Hybrid semi-parametric modeling in process systems engineering: Past, present and future
M Von Stosch, R Oliveira, J Peres… - Computers & Chemical …, 2014 - Elsevier
Hybrid semi-parametric models consist of model structures that combine parametric and
nonparametric submodels based on different knowledge sources. The development of a …
nonparametric submodels based on different knowledge sources. The development of a …
[HTML][HTML] Review and classification of recent observers applied in chemical process systems
Observers are computational algorithms designed to estimate unmeasured state variables
due to the lack of appropriate estimating devices or to replace high-priced sensors in a plant …
due to the lack of appropriate estimating devices or to replace high-priced sensors in a plant …
Big data analytics in chemical engineering
Big data analytics is the journey to turn data into insights for more informed business and
operational decisions. As the chemical engineering community is collecting more data …
operational decisions. As the chemical engineering community is collecting more data …
Artificial Intelligence techniques applied as estimator in chemical process systems–A literature survey
Abstract The versatility of Artificial Intelligence (AI) in process systems is not restricted to
modelling and control only, but also as estimators to estimate the unmeasured parameters …
modelling and control only, but also as estimators to estimate the unmeasured parameters …
Hybrid modeling in the era of smart manufacturing
Smart manufacturing (SM) is a new paradigm that allows manufacturing to enter its fourth
revolution by exploiting state-of-the art sensing, communication and computation as the …
revolution by exploiting state-of-the art sensing, communication and computation as the …
Integration of machine learning and first principles models
L Rajulapati, S Chinta, B Shyamala… - AIChE …, 2022 - Wiley Online Library
Abstract Model building and parameter estimation are traditional concepts widely used in
chemical, biological, metallurgical, and manufacturing industries. Early modeling …
chemical, biological, metallurgical, and manufacturing industries. Early modeling …
Machine learning in chemical product engineering: The state of the art and a guide for newcomers
Chemical Product Engineering (CPE) is marked by numerous challenges, such as the
complexity of the properties–structure–ingredients–process relationship of the different …
complexity of the properties–structure–ingredients–process relationship of the different …
Modelling and control of different types of polymerization processes using neural networks technique: a review
Polymerization process can be classified as a nonlinear type process since it exhibits a
dynamic behaviour throughout the process. Therefore, it is highly complicated to obtain an …
dynamic behaviour throughout the process. Therefore, it is highly complicated to obtain an …
Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks
W Wang, D Wang - Neural Computing and Applications, 2020 - Springer
Online measuring of component concentrations in sodium aluminate liquor is essential and
important to Bayer alumina production process. They are the basis of closed-loop control …
important to Bayer alumina production process. They are the basis of closed-loop control …
Neural network based modelling and control in batch reactor
The use of neural networks (NNs) in all aspects of process engineering activities, such as
modelling, design, optimization and control has considerably increased in recent years …
modelling, design, optimization and control has considerably increased in recent years …