面對全球水資源短缺與環保規範日益嚴格，高科技製造業極需提升廠務水處理系統的效率與穩定性，以兼顧營運風險與成本控管。本研究以半導體產業為實證場域，探討人工智慧（Artificial Intelligence, AI）於水處理系統中的預測性維護應用，並透過層級分析法（Analytic Hierarchy Process, AHP）進行風險評估模型建構。
研究首先透過文獻回顧萃取四項主要構面：系統效能、預測性維護、成本效益與自動化整合，進而設計專家問卷並取得 42份有效樣本。資料分析部分運用 SPSS 進行信效度檢驗與描述性統計，並透過 AHP 進行準則權重評估與排序。
實證結果顯示，AI 可顯著提升污染物去除效率與系統穩定性（權重 0.38），並透過預測性維護降低設備故障率及非計畫性停機時間（權重 0.26）。此外，AI 與物聯網（IoT）深度整合有助於實現無人值守運作（權重 0.22），具長期自動化潛力。然導入障礙主要為資料品質、技術成本與人員適應性，分別占總風險的 35%、30% 與 20%。
本研究建議企業採取「數據治理 → 模組導入 → 人才培育」的分階段導入策略，以平衡技術投資與營運風險；政府則可推動資料標準化與驗證平台設置，強化 AI 在水處理領域之可行性與落地應用。

The increasing scarcity of global water resources and the tightening of environmental regulations have created an urgent need to optimize wastewater treatment systems, particularly in high-tech manufacturing industries such as semiconductors. This study investigates the integration of Artificial Intelligence (AI) into factory utility water treatment systems, focusing on predictive maintenance and comprehensive risk assessment.
Using the Analytic Hierarchy Process (AHP) as the core methodology, this study constructs a multi-criteria evaluation model. Four major dimensions were derived from a literature review: system efficiency, predictive maintenance, cost-effectiveness, and automation. A structured questionnaire was designed based on these dimensions and distributed to professionals in the semiconductor industry, resulting in 42 valid responses. SPSS software was employed to perform reliability analysis, descriptive statistics, and weight calculations via the AHP framework.
The results reveal that AI significantly improves pollutant removal efficiency and overall system stability, ranking as the most critical factor (weight = 0.38). Predictive maintenance enabled by AI contributes to reduced equipment failure rates and shorter unplanned downtimes (weight = 0.26). Furthermore, the integration of AI with the Internet of Things (IoT) allows for unattended operation and supports long-term automation goals (weight = 0.22).
However, the adoption of AI is hindered by several non-technical barriers, with data quality (35%), implementation costs (30%), and employee adaptability (20%) identified as the most significant challenges. These factors affect both the technical feasibility and the organizational readiness for implementation.
Based on these findings, the study recommends a phased adoption strategy for enterprises, beginning with data governance, followed by modular AI implementation, and finally personnel training. It also suggests that government agencies promote data standardization and certification platforms to lower the entry threshold and encourage the adoption of AI in the water treatment sector.

中文文獻
1.陳國華（2020）。人工智慧於污水處理技術的應用分析。《環境工程學報》，36(2)，120-135。
2.劉志強、王大明（2021）。AI 技術於水處理系統的預測性維護效益探討。《台灣環境科技期刊》，25(1)，87-101。
英文文獻
1.Ahmed, A. N., Othman, F. B., Afan, H. Ｓ, 45678.
2.Brown, J. R., Smith, T., & Lee, W. (2021). AI-driven wastewater treatment optimization: A comprehensive review. Environmental Science & Technology, 55(12), 3892-3905.
3.Chen, G. (2004). Electrochemical technologies in wastewater treatment. Separation and Purification Technology, 38, 11–41.
4.Chen, Y., Wang, L., Zhang, H., & Li, X. (2020). Real-time monitoring of wastewater treatment using AI-driven models. Environmental Science & Technology, 54(6), 3456-3472.
5.Jones, M., Kim, Y. S., & Park, H. (2020). Predictive maintenance in wastewater treatment plants using AI and IoT. Water Research, 187, 116543.
6.Kim, S. H., Park, J. M., Lee, H. J., & Yoo, C. K. (2021). AI-driven electrochemical wastewater treatment systems. Water Science & Technology, 83(9), 1985-1998.
7.Lee, C., & Zhao, J. (2019). Integrating AI and traditional maintenance strategies for wastewater treatment optimization. Journal of Industrial Automation and Environmental Engineering, 65(4), 205-218.
8.Liu, X., Wang, L., & Zhao, Y. (2021). Advances in AI-based predictive maintenance for wastewater treatment plants. Science of the Total Environment, 775, 145761.
9.Smith, T., & Zhao, M. (2019). Machine learning applications in industrial wastewater treatment. AI & Environmental Science, 45(3), 1120-1135.
10.Wang, Y., Cheng, Y., Liu, H., Guo, Q., Dai, C., & Zhao, M. (2023). A review on applications of artificial intelligence in wastewater treatment. Sustainability, 15(18), 13557.
11.Woo, T. Y., Nam, K. J., Heo, S. K., Lim, J. Y., Kim, S. Y., & Yoo, C. K. (2022). Predictive maintenance system for membrane replacement time detection using AI-based functional profile monitoring: Application to a full-scale MBR plant. Journal of Membrane Science, 649, 120400.
12.Zhao, J., & Lee, W. T. (2018). Smart sensor networks and AI-based automation in wastewater treatment plants. Journal of Water Research & Technology, 78, 1025-1039.
13.Garcia, L. A., Martinez, J. P., & Rodriguez, S. (2020). AI-driven remote monitoring in wastewater treatment systems. Journal of Environmental Engineering. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001623.
14.Nagpal, M., Siddique, M. A., Sharma, K., & Mittal, A. (2024). Optimizing wastewater treatment through artificial intelligence: Recent advances and future prospects. Water Science & Technology.