process-systems-engineering

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.

AI-assisted Aspen HYSYS skill for existing-case takeover, validation, and reporting. AI 辅助接管已有 HYSYS case，完成验证、导出和报告。

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.

Engineering research in process systems engineering through modelling, simulation, technical investigation, and validation.

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.

Reference Python implementation of the Success-Based Optimization Algorithm (SBOA), a metaheuristic optimizer (Lara-Montaño et al., 2025, Comput. Chem. Eng.).

Chance-constrained design of decentralized plastic-pyrolysis networks under feedstock-composition uncertainty.

Admissibility-aware, fail-closed LLM decision support for process systems engineering

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.

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