2. Problems with other controls
systems
• Controlled variable cannot be measured or has
large sampling period.
3. Possible solutions
• Control a related variable (e.g., temperature instead of
composition).
• Inferential control: Control is based on an estimate of
the controlled variable.
• The estimate is based on available measurements.
• Examples: empirical relation, Kalman filter
• Modern term: soft sensor
4. • In inferential control, the controlled variables
that are difficult to measure are estimated from
some easy to measure process variables and then
used in feedback control.
• Inferential control system has many excellent
performances such as disturbance resisting and
set-point tracking, however, the application is
restricted when strong load disturbance exists or
stable control accuracy and response speed are
highly required in the system.
5. • Uses easily measure process variables (T, P, F) to
infer more difficult to measure quantities such
as compositions and molecular weight.
• Can substantially reduce analyzer delay.
• Can be much less expensive in terms of capital
and operating costs
• Can provide measurements that are not
available any other way.
6. Estimate is based on available measurements
- Inferential Reactor Conversion Control
7. Software sensors (soft sensors)
• The inferred values of the primary variables are
used as feedback signals to an "external"
controller, such as a P+I controller, a predictive
controller, non-linear or even an adaptive
controller. Thus the inferential estimator serves
simply as a software based sensor (soft sensor) .
9. Advantage
• Enable the use of a desired control loop despite
the lack of measurement devices.
• Free from dependence on delayed data (off-line
analysis), leading to better control.
Disadvantages
• Knowledge on the process must be known.
• Wrong estimation leads to wrong control action
and hence detrimental to process operation.
10. How to improve
• Provide measured data periodically (off-line
analysis etc.)
• Better Mode