Abstract
Operations involving gas–liquid agitated vessels are common in the biochemical and chemical industry; ensuring good contact between the two phases is essential to process performance. In this work, a methodology to compute acoustic emission data, recorded using a piezoelectric sensor, to evaluate the gas–liquid mixing regime within gas–liquid and gas–solid–liquid mixtures was developed. The system was a 3L stirred tank equipped with a Rushton Turbine and a ring sparger. Whilst moving up through the vessel, gas bubbles collapse, break or coalesce generating sound waves transmitted through the wall to the acoustic transmitter. The system was operated in different flow regimes (non-gassed condition, loaded and complete dispersion) achieved by varying impeller speed and gas flow rate, with the objective to feed machine learning algorithms with the acoustic spectrum to univocally identify the different conditions. The developed method allowed to successfully recognise the operating regime with an accuracy higher than 90% both in absence and presence of suspended particles.
Highlights
Improving process monitoring is a common need within the process industry, including chemical, food, biochemical and pharmaceuticals (Boyd and Varley 2001)
acoustic emission (AE) are usually acquired at high frequency resulting in a large amount of data; this can cause delay in processing and at the same time represents a challenge for the algorithms to avoid biasing and overfitting
After ranking the frequency spectrum based on decreasing variance, a variable number, n, of frequencies is fed to the training process of the different algorithms
Summary
Improving process monitoring is a common need within the process industry, including chemical, food, biochemical and pharmaceuticals (Boyd and Varley 2001). Amongst several potential sensing methods, acoustic emission (AE) is a low cost, data-rich technique with applicability for in-line monitoring. Acoustic techniques are categorised as either with active or passive acoustics. The former consists of a transmitter generating an acoustic wave within the system and a receiver acquiring the response of the stimulated system. The latter, known as acoustic emission (AE), is composed only by a sensor recording acoustic waves generated by the process itself
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