Abstract
AbstractEnergy 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.
Highlights
This paper describes the IDAES-CMF and model libraries, demonstrating its capabilities for solving complex chemical engineering flowsheets through a case study
The IDAES-CMF builds on the foundation of Pyomo,[27] an open-source, extensible algebraic modeling environment implemented in the Python programming language which provides direct access to stateof-the-art solvers, solution techniques and, through Python, access to numerous libraries for scientific computing, data analyses and visualization capabilities
The case study provides a simple demonstration of how the IDAES-CMF can be used to rapidly assemble an EO model of the process using a library of pre-built, reusable unit operation models and property packages
Summary
As a result of these developments, there is currently no single platform which combines the infrastructure and modeling libraries required to address complex energy and chemical processes with integrated support for advanced optimization-based decision-making, that is, for (i) optimal steady-state and dynamic design and operations, including superstructure based conceptual design, and (ii) data reconciliation, parameter estimation, uncertainty quantification and optimization under uncertainty. This deficiency leads to greatly increased user effort and severely limits the effectiveness of process modeling and optimization workflows, as potential users must have access to and knowledge of multiple tools to handle each task.
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