Department of Instrumentation and Automation Engineering, Petroleum University of Technology,
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Industrial plants are usually heavily instrumented with a large number of sensors. The primary purpose of the sensors is to deliver data for process monitoring and control. In the context of process industry, these predictive models are called Inferential Sensors. Other common terms for predictive sensors in the process industry are soft sensor, virtual on-line analyzer as they are called in the Six-Sigma context and observer-based sensors.
Prediction algorithms using inferential (soft) sensors have recently become very powerful tools to a wide array of real-world applications. The most common application of Inferential Sensors is the prediction of values which cannot be measured on-line using automated measurements. For example, we can use an adaptive learning machine to estimate bottom Benzene concentration in a distillation column as a common industrial unit or another variables related to chemical industries. The main machine could be considered Fuzzy system or Neural network. Conventional Learning of considered machine can be tested by real industrial processes or laboratory variables. The employed machine should be proportional to the variable used in the model. In the prediction mode, the results can confirm that the designed inferential sensor based on the proposed method is accurate, optimized and faster and novel in response for the process variations.