Environmental Control


Based on a legacy program that encompassed the development of advanced mercury and NOx emissions control technologies, coal utilization byproduct (CUB) research, to CO2 emissions control for existing plants and Water-Energy Interface R&D. This area made significant contributions to lowering the environmental impact of coal based power systems. Efforts are underway to analyze the future impacts of water for advanced power systems and how R&D can address these pending issues.


The Carbon Capture Simulation Initiative (CCSI) — This is a partnership among national laboratories, industry, and academic institutions that is developing and deploying state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment for advanced power generation. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models, with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset incorporates commercial and open-source software currently in use by industry and is also developing new software tools as necessary to fill technology gaps identified during execution of the project.


The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The CCSI Toolset consists of 8 product categories: basic data submodels, high resolution filtered submodels, validated high-fidelity CFD models & UQ, steady-state and dynamic process models, process optimization & UQ, integrated framework for dynamics & control, risk analysis & decision making, and crosscutting integration tools.

CCSI works closely with industry using an Industry Advisory Board looking at projects from inception of a project to identify industrial challenge problems to ensure that the simulation tools are developed for the carbon capture technologies of most relevance to industry. Portions of the CCSI Toolset were released in 2012 and 2013 and subsequent releases in 2014 and 2015. Multiple licenses of the toolsets have been issued. The use of the CCSI toolsets is being migrated to the Carbon Capture subprogram area. CTR will continue to conduct research on enhancement of the tools to improve calculation resolution and speed calculation time. The Carbon Capture program will focus on the use of the tools sets and development of specific process simulation packages.

Additional information can be found on the CCSI Project Website: https://www.acceleratecarboncapture.org/.


Institute for the Design of Advanced Energy Systems (iDAES) — The Institute for the Design of Advanced Energy Systems (IDAES) will be the world’s premier resource for the development and analysis of innovative advanced energy systems through the use of process systems engineering tools and approaches. iDAES and its capabilities will be applicable to the development of the full range of advanced fossil energy systems, including chemical looping and other transformational CO2 capture technologies, as well as integration with other new technologies such as supercritical CO2. In addition, the tools and capabilities will be applicable to renewable energy development, such as biofuels, green chemistry, Nuclear and Environmental Management, such as the design of complex, integrated waste treatment facilities. iDAES is intended to fill gaps and limitations identified related to the current computational platforms for process systems engineering. Thus, technical capabilities of iDAES, will build on groundbreaking advances previously developed under CCSI (such as FOQUS and ALAMO), and will extend well beyond the capabilities that currently exist anywhere in the world. In particular, iDAES’s computational capabilities will allow for the development of entirely new equipment, processes and approaches, thus avoiding the limitations associated with trying to fit a new material or concept into existing equipment. iDAES will develop a rigorous, computational approach to help enable the development of such new concepts for energy systems. Because of the complexity of energy systems, and the increasing recognition of the importance of understanding uncertainty, the Institute’s models will be both multi-scale and dynamic in nature while incorporating intrusive UQ techniques. Just as CCSI pushed the limits of what is possible with commercial simulation tools, iDAES will build on existing open source software tools to the extent possible and build new capabilities when required.


Plant Simulation — This effort expands upon the success of the CCSI development of models for carbon capture technologies. This effort will focus on developing process simulation models of interest and relevance to industry. These models can be for either new process technologies, or to provide greater insight into existing process technologies.


These models will be based on work using much of the CCSI Toolset with subroutines developed as needed for each individual process. A critical aspect of this effort is using validated data from experimental bench scale and field tests to improve the accuracy of the simulation results. Facilities such as NETL’s HYPER test unit, the National Carbon Capture Center (NCCC), National Labs, and FOA participants is a key part of this effort.


National Risk Assessment Partnership (NRAP) — This program is a part of an international efforts to develop the risk assessment tools needed for safe, permanent geologic CO2 storage. NRAP members include five national DOE laboratories that have been conducting collaborative research for the Office of Fossil Energy’s Carbon Sequestration Program for many years: NETL, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and the Pacific Northwest National Laboratory. The NRAP initiative receives input from industry, government, non-government organizations, and academia regarding research needs for large-scale CO2 storage deployment. The primary objective of NRAP is to develop a defensible, science-based methodology and platform for quantifying risk profiles at most types of CO2 storage sites to guide decision making and risk management. NRAP also develops monitoring and mitigation protocols to reduce uncertainty in the predicted long-term behavior of a site.


Additional information can be found that their website at https://edx.netl.doe.gov/nrap/.


Fundamental Model Development — This effort focuses on continuously improving the computational capability of the models developed and used for CCCMRP efforts. The continual improvement in the mathematical capability and the improvement of computational platforms (computing hardware) have reduced time to solution for numerous models that have been developed over the years. To keep the simulation and modeling platforms relevant, implementation of new computation techniques along with incorporation of validation data from active research projects is used. This ensures that the simulation and modeling packages remain up-to-date for the user community.

Multiphase Uncertainty Quantification Toolset — This research enhances the predictive ability of the computational tools used to model multiphase flow, while preserving fidelity to predict actual behavior in full-scale systems, which is essential. Determining the degree of fidelity that can be attained when using fast-running ROMS can greatly enhance the value of these computational tools. Advances in computer hardware allow acceleration of the run time for models such as those included in the MFiX family of models. Developing an understanding of the accuracy possible at each level of complexity can lead to creation of a hierarchical set of modeling tools that allow for flexible computational strategies when starting to design novel components, including multiphase reactors such as gasifiers, chemical looping combustors, and oxy-fired systems.

Development of Increased Fidelity ROMs — This research uses reacting multiphase CFD models to develop a high-fidelity basis for creation of the ROMs. Current weaknesses in existing proper orthogonal decomposition-based models will be addressed and/or new theories will be developed to develop fast-running ROMs that are based on high-fidelity reacting multiphase CFD models.

Graphics Processing Units (GPUs) and Other Acceleration Techniques — This research focuses on accelerating the family of models that serve as the core capability within this effort. Advances in computer hardware, using general purpose GPUs for the acceleration of MFiX-DEM and MFiX-PIC models, may lead to methodologies for performing similar acceleration on other NETL applications, such as the MFiX continuum model. High-end computing architectures are going through a transition from few cores on a chip to hundreds of cores or coprocessors. However, the efficient use of many-core architectures is dependent on the communication patterns and memory footprint of the algorithms used in MFiX. At present, MFiX is parallelized using MPI for multiprocessor calculations and with Open MP for in-processor calculations over multiple cores. Efficient Open MP implementation will provide a good starting point for efficient utilization of coprocessing architectures.

Multiscale Modeling — A conceptual framework for integrating models at different scales is shown in the figure below. The models at different scales are bridged by using ROMs that capture the predictive behavior of the lower scale model while allowing the integrated formulation to be computationally tractable. The ROM included at each scale is a reduced representation of the model at the scale below, ranging from a set of parameters (e.g., elementary rate constants) to models derived using sophisticated model reduction techniques such as proper orthogonal decomposition. The goal of multiscale ROMs is that they allow feasible realizations of complex domain models and capture accurate complex model behavior over a wide range of the decision space.

Hierarchy of Models that Exchange Information with the Help of ROMs

This approach allows evaluation of new hardware concepts and virtual exploration of systems such as gasifiers, chemical looping reactors, pressurized oxy-combustion units, and CO2 capture vessels using a hierarchical, integrated framework of models coupled to uncertainty quantification methodologies.


Custom Atomistic Design Modeling for Alloy Properties

The overall objective of the Computational Materials Modeling Research Focus Area is to develop and demonstrate a predictive computational framework that will accelerate the design, development, and optimization of efficient, cost-effective functional materials. This includes developing an integrated computational framework, ranging from first-principle atomistic modeling through the mesoscale modeling capable of predicting thermodynamic and kinetic properties of complex materials.

The use of Computational Materials Modeling is a critical research focus area within the High-Performance Materials key technology area. The use computational modeling provides a method for development of new materials and demonstrate a predictive materials behavior using a computational framework. This modeling and computational framework will accelerate the design, development, and optimization of efficient, cost-effective structural and functional materials for Transformational power-generation cycles. To accomplish this objective, a variety of projects are tackling different aspects of materials modeling. The varied work areas range from first-principle atomistic modeling through the mesoscale modeling to predict thermodynamic and kinetic properties of complex materials. Once new materials are formulated and tested in the lab, validation data is used to develop long term predictive models that predict the time to failure from yield stress and other factors under operating conditions.