.png){kind=icon}
.png){kind=icon}
Return
Fast Algorithms for Equation-Oriented Flowsheet Simulation and Optimization Using Pseudo-Transient Models
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.iecr.8b01461.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The significant challenge for steady-state process optimization using rigorous models is the intractable convergence of complex flowsheet in equation-oriented (EO) modeling environments, which could be resolved to a large extent through using the time-relaxation algorithm based on pseudo-transient continuation (PTC) models. However, such approach often leads to computational inefficiency due to large computational burden from dynamic simulation. In this paper, we develop a fast-tailed algorithm using PTC models, which combines dynamic and steady-state simulation instead of using dynamic simulation alone. To further improve the efficiency of dynamic simulation, a tolerances-relaxation integration strategy is proposed. Several examples from the literature are used to illustrate the capability of the proposed algorithm. The computational results demonstrate that the proposed algorithm is faster than the time-relaxation algorithm. In addition, the proposed algorithm is much easier to be implemented in various EO environments as it is compatible to the inherent optimization solvers in these environments.
Keywords:
COMPLEX DISTILLATION-COLUMNS
DIVIDING WALL COLUMN
DISJUNCTIVE PROGRAMMING-MODELS
OPTIMAL-DESIGN
AZEOTROPIC MIXTURES
MICROCHANNEL REACTORS
SURROGATE MODELS
CONFIGURATIONS
SEPARATION
FRAMEWORK
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W