Soft sensors are inferential estimators, drawing conclusions from
process observations when hardware sensors are unavailable or
unsuitable; they have an important auxiliary role in sensor
validation when performance declines through senescence or fault
accumulation.
The non-linear behaviour exhibited by many industrial processes
can be usefully modelled with the techniques of computational
intelligence: neural networks; fuzzy systems and nonlinear partial
least squares.
Soft Sensors for Monitoring and Control of Industrial Processes
underlines the real usefulness of each approach and the sensitivity
of the individual steps in soft-sensor design to the choice of one
or the other. Design paths are suggested and readers shown how to
evaluate the effects of their choices. All the case studies
reported, resulting from collaborations between the authors and a
number of industrial partners, raised challenging
soft-sensor-design problems. The applications of soft sensors
presented in this volume are designed to cope with the whole range
from measuring system backup and what-if analysis through real-time
prediction for plant control to sensor diagnosis and validation.
Some of the soft sensors developed here are implemented on-line at
industrial plants.
Features:
- soft-sensor design;
- advice on data selection and choice of model structure;
- model validation;
- strategies for the improvement of soft-sensor performance;
- uses of soft sensors in fault detection and sensor
validation;
- soft sensors in use in industrial applications such as a
debutanizer column and a sulfur recovery unit.
This monograph guides interested readers a"researchers, graduate
students and industrial process technologists a" through the design
of their own soft sensors. It is self-contained with full
references and appraisal of existing literature and data sets for
some of the case studies can be downloaded from springer.com.
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