Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Chemical Engineering.
Engineering Research Community
Chemical Engineering & Physics-Informed Machine Learning
Integrating transport, thermodynamics, reaction engineering and process data
Chemical Engineering is one of the most naturally suited disciplines for Physics-Informed Machine Learning (PIML) because chemical-engineering systems are already described by a rich foundation of physical laws, conservation equations, thermodynamics, transport phenomena, reaction kinetics and process dynamics.
conservation of mass; * conservation of momentum; * conservation of energy; * heat transfer; * mass transfer; * fluid mechanics; * chemical reaction kinetics; * thermodynamics; * phase equilibria; * reactor design equations; * population balances; * adsorption and separation models; * process-control equations; and * coupled multiphysics models.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Chemical Engineering guide covers Physics-Informed Neural Networks (PINNs), physics-guided machine learning, scientific machine learning, neural operators, hybrid models and engineering digital twins. Explore the research and project pathways below, then join the Physics-Informed Machine Learning Society to connect with the international PIMLS community.
The central ideaConservation laws + thermodynamics and kinetics + process data + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Chemical Engineering Needs Physics-Informed Learning
Use available scientific knowledge to make limited data more useful, transparent and testable.
Incomplete engineering models
Learn uncertain parameters, closures or discrepancies around an inspectable mechanistic foundation.
Transfer across conditions
Test whether structured models generalize across geometries, materials, assets, operating regimes or sites.
Trustworthy evidence
Use physical residuals, independent measurements, uncertainty and conventional engineering baselines before deployment.
Ten focused directions
Major Chemical Engineering PIML Research Areas
Each card connects a meaningful Chemical Engineering question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Chemical Reaction Engineering
Chemical reaction engineering is one of the most promising areas for Physics-Informed Machine Learning. Chemical reactors are governed by: Reaction Kinetics Mass Transfer Heat Transfer Fluid Flow Thermodynamics Potential PIML applications include: PIML becomes particularly useful when the complete reaction…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Catalysis and Catalytic Reactors
Catalysis provides an especially interesting research opportunity because catalytic systems frequently involve complex interactions among: Reaction Kinetics Surface Chemistry Adsorption Mass Transfer Heat Transfer Fluid Dynamics A catalytic reactor may contain hundreds of possible chemical reactions and…
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Transport Phenomena
Transport phenomena forms the theoretical backbone of Chemical Engineering. It includes:
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Computational Fluid Dynamics
Chemical engineers frequently use Computational Fluid Dynamics for: Reactors Mixers Pipelines Cyclones Fluidized Beds Separators Combustion Systems Multiphase Systems High-fidelity CFD simulations can become computationally expensive, particularly for: Physics-Informed Machine Learning can potentially create…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Process Systems Engineering
Process Systems Engineering is particularly well aligned with machine learning. It encompasses: Process Modelling Simulation Optimization Control Design Operations Planning A 2025 review of AI and machine learning across Process Systems Engineering discusses their use across scales ranging from molecules and…
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Hybrid Process Modelling
Chemical Engineering already has a long tradition of mechanistic modelling. Instead of discarding these models, researchers can combine them with machine learning. This is commonly referred to as: Hybrid Modelling Science-Guided Machine Learning Physics-Guided Machine Learning Grey-Box Modelling…
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Process Control
Chemical-process control is another important PIML application. Chemical processes are dynamic. Variables such as: Temperature Pressure Composition Level Flow Rate change continuously. PIML can potentially support: A 2026 chemical-engineering perspective identifies real-time dynamics and control as one of…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Soft Sensors and State Estimation
Many important process variables cannot be measured continuously. For example: Product Composition Reaction Conversion Internal Reactor Concentration Catalyst Activity Particle Properties Internal Temperature Fields A soft sensor estimates such variables from other measurements. Traditional soft sensors are…
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Separation Processes
Separation processes consume a significant fraction of energy in the chemical industry. Potential PIML applications include: Distillation Absorption Adsorption Extraction Membrane Separation Chromatography Drying Crystallization Filtration Possible research questions include: ---
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Distillation
Distillation combines: Vapour-Liquid Equilibrium Mass Transfer Energy Transfer Hydraulics These relationships provide strong physical constraints. A PIML model could combine: Tray/packing measurements + thermodynamic models + mass and energy balances to predict: Product Purity Column Temperature Profile…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
Choose the right research depth
Projects for Every Academic Stage
Start with a scope that matches your time, mathematical background, experimental access and expected research contribution.
Project pathway 1
B.E./B.Tech
Learn the foundations with a bounded, measurable system.
- PINN for Batch Reactor
- PINN for CSTR
- Physics-Informed Heat Exchanger Model
- PIML for 1-D Diffusion
- PIML for Reaction Kinetics
- PIML for Adsorption
Expected outcome
A reproducible implementation, clear baselines, a manageable dataset and physically meaningful validation.
Project pathway 2
M.Tech
Combine an engineering model, substantial data and rigorous comparison.
- reactor modelling
- CFD surrogates
- inverse kinetic problems
- hybrid process models
- process digital twins
- soft sensors
Expected outcome
A thesis-quality study with held-out regimes, mechanistic and data-only baselines, ablation and uncertainty.
Project pathway 3
Ph.D.
Address a publishable methodological, multiscale or deployment research gap.
- Multiphysics PINNs
- Multiscale PIML
- Neural Operators for Chemical Processes
- Hybrid Mechanistic-Neural Models
- Physics-Informed Graph Neural Networks
- Bayesian PIML
Expected outcome
New methodology or validated engineering insight, multi-regime evidence, reproducible software and journal publications.
From idea to evidence
A Strong PIML Project Workflow
01
Define
Choose one Chemical Engineering question and a measurable engineering output.
02
Model
State the governing relationships, constraints or validated domain knowledge you will retain.
03
Compare
Build mechanistic and data-only baselines before the hybrid model.
04
Validate
Hold out experiments, conditions, assets, sites or regimes at the deployment level.
05
Publish
Report uncertainty, ablation, limitations, data lineage and reproducible code.
Read before you modelDiscuss Your Literature
Selected Publications and Why They Matter
Use this focused reading list to understand the general PIML framework, direct Chemical Engineering evidence and suitable hybrid modelling methods.
Literature review advice
Do not list papers only. Compare the engineering question, incorporated knowledge, data, split strategy, baselines, uncertainty and evidence level.
This source is included in the Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical Engineering.
Read publication or record This source is included in the Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical Engineering.
Read publication or record This source is included in the Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical Engineering.
Read publication or record This source is included in the Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical Engineering.
Read publication or record This source is included in the Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical Engineering.
Read publication or record This source is included in the Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical Engineering.
Read publication or record Build an interdisciplinary team
Where Chemical Engineering Can Collaborate
Computer Science
Scientific ML, optimization, software and scalable computation.
Mechanical Engineering
CFD, thermal systems, equipment and multiphysics.
Biotechnology
Bioprocesses, pharmaceuticals and biological reaction systems.
Electrical & Control
Instrumentation, process control, electrochemistry and energy systems.
Before you begin
Frequently Asked Research Questions
These answers help students avoid common scope, terminology and validation mistakes.
Still have a question?
Use the biweekly meeting form for research guidance.
Request accessNo. Chemical Engineering projects may use physics-guided features, hybrid residual models, differentiable simulators, neural operators, constrained architectures or data assimilation. State exactly what knowledge is incorporated.
Choose one engineering question, a measurable output and a defensible mechanistic baseline. Add learning only where data can identify an uncertainty or discrepancy.
A meaningful question, justified prior knowledge, deployment-level holdouts, strong baselines, ablation, uncertainty, reproducibility and honest limitations.
Simulation can broaden coverage, but simulation-only evidence cannot establish real-system accuracy. Use calibrated experiments, field measurements or trusted independent references appropriate to the claim.
Submit the biweekly members meeting form to discuss your project level, branch, data, model, validation plan and possible collaborators.
Take the next step
Bring your Chemical Engineering research idea to PIMLS
Join the biweekly members meeting for project guidance, collaboration and publication planning—or contact the Society directly.
Meeting participation is requested through the Google form. Complete it carefully so the Society can understand your research interest.