grinding mill system identification and control system design in comminution using artificial neural networks
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Date
2004-04
Authors
Rama Putra, Teuku Andika
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Abstract
Grinding processes are generally complex systems exhibiting strong nonlinearities and
time delays, and hence are very demanding in control requirements. For economical
and final product quality reasons, the size reduction operating conditions must be
controlled in order to obtain a stabilized particle size distribution profile. This research
reports on the control of grinding mill process using artificial neural networks. There
are typically two steps involved when using neural networks for control i.e. the
development of neural networks grinding mill plant model(neural networks based
system identification) and the design of neurocontroller (neural networks based control - system design). System identification with multilayer perceptron and backpropagation
using Levenberg-Marquardt method has been chosen to build the neural networks plant
model as well as the use of neural networks as a generic model structure for the
identification of nonlinear dynamic systems. The neural networks plant model uses
process variable inputs and previous plant output to predict future values of the plant
output. This model uses data collected from the operation of the actual grinding plant
and the data collected from the simulator. The neural network is trained with
backpropagation using Levenberg-Marquardt method. Some of the nonlinear system
identification functions were used to identify the neural networks model. It was found
that the size distribution obtained from the simulation of neural networks grinding mill
model using the new input data (control input) from neurocontroller simulation
approaches the desired size distribution.
Description
Keywords
System identification , System design in comminution , Artificial neural networks