The Role of Artificial Intelligence in Enhancing the Efficiency of Paved Drying Beds for Wastewater Sludge Treatment: A Comprehensive Review

سید برزانی, محبوبه; ایرج پور, میلاد

doi:10.22093/wwj.2025.525853.3496

The Role of Artificial Intelligence in Enhancing the Efficiency of Paved Drying Beds for Wastewater Sludge Treatment: A Comprehensive Review

مقالات آماده انتشار

نوع مقاله : مقالات مروری

نویسندگان

محبوبه سید برزانی ¹

میلاد ایرج پور ²

¹ کارشناسی ارشد مهندسی عمران-محیط زیست، دانشگاه صنعتی اصفهان، اصفهان، ایران

² کارشناسی ارشد مهندسی عمران-محیط زیست، موسسه آموزش عالی دانش پژوهان پیشرو، اصفهان، ایران

10.22093/wwj.2025.525853.3496

چکیده

Paved 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.

کلیدواژه‌ها

Artificial Intelligence, Paved Drying Beds, Wastewater Treatment Plants, Sludge Management, Optimization, Environmental Sustainability

موضوعات

تصفیه

Abdolalian, S. and Qaderi, F., 2022. Removal of phosphorus and chemical oxygen demand from excess sludge supernatant using of ultrasound waves. Journal of Applied Research in Water and Wastewater, 9, 173-179. https://doi.org/10.22126/arww.2023.5744.1190.

Al-Malack, M. H., Abuzaid, N. S., Bukhari, A. A. and Essa, M. H., 2002. Characterization, utilization and disposal of municipal sludge: the state of-the-art. Arabian Journal for Science and Engineering, 27, 3-27. [Link]

Al-Nozaily, F. A., Taher, T. M. and Al-Rawi, M. H. M., 2013. Evaluation of the sludge drying beds at Sana’a wastewater treatment plant. In Seventeenth International Water Technology Conference, IWTC17. [Link]

Arun, V. and Lohani, B. N., 1988. Estimating supernatant recycle from sludge treatment. Journal of Environmental Engineering, 114, 447-453. https://doi.org/10.1061/(ASCE)0733-9372(1988)114:2(447).

Atthajariyakul, S. and Leephakpreeda, T., 2006. Fluidized bed paddy drying in optimal conditions via adaptive fuzzy logic control. Journal of Food Engineering, 75, 104-114. https://doi.org/10.1016/j.jfoodeng.2005.03.055.

Badza, T., Tesfamariam, E. H. and Cogger, C., 2020. Sludge stabilization process, drying depth and polymeric material addition: implication on nitrogen content, selected chemical properties and land requirement in sand drying beds. Energies, 13, 6753. https://doi.org/10.3390/en13246753.

Corrêa, R. S., White, R. E. and Weatherley, A. J., 2012. Effects of sewage sludge stabilization on organic-N mineralization in two soils. Soil Use and Management, 28, 12-18. https://doi.org/10.1111/j.1475-2743.2012.00387.x.

Deepnarain, N., Nasr, M., Kumari, S., Stenström, T. A., Reddy, P., Pillay, K. et al., 2020. Artificial intelligence and multivariate statistics for comprehensive assessment of filamentous bacteria in wastewater treatment plants experiencing sludge bulking. Environmental Technology and Innovation, 19, 100853. https://doi.org/10.1016/j.eti.2020.100853.

Dodane, P. H. and Ronteltap, M., 2014. Unplanted drying beds. Faecal Sludge Manag. Syst. Approach Implement. Oper. 141-154. [Link]

El Gohary, E. H., El Monayeri, D. S., Emeara, M. S. and El Monayeri, O. D., 2022. Application of different techniques to enhance sludge drying paved bed drying process. Alexandria Engineering Journal, 61, 5687-5698. https://doi.org/10.1016/j.aej.2021.10.064.

Elbaz, A. A., Aboulfotoh, A., ElGohary, E. H. and Reham, M. T., 2020. Review classification of sludge drying beds SDB (conventional sand drying beds CSDB, Wedge-wire, Solar and Vacuum assisted and paved drying beds PDB). Journal of Materials and Environmental Sciences, 11, 593-608. [Link]

Elbaz, A. A., Aboulfotoh, A. M., ElGohary, E. H. and Reham, M. T., 2021. Evaluation of the use of modified paved drying beds compared to the conventional sand drying beds. Desalination and Water Treatment, 213, 148-158. https://doi.org/10.5004/dwt.2021.26710.

Epstein, E., 2002. Land Application of Sewage Sludge and Biosolids. 1^st Edition, Boca Raton, CRC Press. https://doi.org/10.1201/9781420032116.

Hamed, M. M., Khalafallah, M. G. and Hassanien, E. A., 2004. Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling and Software, 19, 919-928. https://doi.org/10.1016/j.envsoft.2003.10.005.

Haseltine, T. R., 1951. Measurement of sludge drying bed performance. Sewage and Industrial Wastes, 23(7), 1065-1083. [Link]

Huang, Y. W. and Chen, M. Q., 2015. Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge. Measurement, 73, 640-648. https://doi.org/10.1016/j.measurement.2015.06.014.

Karakaplan, N., Goz, E., Tosun, E. and Yuceer, M., 2019. Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L. Journal of Food Processing and Preservation, 34. https://doi.org/10.1111/jfpp.14142.

Kinsley, C., Kennedy, K. and Crolla, A., 2012. Modelling and application of an uncovered freezing bed technology for septage treatment. Canadian Journal of Civil Engineering, 39, 1136-1144. https://doi.org/10.1139/l2012-099.

Lampreia, E., 2017. Modelling sludge drying bed dewatering processes. Técnico Lisboa, 1-10. [Link]

Macedo, J., Costa, E. and Marques, L., 2016. Genetic Programming Algorithms for Dynamic Environments. In: Squillero, G., Burelli, P. Eds. Applications of Evolutionary Computation, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 280-295. https://doi.org/10.1007/978-3-319-31153-1_19.

Manfio, D. V., Tonetti, A. L. and Matta, D., 2018. Dewatering of septic tank sludge in alternative sludge drying bed. Journal of Water, Sanitation and Hygiene for Development, 8, 792-798. https://doi.org/10.2166/washdev.2018.276.

Manga, M., Evans, B. E., Camargo-Valero, M. A. and Horan, N. J., 2016. Effect of filter media thickness on the performance of sand drying beds used for faecal sludge management. Water Science and Technology, 74, 2795-2806. https://doi.org/10.2166/wst.2016.451.

Masmoudi, A., Ali, A. B. S., Mhiri, H. and Dhaouadi, H., 2019. Experimental study of sludge drying bed under a Mediterranean climate in Tunisia. 2019 10^th International Renewable Energy Congress (IREC). IEEE, Sousse, Tunisia. pp.1-6. https://doi.org/10.1109/IREC.2019.8754628.

Mathaba, M. and Banza, J., 2023. A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. Journal of Environmental Science and Health, Part A, 58, 1047-1060. https://doi.org/10.1080/10934529.2024.2309102.

Mbarki, S., Labidi, N., Mahmoudi, H., Jedidi, N. and Abdelly, C., 2008. Contrasting effects of municipal compost on alfalfa growth in clay and in sandy soils: N, P, K, content and heavy metal toxicity. Bioresource Technology, 99, 6745-6750. https://doi.org/10.1016/j.biortech.2008.01.010.

Metcalf, L., Eddy, H. P. and Tchobanoglous, G., 2004. Wastewater Engineering: Treatment and Reuse. 4^th Edition, McGraw-Hill, New York. 1-53. [Link]

Momeni, S., Alimohammadi, M., Naddafi, K., Nabizadeh, R., Changani, F., Zarei, A. et al., 2019. Study of sludge from the largest wastewater treatment plant in the Middle East (southern Tehran, Iran) based on chemical and microbiological parameters for use in agriculture. Desalination and Water Treatment, 160, 153-160. https://doi.org/10.5004/dwt.2019.24369.

Nasir, F. B. and Li, J., 2024. Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence. Water Environment Research, 96, e11136. http://dx.doi.org/10.1002/wer.11136.

Ngalonkulu, S. M. and Huan, Z., 2024. Viability of an open-loop heat pump drying system in South African climatic conditions. Energies, 17(10), 2432. https://doi.org/10.3390/en17102432.

Obianyo, J. I. and Agunwamba, J. C., 2015. Modeling of evaporation losses in sewage sludge drying bed. Nigerian Journal of Technology, 34, 890-897. http://dx.doi.org/10.4314/njt.v34i4.31.

Pescod, M. B., 1971. Sludge handling and disposal in tropical developing countries. Journal (Water Pollution Control Federation), 555-570. [Link]

Qasim, S. R. and Zhu, G., 2017. Wastewater Treatment and Reuse Theory and Design Examples, Volume 2: Post-Treatment, Reuse, and Disposal. 1^st Edition, CRC press. [Link]

Rathod, P. H., Patel, J. C., Shah, M. R. and Jhala, A. J., 2009. Recycling gamma irradiated sewage sludge as fertilizer: a case study using onion (Alium cepa). Applied Soil Ecology, 41, 223-233. http://dx.doi.org/10.1016/j.apsoil.2008.11.001.

Sakellaropoulos, G. P., 1986. Drying and Evaporation Processes. In: Wang, L. K., Pereira, N. C., Eds. Water Resources and Natural Control Processes. Humana Press, Totowa, NJ, pp. 373-446. [Link]

Saran, A. K. and Devi, R., 2016. Efficiency improvement of sludge drying bed-design modification of present system. International Journal of Multidisciplinary Research and Development, 2(4), 32-34. [Link]

Szeląg, B. and Gawdzik, J., 2016. Application of selected methods of artificial intelligence to activated sludge settleability predictions. Polish Journal of Environmental Studies, 25, 1709-1714. https://doi.org/10.15244/pjoes/62262.

Taheri-Garavand, A., Karimi, F., Karimi, M., Lotfi, V. and Khoobbakht, G., 2018. Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer. Food Science and Technology International, 24, 277-291. https://doi.org/10.1177/1082013217747712.

Uludag-Demirer, S. and Othman, M., 2009. Removal of ammonium and phosphate from the supernatant of anaerobically digested waste activated sludge by chemical precipitation. Bioresource Technology, 100, 3236-3244. https://doi.org/10.1016/j.biortech.2009.02.030.

Wainaina, S., Awasthi, M. K., Sarsaiya, S., Chen, H., Singh, E., Kumar, A. et al., 2020. Resource recovery and circular economy from organic solid waste using aerobic and anaerobic digestion technologies. Bioresource Technology, 301, 122778. https://doi.org/10.1016/j.biortech.2020.122778.

Wang, L. K., Shammas, N. K. and Hung, Y. T., 2007. Biosolids Engineering and Management, Handbook of Environmental Engineering. 1^st edition, Vol. 7. Humana Press, Totowa, NJ, USA. [Link]

Wei, L., Zhu, F., Li, Q., Xue, C., Xia, X., Yu, H. et al., 2020. Development, current state and future trends of sludge management in China: based on exploratory data and CO₂-equivaient emissions analysis. Environment International, 144, 106093. https://doi.org/10.1016/j.envint.2020.106093.