SwePub - sökning: WFRF:(Max Hansen Mark)

Numrering	Referens	Omslagsbild	Hitta
1.	Yamanee-Nolin, Mikael, et al. (författare) Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model 2019 Ingår i: Computer Aided Chemical Engineering. - 1570-7946. - 9780128186343 ; 2019 Tidskriftsartikel (refereegranskat)abstract The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.

Yamanee-Nolin, Mikael, et al. (författare)
Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model
2019
Ingår i: Computer Aided Chemical Engineering. - 1570-7946. - 9780128186343 ; 2019
Tidskriftsartikel (refereegranskat)abstract
- The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.

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