bioprocess-engineering

Pharma 4.0 Bio-Twin console featuring real-time IIoT simulation, MSPC latent soft sensors (PCA), deterministic Groq LLM diagnostic agent loops, and automated 5-minute/instant interrupt SMTP email notification alerting pipelines.

Machine learning workflow for fermentation process analysis and bioprocess yield prediction using biotechnology and chemical engineering datasets.

Closed-Loop CHO-K1 Bioreactor Digital Twin, ISPE A-Mab QbD Design Space, USP <1079.2> VTB/MKT Analyzer & Six Sigma SPC Suite validated under FDA CSA (2026) & 21 CFR Part 11.

Physics-Informed Neural Network (PINN) bioprocess digital twin & gray-box soft sensor for 20L bioreactor state estimation

Non-linear Machine Learning pipeline (Gradient Boosting & Random Forest, R² > 0.90) modeling coupled physicochemical & hydrodynamic kinetics (kLa, shear stress, Haldane) to predict bioreactor yield.

Physics-based interactive bioreactor digital twin & 60 FPS real-time SCADA telemetry simulator

Python framework for structural and practical identifiability analysis of a 13-parameter Multi-Cell Line CHO kinetic model using SVD-ROSM, Monte Carlo simulations, and t-value based parameter fixing.

RNA Manufacturing Innovation Team (RNA MINT) — University of Sheffield. Digitalising RNA vaccine and therapeutic manufacturing. Professor Zoltan Kis