Floc image-driven deep learning enhanced by temporal windows and transformers for carbon emission reduction in drinking water treatment plants.

Using machine learning (ML) and deep learning (DL) algorithms for precise coagulant dosing in drinking water treatment plants (DWTPs) helps ensure drinking water safety and supports greenhouse gas (GHG) emission reduction. The effectiveness of these algorithms depends heavily on the availability of long-term data. Short-term data are used in this study to explore the potential of four traditional ML algorithms and four DL algorithms for precise coagulant dosing. Three strategies were introduced: an innovative method for floc morphological feature extraction, selection of temporal windows, and integration of transformer architecture. Based on these strategies, 16 different scenarios were constructed, resulting in 96 models for analysis. Results show that without any strategy applied, ML models achieved 5.0% higher R and 10.5% higher R² than DL models. This is due to their simplicity, faster convergence, and suitability for low-dimensional data. However, with the proposed strategies, DL models significantly improved and outperformed ML models. Given the time-lagged dependencies across DWTP treatment units, optimized DL models N better captured complex nonlinear temporal relationships. The best-performing model was the temporal convolutional network (TCN) with floc morphological features, 4-h temporal window, and transformer architecture, achieving R and R2 values of 0.99. The model was trained with only one month of data and rapidly deployed. A weekly self-updating mechanism was integrated to ensure long-term adaptability. The model has been operating stably in a DWTP for over six months. It has reduced coagulant dosage by 20% and carbon dioxide equivalent (CO2-eq) emissions by an estimated 70 tons annually. This study demonstrates the strong potential of optimized DL algorithms to improve water purification and reduce carbon emissions.