Activated sludge models at the crossroad of artificial intelligence-A perspective on advancing process modeling

被引:28
|
作者
Sin, Gurkan [1 ]
Al, Resul [1 ]
机构
[1] Tech Univ Denmark, Dept Chem & Biochem Engn, Proc & Syst Engn Ctr PROSYS, Copenhagen, Denmark
关键词
Data integration - Activated sludge process - Artificial intelligence - Wastewater treatment;
D O I
10.1038/s41545-021-00106-5
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The introduction of Activated Sludge Models No. 1 (ASM1) in the early 1980s has led to a decade-long experience in applying these models and demonstrating their maturity for the wastewater treatment plants' design and operation. However, these models have reached their limits concerning complexity and application accuracy. A case in point is that despite many extensions of the ASMs proposed to describe N2O production dynamics in the activated sludge plants, these models remain too complicated and yet to be validated. This perspective paper presents a new vision to advance process modeling by explicitly integrating the information about the microbial community as measured by molecular data in activated sludge models. In this new research area, we propose to harness the synergy between the rich molecular data from advanced gene sequencing technology with its integration through artificial intelligence with process engineering models. This is an interdisciplinary research area enabling the two separate disciplines, namely environmental biotechnology, to join forces and work together with the modeling and engineering community to perform new understanding and model-based engineering for sustainable WWTPs of the future.
引用
收藏
页数:7
相关论文
共 50 条
  • [41] Nonlinear modeling of activated sludge process using the HammersteinWiener structure
    Fracz, Pawel
    1ST INTERNATIONAL CONFERENCE ON THE SUSTAINABLE ENERGY AND ENVIRONMENT DEVELOPMENT (SEED 2016), 2016, 10
  • [42] Toward Explainable Artificial Intelligence for Regression Models A methodological perspective
    Letzgus, Simon
    Wagner, Patrick
    Lederer, Jonas
    Samek, Wojciech
    Mueller, Klaus-Robert
    Montavon, Gregoire
    IEEE SIGNAL PROCESSING MAGAZINE, 2022, 39 (04) : 40 - 58
  • [43] Application of artificial neural network on activated sludge process for wastewater treatment
    Xie, HW
    Zhang, JJ
    Yun, XP
    ISTM/2005: 6th International Symposium on Test and Measurement, Vols 1-9, Conference Proceedings, 2005, : 1720 - 1723
  • [44] Agricultural process data as a source for knowledge: Perspective on artificial intelligence
    Backman, Juha
    Koistinen, Markku
    Ronkainen, Ari
    SMART AGRICULTURAL TECHNOLOGY, 2023, 5
  • [45] MODELING OF PRESSURE DIE CASTING PROCESS: AN ARTIFICIAL INTELLIGENCE APPROACH
    Kittur, Jayant K.
    Patel, G. C. Manjunath
    Parappagoudar, Mahesh B.
    INTERNATIONAL JOURNAL OF METALCASTING, 2016, 10 (01) : 70 - 87
  • [46] Artificial Intelligence based modeling of pervaporation process for alcohol dehydration
    Mittal, Srishti
    Gupta, Aniket
    Srivastava, Saksham
    Jain, Manish
    MATERIALS TODAY-PROCEEDINGS, 2022, 50 : 150 - 154
  • [47] Modeling of Pressure Die Casting Process: An Artificial Intelligence Approach
    Jayant K. Kittur
    G. C. Manjunath Patel
    Mahesh B. Parappagoudar
    International Journal of Metalcasting, 2016, 10 : 70 - 87
  • [48] Metal cutting process parameters modeling: an artificial intelligence approach
    Tanikic, Dejan
    Manic, Miodrag
    Radenkovic, Goran
    Mancic, Dragan
    JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 2009, 68 (06): : 530 - 539
  • [49] Important limitations in the modeling of activated sludge:: biased calibration of the hydrolysis process
    Insel, G
    Gül, ÖK
    Orhon, D
    Vanrolleghem, PA
    Henze, M
    WATER SCIENCE AND TECHNOLOGY, 2002, 45 (12) : 23 - 36
  • [50] Critical review of activated sludge modeling: State of process knowledge, modeling concepts, and limitations
    Hauduc, H.
    Rieger, L.
    Oehmen, A.
    van Loosdrecht, M. C. M.
    Comeau, Y.
    Heduit, A.
    Vanrolleghem, P. A.
    Gillot, S.
    BIOTECHNOLOGY AND BIOENGINEERING, 2013, 110 (01) : 24 - 46