Applied Machine Learning (Polynomial Regression) to optimize the removal of pharmaceutical contaminants from water using graphene nanoplatelets, using R Programming. Analyzed the influence of pH, adsorbent dosage, contact time, and initial concentration to identify conditions for maximum removal efficiency. Created a polynomal regression model of R-squared value 0.81.

Built an AI-based chatbot in Python to support water treatment plant operators by answering 50+ queries related to water quality, chemical dosing, and system anomalies. Integrated a backend database in Supabase with a simple frontend interface in Streamlit for operational support. 

Analyzed long-term lake level data of 20 years, and applied the Long Short-term Memory (LSTM) Machine Learning algorithm in Python to predict extreme flood events over the next decade. Evaluated trends and future flood risk at a major reservoir. 

Designed a two-storey residential building inspired by termite mound ventilation principles, utilizing the concept of biomimicry. Performed basic CFD analysis in SimScale to study airflow and natural ventilation performance.