مهندسین مشاور طرح و تحقیقات آب و فاضلاب اصفهان مجله آب و فاضلاب 1024-5936 مقالات آماده انتشار 2025 11 16 The Role of Artificial Intelligence in Enhancing the Efficiency of Paved Drying Beds for Wastewater Sludge Treatment: A Comprehensive Review The Role of Artificial Intelligence in Enhancing the Efficiency of Paved Drying Beds for Wastewater Sludge Treatment: A Comprehensive Review 234374 10.22093/wwj.2025.525853.3496 FA محبوبه سید برزانی کارشناسی ارشد مهندسی عمران-محیط زیست، دانشگاه صنعتی اصفهان، اصفهان، ایران 0009-0007-8075-7474 میلاد ایرج پور کارشناسی ارشد مهندسی عمران-محیط زیست، موسسه آموزش عالی دانش پژوهان پیشرو، اصفهان، ایران 0009-0000-5252-3880 Journal Article 2025 05 24 <span style="font-size: 18.0pt; mso-bidi-font-size: 21.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Yas; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">P</span><span style="font-size: 10.0pt; mso-bidi-font-size: 13.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Yas; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">aved Drying Beds are a sustainable, low-energy technology for wastewater sludge dewatering, but their efficiency is hindered by long drying times and high land requirements, which are highly dependent on climatic and operational parameters. This comprehensive review synthesizes current research on the potential of Artificial Intelligence to optimize PDB performance. A systematic literature review was conducted, analyzing 32 key studies to evaluate the impact of parameters such as sludge depth, type, and climate on drying efficiency, and to assess the application of AI and machine learning techniques for process prediction and control. The thematic synthesis reveals that shallower sludge depths and favorable climatic conditions significantly reduce drying time. AI models, particularly Artificial Neural Networks and Gradient Boosting Machines, have demonstrated high accuracy in predicting complex sludge treatment processes like settleability and production. The review highlights the promise of hybrid models that integrate AI with physical principles to enhance robustness and generalizability. Despite this potential, significant challenges remain, including model-data mismatch, supernatant management, and a lack of real-world validation. This paper identifies critical future research directions, such as the development of real-time monitoring systems, the use of transfer learning to overcome data scarcity, and the creation of digital twins for adaptive PDB operation. By providing a critical framework for AI integration, this review aims to advance the sustainability, cost-effectiveness, and operational efficiency of PDBs within modern wastewater treatment plants.</span> <span style="font-size: 18.0pt; mso-bidi-font-size: 21.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Yas; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">P</span><span style="font-size: 10.0pt; mso-bidi-font-size: 13.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Yas; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">aved Drying Beds are a sustainable, low-energy technology for wastewater sludge dewatering, but their efficiency is hindered by long drying times and high land requirements, which are highly dependent on climatic and operational parameters. This comprehensive review synthesizes current research on the potential of Artificial Intelligence to optimize PDB performance. A systematic literature review was conducted, analyzing 32 key studies to evaluate the impact of parameters such as sludge depth, type, and climate on drying efficiency, and to assess the application of AI and machine learning techniques for process prediction and control. The thematic synthesis reveals that shallower sludge depths and favorable climatic conditions significantly reduce drying time. AI models, particularly Artificial Neural Networks and Gradient Boosting Machines, have demonstrated high accuracy in predicting complex sludge treatment processes like settleability and production. The review highlights the promise of hybrid models that integrate AI with physical principles to enhance robustness and generalizability. Despite this potential, significant challenges remain, including model-data mismatch, supernatant management, and a lack of real-world validation. This paper identifies critical future research directions, such as the development of real-time monitoring systems, the use of transfer learning to overcome data scarcity, and the creation of digital twins for adaptive PDB operation. By providing a critical framework for AI integration, this review aims to advance the sustainability, cost-effectiveness, and operational efficiency of PDBs within modern wastewater treatment plants.</span> https://www.wwjournal.ir/article_234374_45e58eea35247be16c8970797c5f2c65.pdf