This study is focusing on leveraging the system design tools set for 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 user-friendly graphic user interface built within Jupyter Notebooks, and the whole tools set is open-source public available.

No abstract

Computer-aided process engineering and conceptual design in energy and chemical engineering has played a critical role for decades. Conventional computer-aided process and systems design generally starts with process flowsheets that have been developed through experience, which often relies heavily on subject matter expertise. These widely applied approaches require significant human effort, either providing the initially drafted flowsheet, alternative connections, or a set of well-defined heuristics. These requirements make the system design highly reliant on the engineer’s experiences and expertise. In this study, a novel reinforcement learning (RL) based automated system for conceptual design is introduced and demonstrated. The RL approach provides a generic tool for identifying process configurations and significantly decreases the dependence on human intelligence for energy and chemical systems conceptual design. An artificial intelligence agent performs the conceptual design by automatically deciding which process-units are necessary for the desired system, picking the process-units from the candidate process-units pool, connecting them together, and optimizing the operation of the system for the user-defined system performance targets. The AI agent automatically interacts with a physics-based system-level modeling and simulation toolset, the Institute for the Design of Advanced Energy System Integrated Platform, to guarantee the system design is physically consistent.

No abstract

This study is focusing on leveraging the system design tools set for 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 user-friendly graphic user interface built within Jupyter Notebooks, and the whole tools set is open-source public available.