Predictive models of carbon capture systems and their validation using bench scale and pilot scale data

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

Development of a Dynamic Model and Control System for Load-Following Studies of Supercritical Pulverized Coal Power Plants

Traditional energy production plants are increasingly forced to cycle their load and operate under low-load conditions in response to growth in intermittent renewable generation. A plant-wide dynamic model of a supercritical pulverized coal (SCPC) power plant has been developed in the Aspen Plus Dynamics® (APD) software environment and the impact of advanced control strategies on the transient responses of the key variables to load-following operation and disturbances can be studied. Models of various key unit operations, such as the steam turbine, are developed in Aspen Custom Modeler® (ACM) and integrated in the APD environment. A coordinated control system (CCS) is developed above the regulatory control layer. Three control configurations are evaluated for the control of the main steam; the reheat steam temperature is also controlled. For studying servo control performance of the CCS, the load is decreased from 100% to 40% at a ramp rate of 3% load per min. The impact of a disturbance due to a change in the coal feed composition is also studied. The CCS is found to yield satisfactory performance for both servo control and disturbance rejection

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. A systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions