[HTML][HTML] A review and perspective on hybrid modeling methodologies
The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …
from knowledge about the system) and nonparametric models (typically deduced from data) …
Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges
Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework,
is increasingly being adopted in bioprocess modeling, model‐based experimental design …
is increasingly being adopted in bioprocess modeling, model‐based experimental design …
Exploring the potential of time-series transformers for process modeling and control in chemical systems: an inevitable paradigm shift?
The last two years have seen groundbreaking advances in natural language processing
(NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution …
(NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution …
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers
For prediction and real-time control tasks, machine-learning (ML)-based digital twins are
frequently employed. However, while these models are typically accurate, they are custom …
frequently employed. However, while these models are typically accurate, they are custom …
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 …
Introducing hybrid modeling with time-series-transformers: A comparative study of series and parallel approach in batch crystallization
Given the hesitance surrounding the direct implementation of black-box tools due to safety
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …
[HTML][HTML] Physics-informed machine learning for MPC: Application to a batch crystallization process
This work presents a framework for develo** physics-informed recurrent neural network
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
Achieving optimal paper properties: A layered multiscale kMC and LSTM-ANN-based control approach for kraft pul**
The growing demand for various types of paper highlights the importance of optimizing the
kraft pul** process to achieve desired paper properties. This work proposes a novel …
kraft pul** process to achieve desired paper properties. This work proposes a novel …
Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty
In this work, we present a physics-informed recurrent neural network (PIRNN)-based
modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics …
modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics …
Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical …
Supercritical water gasification (SCWG) technology can convert biomass into hydrogen rich
gas and biochar. Fluidized bed reactor is promising for the industrialization of this …
gas and biochar. Fluidized bed reactor is promising for the industrialization of this …