Modelling and Control of a Pilotplant Rotary Drier Used for Drying of Industrial Ore Concentrates

Yliniemi, Leena; Jutila, E.; Uronen, P.

doi:10.1016/s1474-6670(17)63893-x

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evaporation condensation theory assumes that the flow of moisture within a solid takes place entirely in gaseous phases. Many comprehensive surveys of drying theories exist in the literature, as reviewed by Yliniemi et al (1981).…”

Section: Basics Of Dryingmentioning

confidence: 99%

Advanced Control of a Rotary Dryer

Yliniemi¹,

Koskinen²,

Leiviskä³

1998

IFAC Proceedings Volumes

View full text Add to dashboard Cite

Real knowledge is based on experience.(Chinese saying) AbstractDrying, especially rotary drying, is without doubt one of the oldest and most common unit operations in the process industries. Rotary dryers are workhorses which are easy and reliable to operate, but neither energy-efficient nor environmentally friendly. In order to conform better to the requirements of modern society concerning working conditions, safety practices and environmental aspects, the development of control systems can provide opportunities for improving dryer operation and efficiency. Our in depth understanding of rotary drying is poor, because it is a very complex process that includes the movement of solids in addition to thermal drying. Thus even today rotary dryers are controlled partly manually, based on the operator's "eye" and experience, and partly relying on conventional control methods. The control of a rotary dryer is difficult due to the long time delay, which means that accidental variations in the input variables can disturb the process for long periods of time before they are reflected in the output variables. To eliminate such disturbances at an early stage, increasing interest has been shown in more sophisticated control systems such as model-based constructs, fuzzy logic and neural nets in recent years. Although it has proved difficult and time-consuming to develop model-based control systems, due to the complexity of the process, intelligent control methods based on fuzzy logic and neural nets offer attractive solutions for improving dryer control. These methods make it possible to utilise experience, knowledge and historical data, large amounts of which are readily available.The aim of this research was to improve dryer control by developing new hybrid control systems, one consisting of a fuzzy logic controller (FLC) and PI controller and the other of a three-layer neural network (NN) and PI controller. The FLC and NN act as supervisory controllers giving set points for the PI controllers. The performance of each was examined both with simulations and in pilot plant experiments. The pilot plant dryer at the University of Oulu closely resembles a real industrial situation, so that the results are relevant. Evaluation of these results showed that the intelligent hybrid controllers are well suited for the control of a rotary dryer, giving a performance in which disturbances can be eliminated rapidly and operation of the dryer can thereby be improved, with the aim of enhancing its efficiency and environmental friendliness.

show abstract

Section: Basics Of Dryingmentioning

confidence: 99%

Advanced Control of a Rotary Dryer

Yliniemi¹,

Koskinen²,

Leiviskä³

1998

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

Modeling and simulation of the drying of thin sheets in a continuous infrared dryer

1994

View full text Add to dashboard Cite

The model gives the temperature and moisture distributions of the air, and of the moist sheet, as a function of time and distance in the dryer. The influence of the sheet's velocity and that of the radiant energy on the dryer performance as well as the effect of the moisture content of the entering sheet have been studied. A set of 27 experiments was carried out using the infrared dryer in order to calibrate the model. In these, the following three variables each had three operational levels: web velocity, initial web moisture and heating power. The model predictions agreed very well with the experimental data. Model predictions using arithmetic averages for the parameters, and parameters correlated with operational variables, are also presented and discussed. ~~~ ~Le modkle permet le calcul de la distribution de la temperature et de I'humidite de I'air ainsi que de la nappe en fonction du temps et de la position dans le sechoir. L'influence de la vitesse de defilement de la nappe, de I'energie radiante ainsi que de I'effet de I'humidite de la nappe a I'entrke sur le skchage a Cte etudik. Un ensemble de 27 exfiriences fut realise avec le stchoir B infrarouges afin d'obtenir les donnees nkcessaires a la calibration du modkle. Chacune des trois variables choisies, soit la vitesse de defilement de la nappe. I'humidite de la nappe a l'entree ainsi que la puissance de chauffe, furent operees a trois niveaux distincts. Les predictions du modkle rksultant de I'utilisation de ces paramktres correspondent relativement bien aux donnees. Les predictions du modkle utilisant la moyenne arithmttique des parametres ainsi que celles des parametres correlCs avec les variables opirationnelles sont egalement presentees et discutees.

show abstract

Modelling and Control of a Pilotplant Rotary Drier Used for Drying of Industrial Ore Concentrates

Cited by 2 publications

References 11 publications

Advanced Control of a Rotary Dryer

Advanced Control of a Rotary Dryer

Modeling and simulation of the drying of thin sheets in a continuous infrared dryer

Contact Info

Product

Resources

About