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.

涉及气液搅拌容器的操作在生化和化学工业中很常见。确保两个阶段之间的良好接触对于过程性能至关重要。在这项工作中，开发了一种计算声发射数据的方法，该方法使用压电传感器记录，以评估气液混合物和气固液混合物中的气液混合状态。该系统是装有Rushton涡轮和环形喷头的3L搅拌罐。在通过容器向上移动时，气泡破裂，破裂或聚结，产生声波，该声波通过壁传输到声发射器。通过改变叶轮速度和气体流速，该系统可以在不同的流动状态（非充气状态，负载和完全分散）下运行，目的是向机器学习算法提供声谱，以明确识别不同的条件。所开发的方法可以在不存在和存在悬浮颗粒的情况下，以高于90％的准确度成功识别出操作方案。