AI Watches Flocs Form to Predict the Perfect Water-Treatment Chemical Dose

Water treatment plants around the world share a stubborn problem: the chemicals that clarify drinking water are dosed largely by guesswork refined through slow, reactive feedback. Coagulation, the process that clumps suspended particles together so they can settle and be filtered out, is used in more than half of all water purification plants, yet operators still depend on jar tests and turbidity readings that only reveal what happened minutes or hours earlier. A new study published in Discover Artificial Intelligence proposes a way to break that delay, teaching an artificial intelligence system to watch flocs form in real time and predict the optimal coagulant dose before the water quality sensors catch up.

The research, led by Galang Adira Prayoga of Institut Teknologi Bandung together with Emir Husni, Reza Darmakusuma and Herto Dwi Ariesyady, introduces a framework called Real-Time Sensing and Visual Information-Guided Physics-Informed Neural Networks, or RTS-VG PINNs. The core insight is that turbidity and the degree of flocculation, the two indicators most commonly used to judge how well coagulation is working, are inherently lagging signals. They measure the macroscopic outcome of particle aggregation and sedimentation, processes that take time to propagate from the bottom of a thickener up to the clear supernatant layer where sensors operate. By the time a change is visible in turbidity, the underlying process has already moved on, forcing conventional control strategies into reactive adjustments based on stale information.

To get ahead of that lag, the team combined two sources of information that are usually kept separate: continuous water quality sensing and live video of the flocculation process. On the vision side, the framework uses two complementary convolutional neural networks. A ResNet-50 based two-stage detector provides high-resolution feature extraction, learning hierarchical representations that range from low-level textures to high-level semantic descriptions of floc structure, with residual connections helping the deep network stay sensitive to subtle changes in floc morphology and density. In parallel, a YOLOv8 one-stage detector performs feature extraction and object detection in a single pass, dramatically reducing computational latency so that floc evolution can be identified and tracked in real time.

The camera setup itself was carefully engineered. A Canon 250D recorded video at 60 frames per second at 1920 by 1080 pixel resolution, positioned in front of a jar test reactor under controlled lighting to minimize reflections. Each experimental run followed a standardized protocol of two minutes of rapid mixing at 200 rpm, ten minutes of slow mixing at 30 rpm, and five minutes of settling. Regions of interest were defined within the observation window to focus the networks on representative floc behavior while suppressing background noise, and the resulting image sequences were synchronized with calibrated turbidity, pH and electrical conductivity measurements through a central computing system.

What makes the framework genuinely distinctive is how the visual information is used. Rather than treating the camera output as a standalone classifier, the researchers map the extracted floc features onto a physically meaningful parameter: the time-varying effective sedimentation velocity, v0(t). This velocity dynamically governs the Kynch sedimentation partial differential equation, a classical description of how particle concentrations evolve in space and time as suspensions settle. A small subnetwork called ParamNet learns the nonlinear relationship between image-derived features and this velocity, so the physics model continuously adapts to the evolving coagulation state instead of assuming fixed settling behavior.

The neural network at the heart of the system takes time and depth coordinates as inputs and outputs a predicted floc concentration field, effectively reconstructing the internal density distribution of the suspension, something no external sensor can measure directly. Training is governed by a composite loss function that balances four competing demands: a physics loss that penalizes violations of the Kynch equation, a data loss that anchors predictions to experimental observations, an initial condition loss, and a boundary condition loss enforcing a no-flux constraint at the top surface. The result is a model that is not merely fitting curves but adhering to conservation of mass and sedimentation dynamics, with physics-based residuals converging to the order of ten to the minus three.

The inverse problem is where the framework earns its practical value. A second network takes initial operating conditions, including pH, temperature, velocity gradient, initial zeta potential and initial turbidity, and predicts how zeta potential, turbidity, floc size and settling velocity evolve as functions of coagulant dose. Crucially, these predictions are constrained by embedded physical laws: a Langmuir-type adsorption relation captures the nonlinear surface charge response, while a Smoluchowski-style aggregation and breakup balance governs turbidity evolution. The model can then infer the dose that minimizes final turbidity, benchmarked against an independent experimental ground truth defined as the dose achieving the lowest measured supernatant turbidity in standardized jar tests.

Experiments spanned natural river water from a PDAM utility and artificial kaolin suspensions prepared from tap water and distilled water, with initial turbidities around 50 to 60 NTU, pH from 6.2 to 7.8, and zeta potentials from minus 3.1 to 0.3 mV. Polyaluminum chloride doses ranged from 12 to 120 mg/L, with the most favorable flocculation consistently observed between 24 and 84 mg/L. Against conventional benchmarks including Random Forest, Gradient Boosting, Support Vector Machine and Multilayer Perceptron models, RTS-VG PINNs achieved the highest coefficient of determination, approximately 0.498 compared with 0.484 for Random Forest, along with the lowest RMSE and MAE. The authors are candid that this improvement is modest and, given only ten independent experimental runs, should be read as evidence of potential benefit from combining visual information and physical constraints rather than statistically significant superiority.

The framework also incorporates Monte Carlo Dropout as an approximate Bayesian method for uncertainty quantification. By keeping dropout layers active during inference and running multiple stochastic forward passes, the system produces a distribution of possible dose estimates rather than a single number. The predictive mean serves as the recommended dose, while the predictive variance flags conditions where the model is uncertain, such as unfamiliar water chemistry or ambiguous visual flocculation states. High-variance predictions could be routed to operators for verification before any automated dosing action, a safety layer the authors emphasize is a decision-support indicator for future online implementation rather than a validated closed-loop control criterion.

The researchers are equally transparent about the limits of the current work. The one-dimensional Kynch formulation cannot capture the three-dimensional hydrodynamics of real treatment basins, the validation relied on only two independent laboratory-scale validation runs, and no end-to-end closed-loop dosing experiment was performed, leaving inference latency and actuator response unquantified. Future directions include coupling the vision-informed framework with computational fluid dynamics, incorporating population balance models to resolve microscale aggregation, and extending validation to pilot and full-scale plants under seasonal variation. Even so, the study demonstrates something conceptually important: that a camera watching particles clump together, fused with classical sedimentation physics and live sensor data, can anticipate what a turbidity meter will only report minutes later. If that promise survives scale-up, the humble jar test may finally gain a predictive successor.

Subject of Research: Physics-informed neural networks with computer vision for real-time coagulant dose optimization in water treatment

Article Title: Visual information guided physics informed neural networks framework for predictive coagulant dose optimization

Article References: Prayoga, G. A., Husni, E., Darmakusuma, R., & Ariesyady, H. D. (2026). Visual information guided physics informed neural networks framework for predictive coagulant dose optimization. Discover Artificial Intelligence, 6(1), Article 1380. https://doi.org/10.1007/s44163-026-02372-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02372-z

Keywords: physics-informed neural networks, coagulation, water treatment, computer vision, flocculation, Kynch sedimentation, YOLOv8, ResNet-50, coagulant dose optimization, turbidity sensing, Monte Carlo dropout, machine learning