MODELING AND CONTROL OF BASIS WEIGHT AND MOISTURE USING FUZZY LOGIC CONTROL SYSTEM

Abstract

The thesis starts with the brief introduction on the status of pulp and paper industry. The description of the paper making process and control of basis weight and moisture as interactive and non interactive system is discussed. The fuzzy logic controller, and different types of fuzzy control systems used in the work i.e. the P-Type Fuzzy controller, PD-Type Fuzzy controller and PD+I-Type Fuzzy controller are discussed. Different scaling gains used in these systems and there relationship with each other and how these gains are related to different constants of conventional PID controllers are then discussed. The second chapter puts some light on the Literature review of the process i.e. the basis weight and the moisture as an interactive system and also as a non-interactive system and Fuzzy Logic in general and tuning methods used to tune various scaling gains. The third and fourth chapters deal with the non interacting systems (SISO) relating the basis weight and moisture respectively. It also describes the effect of various scaling gains on performance parameters and gain to tune the system for a particular parameter, which scaling gain should be changed and how. In chapter five the interacting system as a whole is taken, and on the basis of the tuning methods applied in chapter three and four, the system is tuned for optimum values of scaling gains to get the desired output. Conclusions based on the work done in chapter 3, 4 and 5 are given in chapter 6. The recommendations and limitations are also mentioned. The chapter 1 starts with the status of Indian paper mills and the technologies dealing with its processes that are ranging from oldest to the most modern. It describes the paper industry operations and processes, the interactive system relating the description of the controlled variables i.e. the basis weight and the moisture, the manipulated variables i.e. the Pulp flow and the Steam flow. The Process description is given, which gives the detail of the MIMO system used in the system. The chapter also introduces a brief description of the Fuzzy logic Controller and its design parameters which includes the number of fuzzy sets for each input and output, fuzzy rule base structure, shapes and place of the membership functions by which the output ii can be monitored. After that the Fuzzy controller is made to work like a Fuzzy-P, Fuzzy-PD and Fuzzy-PD+I. Thus the description of all these types of Fuzzy controllers is given in detail, along with the relationship between the different scaling gains i.e. GE, GCE, GIE and GU. Two control loops are formed by coupling pulp flow with basis weight and steam flow with moisture and two controllers are used in the two loops. Here the pulp flow is controlled by the Basis weight valve opening and the steam flow is changed by Steam Shower Valve Opening. First the results are analyzed for SISO system i.e. a non-interacting system, and then the interacting system is analyzed. To understand the nature of interaction between the two control loops, we have studied the effects of input changes on the outputs when one loop is closed and the other is open and when both the loops are closed. The system is simulated for the above process using both FLC and Conventional PID controllers and the results are compared. Chapter 2 attempts to review the literature pertaining to the work done in the past on the basis weight and moisture control as an interactive system and as the individual systems and some economic factors related to the paper industry. A survey has been done on the FLC in general and the hybrid system combining P, PD and PD+I type of systems with Fuzzy. As the work deals with the tuning of the scaling factors, thus emphasis has been laid on the self tuning of FLC and how the variations in the scaling gains have been done. A collection of hybrid techniques where Fuzzy system is made to work as PD- Type Fuzzy and PI- Type Fuzzy Logic Control and its comparison with Conventional PID is also taken into consideration. FLC, how and where these controllers are used and implemented in the industry. Study of the Simulink environment using MATLAB, optimization using Neuro-Fuzzy and GA has also been analyzed. Chapter 3 deals with a SISO system, in which only one parameter i.e. Basis weight is taken into consideration. The variations of Basis Weight output are analyzed according to the changing values of basis weight valve opening. Major emphasis has been laid on the design parameters of Fuzzy logic controller. The effects of various scaling gains have been analyzed on the output iii of the system. The system is made to work like a Fuzzy-P, Fuzzy-PD and Fuzzy-PD+I type systems separately. It has been analyzed that the four scaling gains have different effects on the output of the system. The denormalization gain (GU) is mainly responsible for the system offset, thus the offset can be easily minimized by the proper choice of GU. The three normalization gains i.e. GE, GCE, and GIE have the affects on the oscillatory behavior, Rise time and the system stability respectively. A similar type of simulation is performed using a conventional PID controller instead of a fuzzy controller. The effects of the three constants Kp, KD and K1 on the system response are also discussed. All these tests are done both for the step input and the varying input of the basis weight set-point. A Fuzzy Logic Controller gives much better output in comparison to the conventional PID controller. The regulator problem has also been analyzed for the basis weight control using the Fuzzy control system, and it was found that the fuzzy control worked well for regulator problem also. Thus the PID controllers presently used in the industry can be replaced by the Fuzzy control systems. Chapter 4 also deals with a SISO non-interacting system, thus the variations of the moisture output with respect to the change in the Steam Shower Valve Opening are only taken into consideration. The system has been simulated using both Fuzzy and PID controller. Similar types of tests are performed to select the optimum values of the scaling gains for this system when a fuzzy controller is used. Also tests are performed for finding the optimum values of the three constants used in the PID controller for the same system. All these tests are done both for the step input and the varying input of the basis weight set-point and it was found that the Fuzzy controller can be tuned in a far better way to get good results. Chapter 5 shows a detailed description of the MIMO system and the implementation of the controllers (PD+I-Fuzzy Controller and Conventional PID) in the system. The system is simulated for the cases when one loop is closed and the other is open and vice-versa, also the results are compared and discussed when both the loops are closed. The comparisons show that as it is an interacting system, the effect of change in any one of the controlling parameters i.e. the BWVO and the SSVO have its impact on both the controlled iv variables i.e. the BW and the Moisture. The simulation is done for the step input as well as the varying inputs of set-point for both moisture and BW using both FLC and PID controllers. It has been observed that for both the cases i.e. the step input and the varying input using a PID controller, the system becomes unstable for the case when the moisture loop is closed. It means that when the BWVO is not under control, the outputs for both moisture and basis weight are also not under control. While the case is different, when the BWVO is under control and SSVO is not under control, both the outputs are under control. Thus it can be said that the major controlling factor is the BW valve opening, and by varying the value of BWVO both the parameters can be controlled. The SSVO has an insignificant effect in case of the PID controller. But this is not the case for the FLC model. For the FLC model, both the controlling parameters (BWVO and SSVO) have a significant effect on both the controlled outputs (BW and M). Moreover the performance parameters i.e. the RT, delay and overshoot were also calculated for these controllers for step input as well as the varying input and it was observed that the in case of the PID controllers the rise-time and the delay is more, also the overshoot was introduced for the varying input. Chapter 6 give the conclusions based on the work done in chapter 3, 4 and 5 and how the tuning process helps in getting the desired outputs. As the paper industry requires up-gradation of process equipments, especially the paper machines, process automation and control. Thus conventional PID controllers can easily be replaced by the FLC's as Fuzzy logic controller has a better performance in comparison with the PID controller. Even further optimization of the design parameters can be done by using the Hybrid intelligent techniques such as: Neuro-Fuzzy model and Fuzzy controllers using GA.