The IDAES Process Systems Engineering Framework
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Updated
Jul 30, 2026 - Python
The IDAES Process Systems Engineering Framework
Hands-on material for a Machine Learning in Chemical Engineering course
Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.
A numerical platform for the digital design of pharmaceutical processes
Datasets from a fluid catalytic cracking unit to evaluate FDD techniques
A modular PyTorch framework for developing physics-informed neural network surrogate models for chemical process modeling, simulation, and process systems engineering.
Catalogue of Articles, Projects, Papers and Resources relating to PSE
This provides the python-based simulated moving bed (SMB) optimizer developed by the Process Information Engineering lab at the Department of Materials Process Engineering, Nagoya University.
Data-driven computer-aided molecular and process design
This is my second project of the course "Plant Design and Economics" that I managed to get full marks on my 4th year of bachelor's chemical engineering program at sharif university of technology.
Molecular design of solvents coupling variational autoencoder and nonlinear programming
This is my first project of the course "Plant Design and Economics" that I managed to get full marks on my 4th year of bachelor's chemical engineering program at sharif university of technology.
Reference Python implementation of the Success-Based Optimization Algorithm (SBOA), a metaheuristic optimizer (Lara-Montaño et al., 2025, Comput. Chem. Eng.).
This is my final project of the course "Plant Design and Economics" that I managed to get full marks on my 4th year of bachelor's chemical engineering program at sharif university of technology.
Chance-constrained design of decentralized plastic-pyrolysis networks under feedstock-composition uncertainty.
A revisited study of steady-state process data reconciliation: a 2022 Excel/GEKKO experiment preserved, with a transparent and tested Python implementation. Reconciled values are model-constrained estimates, not ground truth.
Admissibility-aware, fail-closed LLM decision support for process systems engineering
Experimental characterization, process modelling, and DWSIM simulation of steam pasteurization for sugarcane bagasse.
Engineering analysis, digital twin development, and industrial analytics for heat exchanger fouling assessment.
P. Petsagkourakis, I. O. Sandoval, E. Bradford, D. Zhang, E.A. del Rio-Chanona, Reinforcement learning for batch bioprocess optimization, Computers & Chemical Engineering, Volume 133, 2020
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