Author(s)

Jiaxiangyi Liu ¹, Xinzhe Gao ²

Affiliation(s)

¹ School of Electronics and Information Engineering, Liaoning University of Science and Technology, Liaoning, China
² Artificial Intelligence Division, Beijing Vision Zhiyuan Technology Co., Ltd., Beijing, China

Corresponding Author

Xinzhe Gao

ABSTRACT

Optimisation of wastewater treatment processes relies on mechanistic models that accurately describe biochemical reactions. However, the parameters of the internationally dominant Activated Sludge Model (ASM) family are calibrated against European and North American influent concentrations, and their direct application to Chinese plants—characterised by low-strength influent, combined-sewer infiltration and frequent industrial shocks—produces systematic distortion, while their elaborate component fractionation is difficult to measure in practice. This paper proposes a full-process mechanistic model, termed Helix-Core, adapted to Chinese influent characteristics, together with a plant-specific calibration method. Built upon the activated sludge mechanism, the model assembles 32 governing equations along the process chain covering six process units, namely the primary clarifier, anaerobic tank, anoxic tank, aerobic tank, secondary clarifier and filter. A five-step calibration pipeline (process parsing, data correction, parameter calibration, measured back-testing, online service) is proposed, which requires only routinely monitored indicators. The adjoint sensitivity method is introduced to make the whole equation system differentiable, turning parameter inversion from manual trial-and-error into gradient-driven optimisation, and a Fourier neural operator is trained as a millisecond-level surrogate of the model. Validation on more than 31 million operating records from 27 wastewater treatment plants shows that the relative deviations of the simulated effluent COD, ammonia nitrogen, total nitrogen and total phosphorus are all below 10%, providing a trustworthy high-fidelity evaluation environment for process optimisation algorithms.

KEYWORDS

Wastewater treatment; Activated sludge mechanistic model; Parameter calibration; Adjoint sensitivity method; Neural operator

CITE THIS PAPER

Jiaxiangyi Liu, Xinzhe Gao. Mechanism-Driven Modelling and Plant-Specific Calibration of a Full-Process Wastewater Treatment Model Adapted to Chinese Influent Characteristics. Computing, Performance and Communication Systems (2026). Vol. 10, No. 1, 87-91. DOI: http://dx.doi.org/10.23977/cpcs.2026.100110.

REFERENCES

[1] Henze M, Gujer W, Mino T, et al. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Publishing, London, 2000.
[2] Gernaey K V, Jeppsson U, Vanrolleghem P A, et al. Benchmarking of Control Strategies for Wastewater Treatment Plants. IWA Publishing, London, 2014.
[3] Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707.
[4] Chen R T Q, Rubanova Y, Bettencourt J, et al. Neural ordinary differential equations. In Advances in Neural Information Processing Systems (NeurIPS), 2018: 6571-6583.
[5] Li Z, Kovachki N, Azizzadenesheli K, et al. Fourier neural operator for parametric partial differential equations. In International Conference on Learning Representations (ICLR), 2021.
[6] Korda M, Mezic I. Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control. Automatica, 2018, 93: 149-160.

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