Advanced Wastewater Analysis: AI-Integrated Flow Injection Analysis (FIA) System for COD Online Monitoring

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陳韻天(Yun-Tien Chen) 查詢紙本館藏

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環境工程研究所在職專班

論文名稱

(Advanced Wastewater Analysis: AI-Integrated Flow Injection Analysis (FIA) System for COD Online Monitoring)

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摘要(中)

在面臨全球暖化、氣候調節逐漸異常，聯合國永續發展目標（Sustainable Development Goals, SDGs）的執行，與水資源永續使用的需求更加突出。透過廢水水質自動監控技術的發展，利用有限的人力達到廢水處理系統的自動化與最佳化，以提升系統之穩定性及處理效率，已然成為發展的趨勢。
本研究旨在驗證人工智慧化學需氧量（AICODOMS）線上監測系統，在監測工業廢水化學需氧量方面的有效性及系統可靠度。該系統結合了流動注射分析（FIA）與人工智慧（AI）的科技，用以檢測水體中的化學需氧量。AICODOMS利用AI的影像識別及學習功能，自動判斷滴定終點，以此減少手動滴定及人為操作可能的誤差，也提升檢測的準確性和可靠性。
本研究針對高雄市某塑膠製造廠廢水廠放流水之化學需氧量作為實驗基質，進行兩個階段的驗證實驗。首先，針對不同濃度的化學需氧量標準溶液、廢水廠之河放汙水、海放汙水樣本，分別進行八重複的試驗。結果顯示AICODOMS與水樣的已知濃度具有高度的一致性和可靠性。第二階段，將AICODOMS直接安裝於工廠的廢水放流口進行即時線上測試，並將其結果與實驗室的分光光度計與第三方實驗室的重鉻酸鉀滴定法進行比較。AICODOMS在廢水廠河放口中與紫外光分光光度計（R2 = 0.9239）和滴定法（R2 = 0.9446）的檢測結果均顯示高度相關性。另外，針對海放口的驗證，與分光光度計（R2 = 0.9175）和滴定法（R2 = 0.9017）的結果也呈高度相關性，具有一定程度之穩定性及準確性。

摘要(英)

Environmental engineering evolves to tackle modern challenges, with this study focusing on validating the artificial intelligence chemical oxygen demand online monitoring system (AICODOMS). AICODOMS, combining flow injection analysis (FIA) and artificial intelligence (AI), precisely assesses chemical oxygen demand (COD) in aquatic environments.

This research aims to verify AICODOMS′ effectiveness in monitoring COD levels within industrial effluents. Comparative analyses with field datasets highlight its potential as an alternative to traditional COD assessment methods. It demonstrates precision and reliability, reducing human-induced errors and offering applications in industrial wastewater management, emergency response, and innovative AI detection techniques.

The study encompasses two experimental phases using wastewater samples from a plastic manufacturing facility. Initially, AICODOMS was validated using treated wastewater and standard COD solutions, including real effluent samples discharged into the river and sea. Strong agreement between AICODOMS and established methods validated its reliability. Additionally, AICODOMS showed strong correlations with spectrophotometric and titrimetric methods for both river (R2 = 0.9239, R2 = 0.9446) and sea discharge (R2 = 0.9175, R2 = 0.9017). In addition, AICODOMS successfully integrates a chloride ion removal technology that can precisely measure COD under high chloride concentrations.

The integration of AI-driven color recognition technology with FIA holds promise for efficient and eco-friendly industrial wastewater analysis. This amalgamation could revolutionize assessment methodologies, enhancing efficiency and environmental sustainability in industrial analysis.

關鍵字(中)

★ 化學需氧量
★ 化學需氧量線上監測系統
★ 流動注射分析
★ 人工智慧

關鍵字(英)

★ Environmental Engineering
★ Chemical Oxygen Demand
★ Artificial Intelligence
★ Flow Injection Analysis
★ Industrial Effluents
★ Chloride Ion Removal

論文目次

List for Figures V
List for Tables VI
Chapter Ⅰ. Introduction 1
Chapter Ⅱ. Literature Review 4
Chapter Ⅲ. Materials and Methods 16
3.1 Flow injection analysis system 17
3.2 Sequential Procedural Overview of the Measurement Process 17
3.3 Reduction of chloride ion interference in COD 18
3.4 AI-enhanced analytical methods for wastewater assessment 20
3.5 Experimental design for AICODOMS validation 22
Chapter Ⅳ. Results and Discussion 24
4.1 Validating and optimizing the AICODOMS 24
4.2 Analysis of industrial wastewater effluent by AICODOMS 29
4.3 Troubleshooting during the experiment 33
4.4 Discussion 34
Chapter Ⅴ. Conclusion and Suggestions 36
5.1 Conclusion 36
5.2 Suggestion 37
References 38

參考文獻

′Applications of artificial intelligence in water treatment for the optimization and automation of the adsorption process: Recent advances and prospects′, Chemical Engineering Journal: 130011.
2. Chailapakul, Orawon, Passapol Ngamukot, Alongkorn Yoosamran, Weena Siangproh, and Nattakarn Wangfuengkanagul. 2006. ′Recent Electrochemical and Optical Sensors in Flow-Based Analysis′, Sensors (Basel, Switzerland), 6: 1383 - 410.
3. Chen, Hsiang-Chieh, Sheng-Yao Xu, and Kai-Han Deng. "Water Color Identification System for Monitoring Aquaculture Farms." Sensors 22.19 (2022): 7131.
4. Dantan, Nathalie, Wolfgang Frenzel, and Stephan Küppers. 2001. ′Flow injection analysis coupled with HPLC and CE for monitoring chemical production processes′, Chromatographia, 54: 187-90.
5. El-Sayed, Mohamed Hanafy. 2015. ′Flow enhanced corrosion of water injection pipelines′, Engineering Failure Analysis, 50: 1-6.
6. Erfani, Seyed Mohammad Hassan, and Erfan Goharian. "Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques." Journal of Hydroinformatics 25.3 (2023): 835-850.
7. Geerdink, R. B., J. Brouwer, and O. J. Epema. 2009. ′A reliable free chemical demand () method′, Anal Methods, 1: 108-14.
8. Islam, M. A., P. Mahbub, P. N. Nesterenko, B. Paull, and M. Macka. 2019. ′Prospects of pulsed amperometric detection in flow-based analytical systems - A review′, Anal Chim Acta, 1052: 10-26.
9. Kolb, M., M. Bahadir, and B. Teichgraber. 2017. ′Determination of chemical oxygen demand (COD) using an alternative wet chemical method free of mercury and dichromate′, Water Res, 122: 645-54.
10. Kozak, J., M. Wojtowicz, N. Gawenda, and P. Koscielniak. 2011. ′An automatic system for acidity determination based on sequential injection titration and the monosegmented flow approach′, Talanta, 84: 1379-83.
11. Lin, Kunning, Jin Xu, Huige Guo, Yunlong Huo, and Yuanbiao Zhang. 2021. ′Flow injection analysis method for determination of total dissolved nitrogen in natural waters using on-line ultraviolet digestion and vanadium chloride reduction′, Microchemical Journal, 164: 105993.
12. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.
13. Liu, X., Jin, L., Han, X., Lu, J., You, J., & Kong, L. (2021, January). Identity-aware facial expression recognition in compressed video. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 7508-7514). IEEE.
14. Meng, Z., Liu, P., Cai, J., Han, S. & Tong, Y. 2017 Identity-aware convolutional neural network for facial expression recognition. In IEEE Int. Conf. Auto. Face Gest. Recog. IEEE, pp. 558–565.
15. Miró, Manuel, Andreu Cladera, José Manuel Estela, and Víctor Cerdà. 2001. ′Dual wetting-film multi-syringe flow injection analysis extraction: Application to the simultaneous determination of nitrophenols′, Analytica Chimica Acta, 438: 103-16.
16. Pamplin, Kim L., and Dennis C. Johnson. 1997. ′The coulometric determination of chemical oxygen demand′, Electroanalysis, 9: 279-83.
17. Ramsing, A. U., Jaromir Prof Ruzicka, and Elo Harald Hansen. 1981. ′The principles and theory of high-speed titrations by flow injection analysis′, Analytica Chimica Acta, 129: 1-17.
18. Ranger, Craig B. 1981. ′Flow injection analysis. Principles, techniques, applications, design′, Analytical Chemistry, 53.
19. Safeer, Soma, Ravi P. Pandey, Bushra Rehman, Tuba Safdar, Iftikhar Ahmad, Shadi Wajih Hasan, and Asmat Ullah. 2022. ′A review of artificial intelligence in water purification and wastewater treatment: Recent advancements′, Journal of Water Process Engineering.
20. Silva Junior, M. M., L. A. Portugal, A. M. Serra, L. Ferrer, V. Cerda, and S. L. C. Ferreira. 2017. ′On line automated system for the determination of Sb(V), Sb(III), thrimethyl antimony(v) and total antimony in soil employing multisyringe flow injection analysis coupled to HG-AFS′, Talanta, 165: 502-07.
21. Solomon, Daniel H., Kelli D. Allen, Patricia Katz, Amr H. Sawalha, and Edward H Yelin. 2023. ′ChatGPT, et al … Artificial Intelligence, Authorship, and Medical Publishing′, ACR Open Rheumatology, 5: 288 - 89.
22. Stewart, Kent K., and A. Gregory. Rosenfeld. 1981. ′Automated titrations: the use of automated multiple flow injection analysis for the titration of discrete samples′, The Journal of Automatic Chemistry, 3: 30 - 32.
23. Tan, Z., Yang, C., Qiu, Y., Jia, W., Gao, C., & Duan, H. (2023, August). A three-step machine learning approach for algal bloom detection using stationary RGB camera images. International Journal of Applied Earth Observation and Geoinformation, 122, 103421. https://doi.org/10.1016/j.jag.2023.103421
24. Toei, Jnn′ichi, Kei Fujii, and Nobuyuki. Baba. 1989. ′An improved reaction valve for flow injection analysis′, Fresenius′ Zeitschrift für analytische Chemie, 334: 13-15.
25. Trojanowicz, M., and K. Kolacinska. 2016. ′Recent advances in flow injection analysis′, Analyst, 141: 2085-139.
26. Valcárcel, Miguel, and María Dolores Luque Castro. 1987. ′Continuous separation techniques in flow injection analysis′, Journal of Chromatography A, 393: 3-23.
27. Vallejo-Pecharromán, B., A. Izquierdo-Reina, and María Dolores Luque Castro. 1999. ′Flow injection determination of chemical oxygen demand in leaching liquid′, Analyst, 124: 1261-64.
28. Yu, Hongbing, Chuanjun Ma, Xie Quan, Shuo Chen, and Huimin Zhao. 2009. ′Flow injection analysis of chemical oxygen demand (COD) by using a boron-doped diamond (BDD) electrode′, Environmental science & technology, 43 6: 1935-9.
29. Zhang, Xuzhi, Huang Mengshi, Jun Zhao, Jingquan Liu, Wenrong Yang, and Keming Qu. 2018. ′Monitoring acid-base, precipitation, complexation and redox titrations by a capacitively coupled contactless conductivity detector′, Measurement, 116: 458-63.
30. Zhou, Tingjin, Sichao Feng, Yongming Huang, Dongxing Yuan, Jian Ma, and Yong Zhu. 2016. ′Determination of Aluminum in Natural Waters by Flow Injection Analysis with Spectrophotometric Detection′, Analytical Letters, 49: 1669-80.
31. Zhou, Yimin. 2023. ′Editorial: Intelligent control and applications for robotics, volume II′, Frontiers in Neurorobotics, 17.

指導教授

林伯勳(Po-Hsun Lin)

審核日期

2024-1-24

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