air pollutants” [grant number kitech KM-21-0255] and “Development of hybrid model and software to optimization of ash removal system in recovery boiler for power generation” [grant number: kitech JH-21-0006].
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Environmental Science(all)
- Computer Science Applications