Abstract
The chemical process industries are facing tighter fugitive emission control standards. Cost efficient, portable, and sensitive sensors are needed to attack this leak detection problem in chemical plants. This paper reports a first step towards this aim, i.e. optimal tuning of micro-hotplate chemical gas sensors to recognize two similar vapors such as methanol and ethanol in air. Experiments are conducted by flowing gas over the sensor at a constant rate and concentration. Heater current pulse amplitudes (micro-hotplate temperatures) are varied randomly to generate a large database for training empirical models. After studying different data-based dynamic modeling techniques, wavelet networks (WNET) method proposed by Zhang and Benveniste was found to give the most accurate predictions for the methanol and ethanol responses.
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