Modeling and Techno-Economic Optimization of a Tetraamine-Appended Metal–Organic Framework for NGCC-Based CO 2 Capture Using Fixed Bed Contactors
Not Available
20 FOSSIL-FUELED POWER PLANTS↗
Engineering topics
Zamarripa-Perez, Miguel
Publications and source records attributed to Zamarripa-Perez, Miguel.
Mathematical Optimization of NGCC Solvent-based Carbon Capture Processes to Explore High Capture Designs
For point-source capture, much of the work to date has focused on achieving 90% capture – a somewhat arbitrary target above which parasitic losses to drive carbon capture and compression systems were believed to be too great to be economically justifiable. The recent push for deep decarbonization has caused this target to be revisited, and existing literature is of limited usefulness as most published studies have focused on carbon capture percentages of 90%. In this work, numerous mathematical optimizations were performed between carbon capture targets between 90%-99.8% of a Natural Gas Combined Cycle (NGCC) plant to better understand incremental cost of these systems so they may be compared to other decarbonization technologies, and to inform technical risk associated with targeting higher capture performance for these types of systems.
carbon capture↗
Optimal Design Approaches for Rapid, Cost-Effective Manufacturing and Deployment of Chemical Processes.
Abstract not provided.
Stinchfield, Georgia↗
Pareto v1.0.0
An Optimization Framework for Produced Water Management and Beneficial Reuse
Beattie, KeithS↗
Machine Learning Tools Set for Natural Gas Fuel Cell System Design
This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.
Wang, Dewei↗