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 …
Artificial neural networks: applications in chemical engineering
M Pirdashti, S Curteanu, MH Kamangar… - Reviews in Chemical …, 2013 - degruyter.com
Artificial neural networks (ANN) provide a range of powerful new techniques for solving
problems in sensor data analysis, fault detection, process identification, and control and …
problems in sensor data analysis, fault detection, process identification, and control and …
ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process
JCB Gonzaga, LAC Meleiro, C Kiang… - Computers & chemical …, 2009 - Elsevier
This paper presents the development and the industrial implementation of a virtual sensor
(soft-sensor) in the polyethylene terephthalate (PET) production process. This soft-sensor …
(soft-sensor) in the polyethylene terephthalate (PET) production process. This soft-sensor …
[图书][B] Data mining and knowledge discovery for process monitoring and control
XZ Wang - 2012 - books.google.com
Modern computer-based control systems are able to collect a large amount of information,
display it to operators and store it in databases but the interpretation of the data and the …
display it to operators and store it in databases but the interpretation of the data and the …
A batch-to-batch iterative optimal control strategy based on recurrent neural network models
Z Xiong, J Zhang - Journal of Process Control, 2005 - Elsevier
A batch-to-batch model-based iterative optimal control strategy for batch processes is
proposed. To address the difficulties in developing detailed mechanistic models, recurrent …
proposed. To address the difficulties in developing detailed mechanistic models, recurrent …
Data-driven methods for batch data analysis–A critical overview and mapping on the complexity scale
More than two decades have passed since the first holistic data-driven approaches for batch
data analysis (BDA) were published. The emphasis was on multivariate statistical process …
data analysis (BDA) were published. The emphasis was on multivariate statistical process …
Multiscale modeling and optimal operation of millifluidic synthesis of perovskite quantum dots: towards size-controlled continuous manufacturing
Inorganic lead halide perovskite quantum dots (QDs) have emerged as a promising
semiconducting nanomaterial candidate for widespread applications, including next …
semiconducting nanomaterial candidate for widespread applications, including next …
Developing robust non-linear models through bootstrap aggregated neural networks
J Zhang - Neurocomputing, 1999 - Elsevier
This paper presents a technique for building robust non-linear models by aggregating
multiple neural networks. Data for building non-linear models are re-sampled using …
multiple neural networks. Data for building non-linear models are re-sampled using …
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 …
Auto-switch Gaussian process regression-based probabilistic soft sensors for industrial multigrade processes with transitions
Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial
processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is …
processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is …