use of Artificial Neural Network models for CO₂ capture plants

• Published in: Applied energy 2011-07, Vol.88 (7), p.2368-2376
• Main Authors: Sipöcz, Nikolett, Tobiesen, Finn Andrew, Assadi, Mohsen
• Format: Article
• Language: English
• Subjects: absorption; Air pollution caused by fuel industries; algorithms; ANN CO2 capture Chemical absorption Levenberg-Marquardt Scaled Conjugate Gradient; Applied sciences; carbon dioxide; Energy; Energy. Thermal use of fuels; Exact sciences and technology; heat; Installations for energy generation and conversion: thermal and electrical energy; neural networks; Pollution reduction; Stack gas and industrial effluent processing; steam; Thermal power plants
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2011.01.013

Artificial Neural Networks (ANN) are multifaceted tools that can be used to model and predict various complex and highly non-linear processes. This paper presents the development and validation of an ANN model of a CO₂ capture plant. An evaluation of the concept is made of the usefulness of the ANN model as well as a discussion of its feasibility for further integration into a conventional heat and mass balance programme. It is shown that the trained ANN model can reproduce the results of a rigorous process simulator in fraction of the simulation time. A multilayer feed-forward form of Artificial Neural Network was used to capture and model the non-linear relationship between inputs and outputs of the CO₂ capture process. The data used for training and validation of the ANN were obtained using the process simulator CO2SIM. The ANN model was trained by performing fully automatic batch simulations using CO2SIM over the entire range of actual operation for an amine based absorption plant. The trained model was then used for finding the optimum operation for the example plant with respect to lowest possible specific steam duty and maximum CO₂ capture rate. Two different algorithms have been used and compared for the training of the ANN and a sensitivity analysis was carried out to find the minimum number of input parameters needed while maintaining sufficient accuracy of the model. The reproducibility shows error less than 0.2% for the closed loop absorber/desorber plant. The results of this study show that trained ANN models are very useful for fast simulation of complex steady state process with high reproducibility of the rigorous model.