The use of Artificial Neural Network models for CO₂ capture plants

Abstract

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.

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.