Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Chemical and Biochemical Engineering.
Engineering Research Community
Chemical and Biochemical Engineering & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Chemical and Biochemical Engineering
Chemical and Biochemical Engineering combines transport phenomena, thermodynamics, reaction engineering, separations, process systems and safety with microbial, enzyme and cell-based production. It spans petrochemical and sustainable chemical processes, biorefineries, fermentation, pharmaceuticals and environmental systems.
PIML can couple conserved chemical process states with uncertain reaction or biological kinetics. The combined programme is especially suited to hybrid flowsheets in which chemical and biological unit operations exchange material, energy and quality information.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Chemical and Biochemical 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 ideaEstablished Chemical and Biochemical Engineering knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Chemical and Biochemical 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 and Biochemical Engineering PIML Research Areas
Each card connects a meaningful Chemical and Biochemical Engineering question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Chemical Reactor Modelling
Kinetics and transport interact under uncertain mechanisms. PIML opportunities: Learn bounded rate or mixing residuals around reactor balances.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Fermentation and Cell Culture
Biological rates vary by strain, batch and environment. PIML opportunities: Use balance-constrained neural ODEs for hidden states and kinetics.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Biorefineries
Chemical pretreatment, biology and separations form a recycle flowsheet. PIML opportunities: Build modular hybrid unit models with conserved interconnections.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Crystallization
Nucleation/growth determine distributions and quality. PIML opportunities: Embed population balances in recurrent models and MPC.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Distillation and Absorption
Equilibria, hydraulics and mass transfer determine separation. PIML opportunities: Learn tray/packing efficiency residuals within balance models.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Chromatography and Adsorption
Transport and binding create nonlinear profiles. PIML opportunities: Use PDE-informed surrogates for calibration and cycle design.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Membranes
Flux, rejection, fouling and polarization evolve. PIML opportunities: Retain transport laws and learn degradation/resistance terms.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Process Soft Sensing
Important quality states are delayed or unmeasured. PIML opportunities: Use balances and measurement models for virtual sensing.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Model Predictive Control
Detailed nonlinear models are expensive online. PIML opportunities: Train constraint-aware hybrid surrogates and verify closed-loop behaviour.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Fault Diagnosis
Faults cause structured balance and equipment residuals. PIML opportunities: Fuse process topology, signed residuals and time-series learning.
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.
- CSTR balance-constrained learner
- fermentation neural ODE
- distillation residual soft sensor
- membrane fouling hybrid model
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.
- crystallizer physics-informed MPC
- modular biorefinery twin
- multi-fidelity reactor scale-up
- probabilistic process fault diagnosis
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.
- differentiable chemical–biochemical flowsheets
- transferable reaction/bioprocess foundation models
- safe learning-based plant control
- uncertainty-aware net-zero process synthesis
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 and Biochemical 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 and Biochemical 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 and Biochemical 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 and Biochemical Engineering.
Read publication or record This source is included in the Chemical and Biochemical 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 and Biochemical Engineering.
Read publication or record This source is included in the Chemical and Biochemical 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 and Biochemical Engineering.
Read publication or record This source is included in the Chemical and Biochemical 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 and Biochemical Engineering.
Read publication or record This source is included in the Chemical and Biochemical 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 and Biochemical Engineering.
Read publication or record This source is included in the Chemical and Biochemical 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 and Biochemical Engineering.
Read publication or record Build an interdisciplinary team
Where Chemical and Biochemical Engineering Can Collaborate
Computer Science
Scientific ML, optimization, trustworthy AI and reproducible research software.
Applied Mathematics
Differential equations, numerical methods, inverse problems and uncertainty.
Sensing & Control
Instrumentation, data acquisition, state estimation and responsible deployment.
Domain Laboratories
Experiments, calibration, validation evidence and practical expertise for Chemical and Biochemical Engineering.
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 and Biochemical 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 and Biochemical 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.