grinding mill system identification and control system design in comminution using artificial neural networks

Simple item page
dc.contributor.authorRama Putra, Teuku Andika
dc.date.accessioned2015-08-20T08:07:52Z
dc.date.available2015-08-20T08:07:52Z
dc.date.issued2004-04
dc.description.abstractGrinding 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1086
dc.language.isoenen_US
dc.subjectSystem identificationen_US
dc.subjectSystem design in comminutionen_US
dc.subjectArtificial neural networksen_US
dc.titlegrinding mill system identification and control system design in comminution using artificial neural networksen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Grinding mill system identification and control system design in communition using artificial neural networks.pdf
Size:
9.64 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tidak Diketahui Pusat Pengajian - Tesis