Online monitoring of gas-liquid dispersion in a tubular reactor using passive acoustic emission

Climate change is one of the most pressing challenges facing our planet today. It is characterized by rising temperatures, disrupted weather patterns and profound environmental impacts. The accumulation of greenhouse gases (GHGs) in the Earth's atmosphere, with water vapor, CO2, CH4 and ozone being the main contributors, is the main catalyst for this phenomenon. In view of the significant greenhouse effect associated with CO2 and CH4, the search for efficient utilization routes for these gases is of paramount importance. There are a number of strategies to reduce the level of CO2 in the atmosphere, with the main focus on the capture, sequestration and storage of CO2. However, in order to significantly reduce overall emissions, it is equally important to achieve a zero-emissions pathway. In this context, industrial chemistry plays a fundamental role, as it has the task of researching new strategies to reduce greenhouse gas emissions and control these processes with extreme efficiency. The present work fits into this scenario by studying an innovative control system based on the recording and subsequent processing of passive acoustic emissions generated inside the tube: in fact, every phenomenon in the process generates emissions. This is a strong point for the future, as it is a non-invasive sensor, which is therefore positioned outside the tube, is very precise and provides data in real time. The aim for the future is to combine this analysis system with the programming of machine learning, a branch of artificial intelligence that brings together methods developed in the last decades of the 20th century in different scientific communities under different names such as: computational statistics, pattern recognition, artificial neural networks, adaptive filtering, dynamic systems theory, image processing, data mining and adaptive algorithms; it uses statistical methods to improve the performance of an algorithm in identifying patterns in data.