Measurement and Control System in Process of Carbon Dioxide Capture Based on Sparse Support Vector Machine

摘要

Capturing CO2 from the flue gas of a power plant based on chemical absorption method is an effective way to cut down the emission of CO2. However, the principal components in the solvent are very difficult to be measured online. Therefore an online measurement and control system was designed for the process of capturing CO2 by MEA chemical absorption. The feature selection was done according to Bayesian networks. Furthermore based on the principle of a new sparse support vector machine (SLS-SVM), the measurement of ionic species distribution in the absorption solution was predicted, and the results agreed well with the data obtained by Nuclear Magnetic Resonance (NMR) measurement. The mean square error is 3.8645×10-7. The measurement of solution species distribution was carried out online by using infrared sensors and NMR technology. Meanwhile the sampling system and NMR unit were linked for the online analysis based on variable-temperature and variable-pressure NMR technology. Finally a measurement and control platform was constructed based on VI technology. It shows that the performance of traditional equipment can be improved by the novel system, which will provide a more effective method for CO2 capture process.