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Nonlinear model predictive control: current status and future directions
Linear model predictive control (LMPC) is well established as the industry standard for
controlling constrained multivariable processes. A major limitation of LMPC is that plant …
controlling constrained multivariable processes. A major limitation of LMPC is that plant …
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 …
Domain adaptation transfer learning soft sensor for product quality prediction
For multi-grade chemical processes, often, limited labeled data are available, resulting in an
insufficient construction of reliable soft sensors for several modes. Additionally, the current …
insufficient construction of reliable soft sensors for several modes. Additionally, the current …
Review of the applications of neural networks in chemical process control—simulation and online implementation
As a result of good modeling capabilities, neural networks have been used extensively for a
number of chemical engineering applications such as sensor data analysis, fault detection …
number of chemical engineering applications such as sensor data analysis, fault detection …
Neural networks for control
MT Hagan, HB Demuth - Proceedings of the 1999 American …, 1999 - ieeexplore.ieee.org
Provides a quick overview of neural networks and explains how they can be used in control
systems. We introduce the multilayer perceptron neural network and describe how it can be …
systems. We introduce the multilayer perceptron neural network and describe how it can be …
Applying neural networks to on-line updated PID controllers for nonlinear process control
The inherent time-varying nonlinearity and complexity usually exist in chemical processes.
The design of control structure should be properly adjusted based on the current state. In …
The design of control structure should be properly adjusted based on the current state. In …
An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network
This paper presents a hybrid neural network, called the self-organising fuzzy neural network
(SOFNN), to extract fuzzy rules from the training data. The first hidden layer of this network …
(SOFNN), to extract fuzzy rules from the training data. The first hidden layer of this network …
Development of adversarial transfer learning soft sensor for multigrade processes
Industrial processes with multiple operating grades have become increasingly important in
satisfying the requirements of agile manufacturing and a diversified market. However …
satisfying the requirements of agile manufacturing and a diversified market. However …
Bioprocess optimization and control: Application of hybrid modelling
An improved technique of hybrid modelling biochemical production processes is described,
composed of a set of dynamical differential equations, an artificial neural network and a …
composed of a set of dynamical differential equations, an artificial neural network and a …
Control of fermenters–a review
Fermenter control has been an active area of research and has attracted more attention in
recent years. This is due to the new developments in other related areas which can be …
recent years. This is due to the new developments in other related areas which can be …