.png){kind=icon}
返回
Semi-Infinite Optimization with Hybrid Models
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.iecr.2c00113.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
: The robust design of performance/safety-critical process systems, from a model-based perspective, remains an existing challenge. Hybrid firstprinciples data-driven models offer the potential to dramatically improve model prediction accuracy, stepping closer to the digital twin concept. Within this context, worst-case engineering design feasibility and reliability problems give rise to a class of semi-infinite program (SIP) formulations with hybrid models as coupling equality constraints. Reduced-space deterministic global optimization methods are exploited to solve this class of SIPs to ??-global optimality in finitely many iterations. This approach is demonstrated on two challenging case studies: a nitrification reactor for a wastewater treatment system to address worst-case feasibility verification of dynamical systems and a three-phase separation system plagued by numerical domain violations to demonstrate how they can be overcome using a nonsmooth SIP formulation with hybrid models and a validity constraint incorporated.
Keyword:
GLOBAL OPTIMIZATION
BILEVEL PROGRAMS
NEURAL-NETWORK
ALGORITHM
SYSTEMS
RELAXATIONS
DIVERSITY
BACTERIA
VALIDITY
DESIGN
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
机构
引用论文
Next Next steps in clean water oxygen transfer testing - A critical review of current standards.png)
WATER RESEARCH
OAAI
WATER RESEARCHIF12.4
OAAI分享收藏
分享收藏Perspectives in Multiparametric Programming and Explicit Model Predictive Control多参数编程和显式模型预测控制的观点
PREAI
分享收藏
查看更多内容