A precipitating potassium carbonate (K2CO3)-based solvent absorption process has been developed by the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC) for capturing carbon dioxide (CO2) from industrial sources, such as power plant flue gases. Demonstration of this process is underway using both a laboratory-based pilot plant located at The University of Melbourne and an industrial pilot plant located at the Hazelwood Power Station in Victoria, Australia. The laboratory-scale pilot plant has been designed to capture 4–10 kg/h CO2 from an air/CO2 feed gas rate of 30–55 kg/h. The power-station-based pilot plant has been designed to capture up to 1 tonne/day CO2 from the flue gas of a brown-coal-fired power station. In this paper, results from trials using concentrated potassium carbonate (20–40 wt %) solvent are presented for both pilot plants. Performance data (including pressure drop, holdup, solvent loadings, temperature profile, and CO2 removal efficiency) have been collected from each plant and presented for a range of operating conditions. Plant data for the laboratory-scale pilot plant (including temperature profiles, solvent loadings, and exit gas CO2 concentrations) have been used to validate and further develop Aspen Plus simulations, in anticipation of further work involving precipitation and the industry-based pilot plant.

A statistical methodology was applied to the simultaneous calibration and validation of thermodynamic models for the uptake of CO2 in mesoporous silica-supported amines. The methodology is Bayesian, and follows the procedure introduced by Kennedy and O'Hagan. One key aspect of the application presented is the use of quantum chemical calculations to define prior probability distributions for physical model parameters. Inclusion of this prior information proved to be crucial to the identifiability of model parameters against experimental thermogravimetric data. Through the statistical analysis, a quantitative assessment of the accuracy of various quantum chemical methods is produced. Another important aspect of the current approach is the conditioning of the model form discrepancy - a critical component of the Kennedy and O'Hagan methodology - to the experimental data in such a mannner that it becomes an implicit function of the model parameters and thereby connected with the posterior distribution. It is shown that the inclusion of prior information in the analysis leads to a shifting of uncertainty from the posterior distribution for model parameters to this conditioned model form discrepancy. Prospects for more accurate model predictions and propagation of uncertainty in upscaling and extrapolation through a "model-plus-discrepancy" approach are discussed. The synthesis methods and thermogravimetric characterization of hybrid grafted/impregnated mesoporous silica-supported amine sorbents are presented, along with the details of the quantum chemical study, which shows that a carbamic acid-base acceptor complex is the most stable form of adsorbed CO2 in both alkanol- and ethyleneamines.

Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state-of-the-art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES-CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open-source platform for optimization of process flowsheets utilizing state-of-the-art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure-based conceptual design and optimization under uncertainty.

A general one-dimensional (1-D), three-region model for a bubbling fluidized-bed adsorber with internal heat exchangers has been developed. The model can predict the hydrodynamics of the bed and provides axial profiles for all temperatures, concentrations, and velocities. The model is computationally fast and flexible and allows for any system of adsorption and desorption reactions to be modeled, making the model applicable to any adsorption process. The model has been implemented in both gPROMS and Aspen Custom Modeler, and the behavior of the model has been verified. ■ INTRODUCTIONAs part of ongoing research at the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) into ways to reduce carbon dioxide emissions from fossil fuel power plants, solid sorbents have received a large amount of attention because of their potentially lower cost, relative to other CO 2 separation technologies, due to a reduction in energy demands. In order to accelerate the development of carbon capture technologies by industry, the DOE has initiated the Carbon Capture Simulation Initiative (CCSI). CCSI is developing new computational tools to screen alternatives more rapidly, reduce the time for scale-up and troubleshooting new devices and process, and quantify the technical risk in taking technology from laboratory-scale to commercial-scale. The CCSI toolset is being developed and demonstrated around industry challenge problems, the first of which involves the development of a solid sorbent-based capture system. Fluidized-bed reactors are used extensively in a wide range of chemical processes that rely on reactions between gases and solids. Fluidized beds are used in combustion processes, gasification, fluidized catalytic cracking of hydrocarbons, and chemical synthesis. They are currently being studied for their potential to remove CO 2 from flue gas, since they provide good solid−gas contacting and enable effective heat transfer. Historically, scaleup of fluidization processes from bench-scale to industrial-scale processes has required many intermediate scale tests, because the behavior of the system can change considerably between differently sized units. The need for multiple intermediate scale tests makes the scale-up process expensive and time-consuming. Thus, considerable effort has been put into developing predictive models of fluidization systems to aid in the scale-up process.The term "fluidization" encompasses a broad range of different systems, which can show significantly different hydrodynamic behavior, depending on the dimensions, solid properties, and gas and solid feed rates in the system. Geldart and Abrahamsen 1 studied the fluidization behavior of a range of different powers, and observed that powders could be classified into four groups based on their behavior. Group A materials, referred to as aeratable materials, consist of fine particles that show a period of uniform expansion of the fluidized bed after fluidization before the formation of bubbles. Group B materials, referen...