Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges
B Mahanty - Biotechnology and Bioengineering, 2023 - Wiley Online Library
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?
N Sitapure, JSI Kwon - Chemical Engineering Research and Design, 2023 - Elsevier
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
N Sitapure, JSI Kwon - Computers & Chemical Engineering, 2023 - Elsevier
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
N Sitapure, J Sang-Il Kwon - Industrial & Engineering Chemistry …, 2023 - ACS Publications
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 …
Achieving optimal paper properties: A layered multiscale kMC and LSTM-ANN-based control approach for kraft pulping
The growing demand for various types of paper highlights the importance of optimizing the
kraft pulping process to achieve desired paper properties. This work proposes a novel …
kraft pulping process to achieve desired paper properties. This work proposes a novel …
From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives
Deep learning is emerging in many industrial sectors in hand with big data analytics to
streamline production. In the biomanufacturing sector, big data infrastructure is lagging …
streamline production. In the biomanufacturing sector, big data infrastructure is lagging …
An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries
During the multi-cycle operation of a Li-ion battery, its process dynamics evolve in two
distinct timescales: slow degradation dynamics over multiple cycles and fast cycling …
distinct timescales: slow degradation dynamics over multiple cycles and fast cycling …
Advancing biomass fractionation with real-time prediction of lignin content and MWd: A kMC-based multiscale model for optimized lignin extraction
Recently, lignin has garnered significant research attention due to its abundance in nature.
However, lignin is viewed as a recalcitrance factor as it impedes the overall biomass …
However, lignin is viewed as a recalcitrance factor as it impedes the overall biomass …
Unveiling latent chemical mechanisms: Hybrid modeling for estimating spatiotemporally varying parameters in moving boundary problems
Hybrid modeling has gained substantial recognition due to its capacity to seamlessly
integrate machine learning methodologies while preserving the fundamental physical …
integrate machine learning methodologies while preserving the fundamental physical …