Materials & Chemistry
Chemical Engineering
Discover reaction rate laws, transport correlations, and conservation equations directly from reactor and separation train data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Chemical Engineering teams still struggle to explain what is happening
Chemical engineering processes (reactors, distillation columns, heat exchangers, separation trains) generate continuous process data: temperature profiles, pressure readings, flow rates, composition measurements, and conversion metrics. The governing relationships are rooted in conservation laws, reaction kinetics, and transport phenomena, but extracting accurate rate laws from real plant data remains a persistent challenge. Process conditions drift, instruments degrade, and side reactions introduce unmodeled dynamics. Engineers typically rely on textbook kinetic forms or small-scale laboratory fits that do not translate reliably to industrial operating conditions.
Traditional reaction engineering assumes known stoichiometry and pre-specified rate law forms (Arrhenius kinetics, power-law models, Langmuir-Hinshelwood mechanisms), then fits parameters from controlled experiments. These frequently fail under industrial conditions where heat and mass transfer limitations, catalyst deactivation, and impurity effects alter effective kinetics. Separation processes face analogous challenges: thermodynamic models calibrated on pure-component data deviate for complex industrial mixtures, leading to suboptimal process design and over-conservative safety margins.
The CDE Approach
How CDE closes the explanation gap in chemical engineering
CDE takes raw process data (reactor temperature and concentration time-series, distillation tray compositions, heat exchanger duty measurements) and discovers governing kinetics, transport correlations, and conservation relationships directly from operating data. Conservation law detection is particularly valuable for chemical systems, automatically validating material and energy balances and identifying violations that indicate measurement errors, instrument drift, or unknown side reactions.
Symbolic mode excels at extracting closed-form rate laws and heat transfer correlations that process engineers can interpret, validate, and implement in plant control systems. The Evidence Ledger records every discovery with full provenance (data lineage, configuration, replay recipe), meeting process safety management and regulatory compliance requirements. Stability analysis and a suite of negative controls ensure discovered kinetics reflect genuine physical relationships.
Discovery Engine
How CDE applies here
Symbolic discovery identifies closed-form rate laws, mass transfer correlations, and thermodynamic relationships that integrate directly into process simulators and control systems. Causal mode maps relationships between operating parameters (temperature, pressure, catalyst age, feed composition) and outcomes like conversion, selectivity, and purity, enabling root-cause analysis when processes deviate. Neuro-Symbolic mode handles complex reaction networks where symbolic forms alone may not capture the full dynamics.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Reaction rate governing equations
- Heat transfer correlations
- Mass transport laws
- Conservation law validation
- Catalyst deactivation dynamics
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For chemical engineering, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real chemical engineering problem
Whether you are exploring chemical engineering data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.