Machine-learning-based advanced process control that continuously retrains against live plant data, rather than the fixed linear models of first-generation APC.
AI-based soft sensors / virtual metering
Machine-learning models that infer a hard-to-measure variable — composition, quality, emissions — from cheaper, faster process signals already being collected.
Short entry. This page summarises our current placement. Full, sourced analyses are published first for the technologies closest to practical use; see the list of full analyses.
How it works
A soft sensor, also called an inferential or virtual sensor, is a model that estimates a variable you cannot measure cheaply or quickly from variables you already measure. Temperatures, pressures, flows and valve positions go in; a product quality, composition or emission rate comes out.
The model is trained against reference data, typically laboratory samples or a physical analyser, using anything from partial least squares regression to neural networks, sometimes combined with first-principles equations. Because processes and feedstocks change, a working soft sensor is periodically bias-corrected against fresh lab results and monitored for inputs that fall outside the range it was trained on.
Why it matters
Cuts capital cost and lag time versus physical analysers for quality and emissions variables, and can fill gaps when a physical analyser is down for maintenance.
Where it fits
Soft sensors are well suited to quality variables that are sampled a few times a shift, to filling the gap while an analyser is out of service, and to giving an advanced process controller a faster feedback signal than the lab can. For emissions, some regulators accept predictive emissions monitoring systems under defined performance tests, but acceptance is jurisdiction-specific and should be confirmed before a physical analyser is designed out.
Why we place it at the Over-Promise stage
Vendor claims are running well ahead of validated, audited performance in regulated (e.g. emissions-reporting) applications — a classic peak-of-expectations signature. Estimated time to the Proven in Use stage: 2–5 yrs. What the stages mean.
Key risk
Model reliability outside its training envelope, and regulator acceptance of AI-inferred values in place of certified analysers, remain open questions.
Questions to ask before committing
- What reference measurement will be used to validate and re-bias the model, and how often?
- How does the system detect and flag operation outside its training envelope?
- If the value feeds closed-loop control or a regulatory report, who has accepted that use in writing?
- Who will own model maintenance after the project team leaves?
Standards and terms to know
- Inferential
- The traditional process-control name for a model-based estimate of an unmeasured variable.
- PEMS
- Predictive emissions monitoring system, a soft sensor used in place of or alongside a continuous emissions analyser.
- Training envelope
- The range of operating conditions represented in the data the model learned from.
Related technologies
See this entry on the full maturity map · All Instrumentation entries · How placements are made. Stage and time-horizon placements are editorial judgements based on public information as of October 2026, not engineering, safety or investment advice; see the Terms of Use.