Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
C Ning, F You - Computers & Chemical Engineering, 2019 - Elsevier
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …
modern data lens, highlights key research challenges and promise of data-driven …
[HTML][HTML] A review on superstructure optimization approaches in process system engineering
In this paper, we survey the main superstructure-based approaches in process system
engineering, with a particular emphasis on the existing literature for automated …
engineering, with a particular emphasis on the existing literature for automated …
Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective
Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize
fossil-based power and industrial sectors and is a bridging technology for a sustainable …
fossil-based power and industrial sectors and is a bridging technology for a sustainable …
A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage
Intermittent solar and wind availabilities pose design and operational challenges for
renewable power systems because they are asynchronous with consumer demand. To align …
renewable power systems because they are asynchronous with consumer demand. To align …
A multi-scale energy systems engineering approach towards integrated multi-product network optimization
Abstract 21 st century energy production, conversion, and delivery systems need to go
through a transition to be less carbon-intensive while meeting an increasing energy …
through a transition to be less carbon-intensive while meeting an increasing energy …
Learning and optimization under epistemic uncertainty with Bayesian hybrid models
Abstract Hybrid (ie, grey-box) models are a powerful and flexible paradigm for predictive
science and engineering. Grey-box models use data-driven constructs to incorporate …
science and engineering. Grey-box models use data-driven constructs to incorporate …
A multiscale energy systems engineering approach for renewable power generation and storage optimization
Successful integration of intermittent renewable resources into the energy mix is
instrumental to meet the growing global energy demand while reducing the carbon …
instrumental to meet the growing global energy demand while reducing the carbon …
Design and analysis of concentrating solar power plants with fixed-bed reactors for thermochemical energy storage
X Peng, M Yao, TW Root, CT Maravelias - Applied Energy, 2020 - Elsevier
Concentrating solar power (CSP) integrated with thermochemical energy storage (TCES)
has the potential to deliver cost-effective and dispatchable renewable power. In this work, a …
has the potential to deliver cost-effective and dispatchable renewable power. In this work, a …
Multi-scenario data-driven robust optimisation for industrial steam power systems under uncertainty
Y Han, J Zheng, X Luo, Y Qian, S Yang - Energy, 2023 - Elsevier
In actual industrial production, the deterministic optimisation of the steam power system
cannot meet most production scenarios due to the influence of uncertain factors such as …
cannot meet most production scenarios due to the influence of uncertain factors such as …
Solid-gas thermochemical energy storage strategies for concentrating solar power: Optimization and system analysis
A system-level analysis is presented for concentrating solar power systems employing
various solid-gas thermochemical energy storage strategies, that is, different combinations …
various solid-gas thermochemical energy storage strategies, that is, different combinations …