[HTML][HTML] Machine learning for biochemical engineering: A review
The field of machine learning is comprised of techniques, which have proven powerful
approaches to knowledge discovery and construction of 'digital twins' in the highly …
approaches to knowledge discovery and construction of 'digital twins' in the highly …
Machine learning in bioprocess development: from promise to practice
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess
development provides large amounts of heterogeneous experimental data, containing …
development provides large amounts of heterogeneous experimental data, containing …
Applications of machine learning algorithms for biological wastewater treatment: updates and perspectives
B Sundui, OA Ramirez Calderon… - Clean Technologies and …, 2021 - Springer
Biological wastewater treatment using algae–bacteria consortia for nutrient uptake and
resource recovery is a 'paradigm shift'from the mainstream wastewater treatment process to …
resource recovery is a 'paradigm shift'from the mainstream wastewater treatment process to …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
Reinforcement learning for batch bioprocess optimization
Bioprocesses have received a lot of attention to produce clean and sustainable alternatives
to fossil-based materials. However, they are generally difficult to optimize due to their …
to fossil-based materials. However, they are generally difficult to optimize due to their …
A survey on uncertainty quantification methods for deep neural networks: An uncertainty source perspective
Deep neural networks (DNNs) have achieved tremendous success in making accurate
predictions for computer vision, natural language processing, as well as science and …
predictions for computer vision, natural language processing, as well as science and …
Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization
D Zhang, EA Del Rio‐Chanona… - Biotechnology and …, 2019 - Wiley Online Library
Abstract Model‐based online optimization has not been widely applied to bioprocesses due
to the challenges of modeling complex biological behaviors, low‐quality industrial …
to the challenges of modeling complex biological behaviors, low‐quality industrial …
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
Y Amar, AM Schweidtmann, P Deutsch, L Cao… - Chemical …, 2019 - pubs.rsc.org
Rational solvent selection remains a significant challenge in process development. Here we
describe a hybrid mechanistic-machine learning approach, geared towards automated …
describe a hybrid mechanistic-machine learning approach, geared towards automated …
[HTML][HTML] Stochastic data-driven model predictive control using gaussian processes
Nonlinear model predictive control (NMPC) is one of the few control methods that can
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …
Machine learning for algal biofuels: a critical review and perspective for the future
In this work, machine learning (ML) applications in microalgal biofuel production are
reviewed. First, the basic steps of algal biofuel production are summarized followed by a …
reviewed. First, the basic steps of algal biofuel production are summarized followed by a …