April 10-14, 2016
Predictive steady-state and dynamic models are essential for optimal design and scale up of CO2 capture processes. The models should be able to predict accurately across all scales and required operating conditions with quantified uncertainty. The U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI) process modeling team has been working on the development of a framework to develop such models. This framework is demonstrated on a typical amine-based system which is highly non-ideal and can exhibit large nonlinearities and therefore serves as a nice platform to test the framework. To validate both steady state and dynamic models developed using this framework, the team recently collaborated with the National Carbon Capture Center (NCCC) in Wilsonville, AL to obtain both steady-state and dynamic data under widely varying operating conditions. The dynamic test runs were conducted by introducing step changes in the solvent, flue gas, and reboiler steam flowrates and recording the transients of all key variables. The step tests were designed to approximately maintain persistence of excitation in order to provide information across the entire spectrum of data including both high and low frequency information. The measured data include the transient response of all the sensors in the pilot plant including the gas composition sensors. Due to measurement noise and inconsistencies in the sensor data, a dynamic data reconciliation approach is developed to guarantee mass and energy balances. This framework for the development of predictive models is then extended to a non-aqueous solvent that is under development. This solvent can be regenerated at a much higher pressure than the traditional amine solvents and therefore can result in reduced energy penalty for desorption as well as reduction in energy requirement for CO2 compression. However this solvent has much higher viscosity compared to traditional solvents and exhibits significantly different thermodynamic and transport properties resulting in numerous modeling challenges. The steady-state model of this high-viscosity solvent is validated by using the bench scale data.
Debangsu Bhattacharyya, Anderson Soares, Joshua Morgan, Benjamin Omell, Sarah Genovese, and David MIller, "Predictive models of carbon capture systems and their validation using bench scale and pilot scale data" in "CO2 Summit II: Technologies and Opportunities", Holly Krutka, Tri-State Generation & Transmission Association Inc. Frank Zhu, UOP/Honeywell Eds, ECI Symposium Series, (2016). http://dc.engconfintl.org/co2_summit2/26