The use of Artificial Neural Network models for CO2 capture plants
Introduction
The increased competitiveness due to deregulation of the electricity market and more stringent laws for environmental protection has driven the technology development in the power sector over the last two decades. This development has also lead to focus on CO2 abatement. It is of great importance to find an optimal technology for capturing CO2 as well as to integrate and optimize the capture technology with the power plant. Today most researchers believe that CO2 sequestration is necessary from fossil fuels, thus there is great emphasis towards viable CO2 capture technologies for a future low carbon economy. Capture of CO2 from flue gases by the utilization of chemical solvents, such as amines, is a proven technology, although not yet at the scales required for full scale capture. However, it is currently the most viable option for CO2 removal from exhaust gases and two commercialised processes exist and others are currently under development [1], [2]. However the high energy requirement for solvent regeneration will reduce the effectiveness of the power plant with about 10% for a traditional amine MEA solvent as well as increase power plant operational costs [3], [4], [5], [6], [7]. These effects have been studied in various techno-economic evaluations using commercially available simulation tools such as Aspen Plus, Hysys and gPROMS. Although many commercial simulators offer advanced features such as customizing component models for the application in hand, the possibility to carry out sophisticated process simulations by means of flexible user defined component models is limited. This restriction is mainly due to the limited access to the underlying sub models used which gives rise to uncertainties as to the underlying theory and assumptions made. They thus act as “black-boxes” without revealing the theory the simulator is based upon [8]. Nevertheless, it is of importance that simulation tools used for research purposes have the level of flexibility allowing for incorporation of specific component characteristics to be able to perform evaluation studies for design and optimization.
Previous works from our research group include development of power plant component for different power plant applications such as analysis, monitoring and optimization at both design and off design conditions [9], [10], [11], [12], [13]. These models which rely on chemical-, fluid mechanic- and thermodynamic laws both require and provide extensive knowledge of the underlying physics of the process. However, processes like details of gas turbines, absorption- and desorption processes in CO2 removal plants involve non-ideal characteristics and accurate modeling of these systems are necessary in order to obtain sufficient details of the particular system, such as column heights. The solution of these systems are iterative procedures which for purposes such as certain operating point analysis and optimization studies are very complex and computationally very time consuming. For this reason, Artificial Neural Networks (ANN) has been considered as a valuable alternative modeling approach to replicate the rigorous model and at the same time obtain the same level of detail.
In this work an ANN model of a CO2 capture plant based on a chemical absorption process utilizing monoethanolamine (MEA) has been developed for a feasibility study. The data for the ANN model has been generated by the process simulation tool CO2SIM [8], which has the ability to model CO2 absorption processes using rate based modeling [8], [14], [15].
The objective of this article is to present the application of the developed ANN for a chemical absorption capture system, by capturing and finding relationships among inputs and outputs represented by the data sets of the absorption and desorption cycles. In a follow-up study the developed ANN model representing the CO2 capture plant will be implemented into a simulator particularly developed for power plant modeling. In this respect the simulators which are suited for each section of the power plant will be used to give the best predictive power for a techno-economic study of power plants with CO2 removal.
Section snippets
Brief description of Artificial Neural Networks
ANN technology is a non-parametric-, statistical modeling tool and does not require any pre-assumption of the input–output relationship. It has also been shown that ANN has the ability to approximate any non-linear system with high interpolation capacity [16]. ANN are especially suited for high dimension modeling since the number of free parameters increases linearly with the dimension compared to e.g. polynomial fitting where the number of free parameters increases exponentially [17], thus
Description of a CO2 absorption capture plant
The CO2 removal system used in this work is a conventional amine absorption process as illustrated in Fig. 1. The process consists of two main units – the absorber and desorber columns, which are both of packed bed type. The flue gas from the power plant flows counter currently with the lean MEA solution through the absorber. The absorbent, MEA, reacts chemically with the CO2 in the flue gas. The treated gas stream of lower CO2 content is further treated in a water wash section at the top of
Selection of input and output parameters
Before training of the network is initiated a selection of input- and output parameters has to be made. As mentioned, special attention was paid to include not only parameters important for reflecting the behavior of the actual CO2 capture process, but also those necessary for inclusion into the power plant simulator, in this case IPSEpro. The size of the flue gas mass flow, flue gas temperature, CO2 removal efficiency and amount CO2 in the flue gas, corresponded to the definition of the power
Comparison of scaled conjugate- and Levenberg–Marquardt algorithms
The training procedure with the SCG algorithm was carried out with a variation of 12–30 neurons and 40,000 epochs. The best solution converged at 18 neurons and 40,000 epochs. Training with additional epochs did not improve the results. The training of the network with LM algorithm was made with the same variation of neurons as with the SCG algorithm, but the number of epochs was limited to 5000. The reason for not performing training with a higher number of epochs is due to limitation in
Conclusions
Artificial Neural Networks are found to be useful tools for predicting complex processes such as CO2 capture processes, which, when simulating the closed process network, yields a challenging solution pathway which is computationally demanding and difficult to simulate using traditional methods. A trained ANN can replicate such complex processes without loosing accuracy. This is demonstrated by the development of two ANN models of a CO2 capture plant for which high prediction accuracy was
Future work
This work has evaluated ANN as tool for modeling amine based CO2 capture processes. The original ANN model described in the work has been integrated externally into our existing heat and mass balance component library in IPSEpro using the traditional programming language C++ [25]. These external functions are introduced in the IPSEpro framework based on the Dynamic Link Library (DLL) concept. The description of the implementation and further analysis of the ANN model performance in IPSEpro
Acknowledgement
The authors would like to thank Thomas Palmé for his technical support and profound knowledge about ANN modelling.
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