Abstract
A neural network has been used to reduce the effects of background temperature fluctuations in thermal monitoring systems. In this approach, ambient temperature signals determined at specified points external to the sensor were used as inputs to a network designed to map them to the temperature fluctuations at the target point where the sensor is located. Once trained, the network allowed the effects of external fluctuations to be subtracted from the sensor signal such that sensitivity could be improved considerably. In the test case studied, the thermal monitoring of phenol adsorption onto a carbon bed, the results demonstrated levels of improvement sufficient to allow detection in the absence of external temperature control.
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