- FOQUS Installation and Running
- Flowsheets and Settings
- Uncertainty Quantification (UQ)
- Optimization Under Uncertainty (OUU)
- Surrogate Modeling
- Sequential Design of Experiments (SDOE)
- Heat Integration
- Simulation Standard Interface (SimSinter)
- Surrogate Model Based Optimizer
- Developer Documentation
- Copyright and License
The Framework for Optimization, Quantification of Uncertainty, and Surrogates (FOQUS) serves as the primary computational platform enabling advanced Process Systems Engineering (PSE) capabilities to be integrated with commercial process simulation software. It can be used to synthesize, design, and optimize a complete carbon capture system while considering uncertainty. FOQUS enables users to effectively screen potential capture concepts in the context of a complete industrial process so that trade-offs can be appropriately evaluated. The technical and economic performance characteristics of the capture process are highly dependent on employing an effective approach for process synthesis. Since large-scale carbon capture processes are outside of current experience, heuristic and evolutionary approaches are likely to be inadequate. Thus, a key aspect of FOQUS is that it bridges this gap by supporting a superstructure-based approach to determine the optimal process configuration and equipment interconnections.
- SimSinter provides a wrapper to enable models created in process simulators to be linked into a FOQUS Flowsheet.
- The FOQUS Flowsheet is used to link simulations together and connect model variables between simulations on the flowsheet. FOQUS enables linking models from different simulation packages.
- Simulations are run through Turbine, which manages the multiple runs needed to build surrogate models, perform derivative-free optimization or conduct an Uncertainty Quantification (UQ) analysis. Turbine provides the capability for job queuing and enables these jobs to be run in parallel using cloud- or cluster-based computing platforms or a single workstation.
- The Surrogates module can create algebraic surrogate models to support large-scale deterministic optimization, including superstructure optimization to determine process configurations. One of the available surrogate models is the Automated Learning of Algebraic Models for Optimization (ALAMO). ALAMO is an external product due to background Intellectual Property (IP) issues.
- The Derivative-Free Optimization (DFO) module enables derivative-free (or simulation-based) optimization directly on the process models linked together on a FOQUS Flowsheet. It utilizes Excel to calculate complex objective functions, such as the cost of electricity.
- The UQ module enables the effects of uncertainty to be propagated through the complete system model, sensitivity of the model to be assessed, and the most significant sources of uncertainty identified to enable prioritizing of experimental resources to obtain additional data.
- The Optimization Under Uncertainty (OUU) module combines the capabilities of the DFO and the UQ modules to enable scenario-based optimization, such as optimization over a range of operating scenarios.
- The Sequential Design of Experiments (SDOE) module currently provides a way to construct flexible space-filling designs based on a user-provided candidate set of input points. The method allows for new designs to be constructed as well as augmenting existing data to strategically select input combintions that minimizes the distance between points. Development of this module is continuing and will soon include other options for design construction.
Application Based Examples¶
FOQUS has been used to solve problems based on comprehensive analysis and optimization of carbon capture systems. Some relevant research work that includes FOQUS can be found in the following publications:
Chen, Y., Eslick, J.C., Grossmann, I.E., Miller, D.C., 2015. Simultaneous process optimization and heat integration based on rigorous process simulations. Computers and Chemical Engineering 81, 180–199.
Gao, Q., Miller, D.C., 2015. Optimization of amine-based solid sorbent chemistry for post-combustion carbon capture. Paper presented at: 2015 International Pittsburgh Coal Conference; 5–8 October 2015; Pittsburgh, PA, USA.
Ma, J., Mahapatra, P., Zitney, S.E., Biegler, L.T., Miller, D.C., 2016. D-RM Builder: A software tool for generating fast and accurate nonlinear dynamic reduced models from high-fidelity models. Computers and Chemical Engineering 94, 60–74.
Miller, D.C., Agarwal, D., Bhattacharyya, D., Boverhof, J., Chen, Y., Eslick, J., Leek, J., Ma, J., Mahapatra, P., Ng, B., Sahinidis, N.V., Tong, C., Zitney, S.E., 2017. Innovative computational tools and models for the design, optimization and control of carbon capture processes, in: Papadopoulos, A.I., Seferlis, P. (Eds.), Process Systems and Materials for CO2 Capture: Modelling, Design, Control and Integration. John Wiley & Sons Ltd, Chichester, UK, pp. 311–342.
Soepyan, F.B., Anderson-Cook, C.M., Morgan, J.C., Tong, C.H., Bhattacharyya, D., Omell, B.P., Matuszewski, M.S., Bhat, K.S., Zamarripa, M.A., Eslick, J.C., Kress, J.D., Gattiker, J.R., Russell, C.S., Ng, B., Ou, J.C., Miller, D.C., 2018. Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing. In: Eden, M.R., Ierapetritou, M.G., Towler, G.P. (Editors), 13th International Symposium on Process Systems Engineering (PSE 2018). Elsevier, Amsterdam, pp. 283-288.
Additional research work can be found on https://www.acceleratecarboncapture.org/publications