Modelling of a post-combustion CO2 capture process using deep belief network

作者：Li, Fei; Zhang, Jie^*; Shang, Chao; Huang, Dexian; Oko, Eni; Wang, Meihong

来源：Applied Thermal Engineering, 2018, 130: 997-1003.

DOI：10.1016/j.applthermaleng.2017.11.078

• CO(2)capture
• Chemical absorption
• Deep belief network
• Restricted Boltzmann machine

This paper presents a study on using deep learning for the modelling of a post-combustion CO2 capture process. Deep learning has emerged as a very powerful tool in machine learning. Deep learning technique includes two phases: an unsupervised pre-training phase and a supervised back-propagation phase. In the unsupervised pre-training phase, a deep belief network (DBN) is pre-trained to obtain initial weights of the subsequent supervised phase. In the supervised back-propagation phase, the network weights are fine-tuned in a supervised manner. DBN with many layers of Restricted Boltzmann Machine (RBM) can extract a deep hierarchical representation of training data. In terms of the CO2 capture process, the DBN model predicts CO2 production rate and CO2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. A greedy layer wise unsupervised learning algorithm is introduced to optimize DBN, which can bring better generalization than a single hidden layer neural network. The developed deep architecture network models can then be used in the optimisation of the CO2 capture process.

• 出版日期2018-2-5
• 单位清华大学