Advanced control of a rotary dryer

Abstract Drying, 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 utilize 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

Development of a self-tuning fuzzy logic controller for a rotary dryer

Abstract It is well known that the control of a rotary dryer is difficult due to long delay times of the process. The main target of this research was to find a self-tuning technique for tuning the parameters of the fuzzy logic controller, which has been developed to the pilot plant rotary dryer located in the Control Engineering Laboratory at the University of Oulu. The aim is to improve the performance of the FLC by making the responses more robust to the input disturbances of the process, mainly to the input moisture of solids. First, the literature review is made in order to see the current situation of the self-tuning FLC's, and then one self-tuning technique presented in the literature is selected and applied to the tuning of the hybrid PI-FLC and of the pure FLC used in the control of the rotary dryer. The resulting control behaviour has been examined with simulations and the comparison with the results achieved with theFLC's without self-tuning is made

Development of a fuzzy logic controller for a rotary dryer with self-tuning of scaling factor

Abstract The control of the drying process in a rotary dryer is difficult due to delay time and the very complex process that includes the movement of the solids in addition to thermal drying. Thus, even today rotary dryers are partly manually controlled based on the operator's experience and partly automatically controlled relying on conventional control methods. In this paper, which applies advanced control to a rotary dryer, two different control systems are developed. The first is a self-tuning PID-type pure fuzzy feedback and the second is a self-tuning PID-type hybrid feedback and feedforward. The performances are compared with a traditional PID controller and both control systems give much better results then a traditional PID controller

Fuzzy modelling for a rotary dryer

Abstract In this research a fuzzy model is developed for a rotary dryer. It is applied to the pilot plant rotary dryer located in the Control Engineering Laboratory at Oulu University. Firstly, a literature review looking at the current situation of fuzzy modelling and comparison of different methods is done. One modelling method is then applied to the building of the model from data. The rule parameters are determined on the basis of clusters created by Kohonen learning rule method and the initial model is optimised by the trial and error method. The resulting model behaviour is examined with simulation and, the results achieved are compared with other models

Web based monitoring and control of industrial processes

Abstract Nowadays the Internet is playing a very important role in different domains. During the previous years a lot of research has been done for trying to develop applications, which make it possible to supervise and control industrial processes using the World Wide Web. Although different experiments have proven that this technology has a great impact in the future there are still some problems. These problems concern architectures, requirement specifications, and security aspects. This report will give a brief overview about the design methods and architectures developed so far for the Internet-based process control systems and also about the problems concerning security aspects