Building an appropriate mathematical model that describes the system behaviour with a
certain degree of satisfaction is quite challenging owing to the uncertain and volatile nature …

Recently, artificial neural networks have been widely applied in process engineering, where
the back-propagation neural networks are most frequently used, while the recurrent neural …

In this paper, a new radial basis function network-based model predictive control (RBFN-
MPC) is presented to control the steam temperature of a power plant boiler. For the first time …

This study presents a model predictive control of a 4× 3 Multiple-Input Multiple-Output
(MIMO) biomass control system that uses radial basis function (RBF) as an activation …

A radial basis function neural network (RBFNN), with a strong function approximation ability,
was proven to be an effective tool for nonlinear process modeling. However, in many …

Many systems in reality exhibit nonlinear characteristics and in most cases they cannot be
treated satisfactorily using linearized approaches over the full operating range. In this paper …

A constrained multivariable control strategy along with its application in more efficient
thermal power plant control is presented in this paper. A neural network model-based …

Thermal power generation plays a major role in meeting present-day power demands.
Efficient and economic controls of crucial elements like steam boiler section need superior …

This thesis provides a bridge between analytical modeling and neural network modeling.
Two different approaches have been explored. Both approaches rely on embedding radial …

In this paper based on the fundamental laws of physics, thermodynamics principles and
energy semi-empirical laws for heat transfer, the mathematical models are developed and …