Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies …
Kinetic modeling of fermentation processes is difficult due to the use of micro-organisms that
follow complex reaction mechanisms. Kinetic models are usually not perfect owing to …
follow complex reaction mechanisms. Kinetic models are usually not perfect owing to …
Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae
Abstract β-Carotene has a positive impact on human health as a precursor of vitamin A.
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …
Deep hybrid modeling of chemical process: Application to hydraulic fracturing
Process modeling began with the use of first principles resulting in 'white-box'models which
are complex but accurately explain the dynamics of the process. Recently, there has been …
are complex but accurately explain the dynamics of the process. Recently, there has been …
Hybrid Koopman model predictive control of nonlinear systems using multiple EDMD models: An application to a batch pulp digester with feed fluctuation
In the pulping process, feed fluctuations often occur due to the supply of raw materials from
various and unconventional sources, such as recycle to meet the increasing market demand …
various and unconventional sources, such as recycle to meet the increasing market demand …
Deep hybrid model‐based predictive control with guarantees on domain of applicability
A hybrid model integrates a first‐principles model with a data‐driven model which predicts
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …
Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems
A Narasingam, JSI Kwon - AIChE Journal, 2019 - Wiley Online Library
In this work, we propose the integration of Koopman operator methodology with Lyapunov‐
based model predictive control (LMPC) for stabilization of nonlinear systems. The Koopman …
based model predictive control (LMPC) for stabilization of nonlinear systems. The Koopman …
[HTML][HTML] Data-driven modeling of multimode chemical process: Validation with a real-world distillation column
Real-world industrial processes frequently operate in different modes such as start-up,
transient, and steady-state operation. Since different operating modes are governed by …
transient, and steady-state operation. Since different operating modes are governed by …
Development of offset-free Koopman Lyapunov-based model predictive control and mathematical analysis for zero steady-state offset condition considering influence …
Koopman operator theory enables a global linear representation of a given nonlinear
dynamical system. However, since an approximation to the Koopman operator cannot fully …
dynamical system. However, since an approximation to the Koopman operator cannot fully …
Deep reinforcement learning control of hydraulic fracturing
Hydraulic fracturing is a technique to extract oil and gas from shale formations, and
obtaining a uniform proppant concentration along the fracture is key to its productivity …
obtaining a uniform proppant concentration along the fracture is key to its productivity …
Application of offset‐free Koopman‐based model predictive control to a batch pulp digester
This work presents the application of a Koopman operator approach to a batch pulp
digester. To manufacture paper products with desired properties, it is essential to consider …
digester. To manufacture paper products with desired properties, it is essential to consider …