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Recent trends on hybrid modeling for Industry 4.0
The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …
control, diagnosis, optimization, and design, especially since the third industrial revolution …
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 …
Bayesian reaction optimization as a tool for chemical synthesis
Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of
industrial processes to selecting conditions for the preparation of medicinal candidates …
industrial processes to selecting conditions for the preparation of medicinal candidates …
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
Overview of surrogate modeling in chemical process engineering
K McBride, K Sundmacher - Chemie Ingenieur Technik, 2019 - Wiley Online Library
The ability to accurately model and simulate chemical processes has been paramount to the
growing success and efficiency in process design and operation. These improvements …
growing success and efficiency in process design and operation. These improvements …
A hybrid science‐guided machine learning approach for modeling chemical processes: A review
This study presents a broad perspective of hybrid process modeling combining the scientific
knowledge and data analytics in bioprocessing and chemical engineering with a science …
knowledge and data analytics in bioprocessing and chemical engineering with a science …
Machine learning-based optimization of a multi-step ion exchange chromatography for ternary protein separation
Ion-exchange chromatography is an essential but complicated step in the biopharmaceutical
downstream process, with multiple factors affecting the separation efficiency. Model-based …
downstream process, with multiple factors affecting the separation efficiency. Model-based …
Evolution of concepts and models for quantifying resiliency and flexibility of chemical processes
This paper provides a historical perspective and an overview of the pioneering work that
Manfred Morari developed in the area of resiliency for chemical processes. Motivated by …
Manfred Morari developed in the area of resiliency for chemical processes. Motivated by …
Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis
Supply chain operations and scheduling are well-studied problems in the literature.
Although these problems are related, they are often solved sequentially. This uncoordinated …
Although these problems are related, they are often solved sequentially. This uncoordinated …
Obey validity limits of data-driven models through topological data analysis and one-class classification
Data-driven models are becoming increasingly popular in engineering, on their own or in
combination with mechanistic models. Commonly, the trained models are subsequently …
combination with mechanistic models. Commonly, the trained models are subsequently …