Bilevel and Parallel Programing-Based Operability Approaches for Process Intensification and Modularity

Juan Carlos Carrasco

doi:10.1002/aic.16113

Return

Bilevel and Parallel Programing-Based Operability Approaches for Process Intensification and Modularity

2018-02-22

PRE

Juan Carlos Carrasco

Fernando V. Lima *

DOI：10.1002/aic.16113

Original

Original request for help

Save

Abstract

En 中文

Process operability emerged in the last decades as a powerful tool for the design and control of chemical processes. Recent efforts in operability have been focused on the calculation of the desired input set for process design and intensification of natural gas utilization applications described by nonlinear models. However, there is still a gap in terms of problem dimensionality that nonlinear operability methods can handle. To fill this gap, in this article, the incorporation of bilevel and parallel programing approaches into classical process operability concepts is discussed. Results on the implementation of the proposed method show a reduction in computational time up to two orders of magnitude, when compared to the original results without parallelization. These results could be extrapolated for use in a supercomputer as presented in the computational time analysis performed. In terms of intensification, the proposed approach can produce a natural gas combined cycle plant modular design with a dramatic reduction in size, from the original 400 to 0.11 MW, while still keeping the high net plant efficiency. This approach thus provides a computationally efficient framework for process intensification of high-dimensional nonlinear energy systems toward modularity. The proposed approach also enables the verification of a modular design and conditions that can be obtained according to economic and physical constraints associated with a specific natural gas well production. (C) 2018 American Institute of Chemical Engineers

Keywords:

operability

bilevel optimization

parallel programing

process intensification

modularity

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.