REFINED DATA VISUALIZATION / MODEL OUTPUT
Wastewater Treatment Modeling
Developed a predictive modeling system to analyze fluid dynamics and filtration efficiency within wastewater treatment plants. This project utilizes supervised learning models trained on plant data to approximate water quality and optimize aeration effectiveness, nitrification performance, and effluent purity.
MODELING & METHODOLOGY
The project models wastewater treatment through integrated biological, chemical, and physical data analysis. We applied supervised learning techniques in MATLAB to process complex influent flow rates and dissolved oxygen levels. By normalizing high-dimensional datasets, we improved model stability, allowing for more accurate predictions of effluent water quality.
PERFORMANCE & OPTIMIZATION
Evaluated model performance by varying training set sizes, identifying a clear trend where larger training datasets significantly reduced the Root Mean Square Error (RMSE). We compared linear regression against polynomial regression models to balance predictive accuracy with robustness, ensuring the models successfully avoid overfitting while capturing non-linear patterns in filtration efficiency.
MODEL INTERPRETATION / NEXT ITERATION
Project Insights
Our analysis demonstrates that while polynomial models can provide a tighter fit to specific data subsets, linear regression offers superior stability for general application. By refining aeration parameters, we can better balance treatment effectiveness against operational energy costs, ensuring a more sustainable purification process.
Future Directions
01 — Implement hybrid modeling
02 — Explore non-linear deep learning architectures
03 — Develop self-updating models that adapt in real-time as new influent data is presented