What are the limitations of mechanistic models in wastewater treatment, and how do data-driven models like deep learning address these limitations?

Mechanistic models in wastewater treatment are based on mass conservation and biochemical rate equations, which approximate aggregated biological groups. They are useful for design and interpretation but cannot capture all behaviors, especially in complex, data-rich systems or when governing mechanisms are unknown. Data-driven models like deep learning address these limits by learning directly from large sensor datasets, and architectures such as Bi-LSTM can capture both short- and long-term process dynamics, improving effluent quality prediction and nutrient removal forecasting.

According to the source, mechanistic wastewater models are built on conservation of mass with chemical and biochemical rate equations that approximate aggregate biological functional groupings. They were developed and verified using field and laboratory measurements, making them valuable for design, feasibility, and control. However, their limitation is that they cannot capture all behaviors of wastewater processes, which are very dynamic, nonlinear systems with randomly fluctuating sewage conditions. This limitation is particularly evident when studying complex systems in data-rich environments or when governing mechanisms are not fully known. In contrast, modern data-driven approaches such as machine learning and deep learning have become widely used for data mining, optimisation, forecasting, and modelling of water treatment systems. The availability of massive volumes of sensor data enables these methods to learn patterns directly. Deep learning methods, including RNNs, LSTMs, GRUs, 1D-CNNs, and Bi-LSTMs, outperform other empirical techniques for predicting future states. The source specifically notes that LSTM-based models show superior performance in effluent concentration prediction and nutrient removal efficiency. Bi-LSTM networks are highlighted as especially suitable for wastewater processes because they can retrieve future information states and recall past data, handling the large range of fundamental time constants, from minutes for gas dissolution to weeks for sludge composition, better than standard RNNs. The source also describes a hybrid approach in which a mechanistic model is used for data augmentation to balance 15-minute SCADA data with weekly offline measurements, showing that mechanistic and data-driven models can complement each other. Overall, data-driven deep learning addresses mechanistic model limitations by improving predictive accuracy, enabling soft sensors, fault detection, and control optimisation, while also allowing automated feature selection and neural architecture optimisation through evolutionary algorithms.