Science and engineering
Machine learning for chemical engineering
Chemical engineers use machine learning to detect process faults early, to build soft sensors that estimate hard-to-measure qualities from routine plant data, and to model reactor and emission behaviour for optimisation and control.
How machine learning is used in chemical engineering
Detecting process faults early
Models learn what normal operation looks like from historian data across hundreds of sensors and flag deviations before they become alarms or trips. PCA and PLS monitoring charts are the classic tools, and autoencoders and recurrent networks add the ability to capture process dynamics.
Real exampleHierarchical recurrent network for fault detection (arXiv, 2020)Proposed a supervised recurrent autoencoder for detecting and classifying incipient faults and tested it on the Tennessee Eastman benchmark process, reporting improvements over linear and non-hierarchical baselines.
Soft sensors for hard-to-measure qualities
A soft sensor estimates a quality that is measured rarely or slowly in the lab, such as viscosity or purity, from temperatures, pressures and flows logged every few seconds. Operators can then control quality between samples. Partial least squares, Gaussian processes and neural networks are common choices.
Real exampleDeep kernel learning soft sensor for rubber mixing (Sensors, 2020)Proposed a semi-supervised soft sensor that combines a deep belief network with kernel regression to predict Mooney viscosity in industrial rubber mixing.
Optimising reactions and operating conditions
Bayesian optimisation chooses the next experiment or operating point by weighing what a surrogate model predicts against how uncertain it is. It usually reaches good conditions in fewer runs than grid searches, which matters when each run costs a day or a batch of material.
Real exampleBayesian reaction optimisation (Shields et al., Nature, 2021)Benchmarked Bayesian optimisation against human decision-making in reaction optimisation and found it outperformed humans in average efficiency and consistency.
Model-based optimisation of plant control
Models trained on plant history predict how setpoint changes affect energy use and constraints, and an optimiser recommends or applies the best changes. Safety limits and operator oversight stay in the loop.
Real exampleAutonomous data centre cooling control (Google DeepMind, 2018)Every five minutes, deep neural networks predict how candidate cooling actions affect future energy use, and the system picks actions that minimise energy while meeting safety constraints.
Can chemical engineers learn machine learning?
Yes. Chemical engineers already know mass and energy balances, process control and statistics, which covers much of what process data work needs. Add Python, time-series data handling and classical models such as PLS and gradient boosting, then monitoring and optimisation methods.
- Python programmingWrite scripts and notebooks that automate the spreadsheet analyses you already do.Learn it in Python
- Time-series data handlingAlign, resample and clean historian data with pandas, and match lab results to the right process window.Learn it in Data Tools
- Multivariate statisticsPCA, PLS and control charts are the backbone of process monitoring and soft sensors.Learn it in Statistics
- Supervised learning and validationTrain regressors and classifiers, and validate on later time periods rather than random splits so results reflect real operation.Learn it in Machine Learning
- Optimisation and controlCombine models with Bayesian optimisation or model predictive control, keeping safety constraints explicit.
Moving into machine learning for chemical engineering
If you already work in the field
Process engineers understand the plant, the instruments and why a correlation might be spurious, which is the scarce skill in industrial machine learning. Learn Python and a few core methods, then build a soft sensor or fault detector on historian data from a unit you know, with permission. Many first projects start inside an existing role as part of a digitalisation effort.
If you come from data science or ML
Data scientists need process basics: unit operations, control loops, steady state versus transient behaviour and how sensors drift. Pair with a process engineer, study the Tennessee Eastman benchmark and learn why shuffled train-test splits overstate accuracy on plant data.
Roles that do this workProcess data scientistDigital process engineerAdvanced process control engineerProcess systems engineer
Tools you will use
| Tool | What you use it for |
|---|---|
| scikit-learn | Fit PCA monitoring models, soft-sensor regressions and fault classifiers on process tables. |
| PySINDy | Discover compact equations for reactor and process dynamics from measured time series. |
| Cantera | Compute kinetics, thermodynamics and transport properties to generate or check training data. |
| CasADi | Formulate optimisation and model predictive control problems that use a learned process model. |
Public data to practise on
- Tennessee Eastman Process simulation data: Simulated chemical-plant runs with normal operation and labelled faults for training and testing fault detectors.
- Gas Turbine CO and NOx Emission (UCI): Hourly sensor readings from a gas turbine, used to predict carbon monoxide and nitrogen oxide emissions.
Your first project
Download the Tennessee Eastman Process data, flag faulty runs with PCA reconstruction error, and compare it with per-variable three-sigma control limits using detection rate and false-alarm rate.
Common questions
Is machine learning useful for chemical engineers?
Yes, mainly for process monitoring, soft sensing, optimisation and faster models of reactors and separations. It works best combined with first-principles knowledge, because plant data rarely covers every operating condition.
Which programming language should chemical engineers learn for machine learning?
Python. It has the main machine learning libraries, good time-series tools and is the language most job descriptions in this area ask for. MATLAB remains common in control, so knowing both helps.
What is the Tennessee Eastman process?
It is a simulated chemical plant published by Eastman Chemical Company as a benchmark for process control and monitoring. It includes normal operation and a set of programmed faults, which makes it the standard test case for fault detection methods.
Can machine learning replace first-principles process models?
Rarely on its own. Hybrid models, where machine learning corrects or fills in part of a mechanistic model, are common because they extrapolate better and keep mass and energy balances intact.
Courses for these skills
- Practical Machine Learning in PythonLearn regression and representation comparisons.51 published lessons · more lessons in production
- Data Tools: NumPy, pandas, Matplotlib and scikit-learnAlign process signals and operating metadata.23 published lessons · all planned lessons open
- Deep Learning with PyTorchStudy sequence models and neural optimisation.Syllabus available · lessons open as produced
- Maths for Machine LearningConnect state vectors, derivatives and dynamical behaviour.26 published lessons · all planned lessons open
New to programming or maths? Start with Python, or follow the machine learning path in order. Published lessons are free to read; compare Free and Pro.