The choice of this investigation is to tune the proportional-integral-derivative (PID) parameters separately for controlling the moisture content in paper industry by using Particle Swarm Optimization (PSO). This paper boon a new algorithm for PID controller tuning based particle swarm optimization. PSO algorithm has recently developed as a very powerful method for real parameter optimization. This new process is proposed to combine both the algorithms to get better optimization values. The proposed algorithm tuned the PID parameters and its performance has been compared with PID algorithm. Compared to PID algorithm technique, dynamic performance requirements such as rise time settling time and peak overshoot optimal values produced by PSO. The plant model represented by the transfer function is obtained by the system identification toolbox.
In current times, paper is one of the basic materials, normally found around all part of the world. The role of a paper machine is to form the paper sheet and remove the water from the paper sheet. A paper machine is divided into three main divisions, the wire section, the press section and the drying section [
the water goes, the cellulose fibres start to adhere to one another by hydrogen bonds and form a paper web. When the paper web leaves the wire section and enters the press section, the dry solid content is around 20%. In the press section, the newly formed sheet is pushed between rotating steel rolls and water is banished into a press belt. After a few press nips, the web enters the drying section with a solid content of approximately 50%. It now encounters the dryer cylinders. The dyer cylinders are enormous hollow metal cylinders, heated internally with steam, which dry the paper as it passes through them. Lastly, the paper is wound up on a big roll and separate from the paper machine. The moisture content is now roughly about 5% - 10%. Many properties of paper such as curl, stretch, tear, strength and stiffness depend on moisture content. So far drying section is concerned, it is liable for removing less than 1% of the water volume from the original stock to the head box; this is the part of the paper machine that, by far, consumes maximum energy. In the treating of moisture, most of the variations can adversely affect the future processing units like calendaring, converting (or) packaging line or even the printing press of customer. At the time of manufacture, the content of moisture is therefore measured and monitored online and if it deviates outside of the specified limits, the product of paper is rejected. During usual operation, stable and uniform moisture content guarantees a low rejection and consequently high production rate. The current day paper machine provides around 1000 tons of paper per day. A reduction of moisture around 0.1% corresponds to 365 tons of raw material per annum. A well-tuned moisture control system will reduce the time to carry out a grade change (state transition). In practice, the moisture feedback loop is often turned off during a grade change and the process is run in open loop (feed forward). The moisture control loop is indirectly involved during a web break by the steam pressure in the steam cylinders. A very common problem is that the cylinders become exciting since there is no longer any possibility of cooling paper around them. When the paper web is put back, picking and breakage of new web easily occur. A new tuning method is developed for both PID and PI controllers based on optimization of moisture. It is compared to a few other design methods and tested on a real paper machine. The objective of the proposed work is to develop a soft computing based PID tuning methodology for optimizing the control of moisture in paper machine. It proposes the development of a tuning technique which would be best suitable for optimizing the MD control of processes operating in a single-input-single- output process control loop. The SISO topology has been selected for this study because it is the most fundamental control loop and the theory developed for this type of loop can be easily extended to more complex loop. In this approach the transfer function of moisture process was determined using system identification tool box in MATLAB which is utilized for the soft computing based tuning simulation. The PID tuning parameters are determined from the soft computing methodology. PID controller is well-suited for industrial application.
Machine Direction (MD) Moisture ControlThe quality control system (QCS) is divided in two separate dimensions, the machine direction control (MD) and the cross direction control (CD).The conventional technique is to measure the MD and CD signals by scanning the sheet with a single sensor. The sensor is mounted in a scanner platform, where it moves back and forth in the cross direction (see
the different dryer groups can then be controlled individually go obtain the desired pressure profile through the drying section, from the first group to the last one. Since the steam inside the cylinder can be regarded as saturated because of the continuous condensation at the cylinder wall, there is a direct correlation between the steam pressure and steam temperature and you could also talk about a temperature profile. For most paper grades, dryer steam pressure is increased gradually for drying capacity and run ability reasons. The modelled system is represented in the Equation (1).
During the past few decades, process control techniques in the industry have made great advancement. Many process control methods based on adaptive control, predictive control, neural control and fuzzy control have been studied. Despite many efforts, the Proportional-Integral-Derivate (PID) controller continues to be the main component in industrial control system, including embedded controllers, programmable logic controllers and distributed control systems. The reason is that it has a simple structure easily understood by the engineers and it gives robust performance in a wide range of operating conditions [
The general PID equation represents in the Equation (2)
Kp = proportional gain, Ki = integral time and Td = derivative time.
The PSO approach has superior feature, including easy implementation, stable convergence characteristic and very good computational performance efficiency [
For the PID-controlled system, there are often only one indices to represent the system performance: IAE. They are defined as follows in Equation (3)
The major objective of this work is to test the performance of the developed particle swarm optimization algorithm by tuning the PID controller. Attempt has been made to achieve globally minimal Absolute error in step response of a process which is cascaded with PID controller by tuning the Kp proportional gain, Kd differential gain and Ki integral gain values and compute the integral absolute error. In PSO tuning of PID controller, best possible value of [kp, ki and kd] are obtained which exhibits less overshoot, has a reasonable level of settling time, low rise time and zero steady state error
The paper machine is modeled with moisture content control loop by using DCS. The transfer function is obtained to validate the moisture control process with the real time data. The mathematical model of the system is to be analyzed as a closed-loop system. By selecting the process model with poles and delay, the real time data has been estimated and validated. In this paper a time domain criterion is used for evaluating the PID controller. A set of control parameters P, I, and D can yield a good step response that will results in minimization of performance criteria in the time domain. The performance criteria in the time domain includes the over shoot, rise time and setting time. To control the plant model, PID and PSO techniques are used to verify the performance of the PID controller Parameters. Among these techniques, PSO based tuning methods have proved their excellence in giving better results by improving the time domain specifications and performance indices. Figures 4-7 represents the System identification tool box, Noise spectrum, Model output and Data model. The output response of PSO and PID are shown in
In this work, optimal PID tuning is obtained by using PSO. Simulation results demonstrate the tuning method which in this paper has provided better control performance compared with the conventional ones. Approximations that are typical to classical tuning rules are not needed. Soft computing techniques are often criticized for two reasons: algorithms are computationally heavy and convergence to the optimal solution cannot be guaranteed.
Components | Technical Specifications |
---|---|
DCS System AC 450 Controller | AC 450 Controller AI-4-20 MA, AO-4-20 MA AMPL-Programming |
Quality Control Scanner | Moisture-IR Sensor Output-(4-20 MA) Honey Well make |
Control Valve | Size:6”, Pneumatic actuated Type: Air to Open |
I/P Converter | Input-4-20 MA; Output-0-6 Bar |
Dryer | 43 Cylinders, 5 Groups; Material-Milled Steel |
Steam Pressure | 3.5 Bar to 4.5 Bar |
Steam Temperature | 150C to 180C |
Day Production & Machine Speed | 350 MT & 700 M |
Tuning Method | PID Parameter | Dynamic Performance Specifications | Performance Index | |||
---|---|---|---|---|---|---|
Kp (Proportional gain) | Ki (Integral gain) | Tr (Rise time) | Ts (Settling time) | Mp (%) (Peak overshoot) | IAE (Integral Absolute error) | |
PID | 6 | 0.7 | 2.1648 | 26.8930 | 17.4868 | 2 |
PSO | 5.34542 | 6.86754 | 0.56423 | 4.3456 | 0 | 1.43545 |
PID controller tuning is a small-scale problem in which computational complexity is not an issue. It took only a couple of seconds to solve the problem. Compared to conventionally tuned system, PSO tuned system has good steady state response and performance indices.
M. Senthil Kumar,K. Mahadevan, (2016) Removal of Moisture Content in Paper Machine Using Soft Computing Techniques. Circuits and Systems,07,2542-2550. doi: 10.4236/cs.2016.79220