How do you design an inferential measurement system?

Inferential measurements estimate unmeasured variables from measured ones using process models. Design steps: identify measurable variables correlated with target variable, collect operating data covering expected range, develop model (first principles, empirical regression, or neural network), validate on independent data, implement online calculation, and configure bias update from analyzer feedback. Applications: distillation composition from temperatures, reactor conversion from conditions. Considerations: model robustness, input variable reliability, drift compensation, and fail-safe behavior. Regular validation against lab or analyzer data required.