Optimization of PID Controller Parameters on Flow Rate Control System Using Multiple Effect Evaporator Particle Swarm Optimization

This study aims to improve the performance of Proportional+Integral+Differential (PID) control to reduce the speed of the steam flow rate and search optimal points in the evaporation process of Multiple Effect Evaporator (MEE). Optimization of PID control tuning parameters using Particle Swarm Optimization (PSO) by adding a weighting factor of inertia is expected to handle nonlinear systems with evaporator undershoot response characteristics that are difficult to treat and improve response of the system with large overshoot, and long rise time. The results showed PSO is able to provide improved performance tuning PID control system to improve system response in MEE control system with a rise time of 0.01 seconds, 3.35% overshoot, settling time of 6.10 seconds and able respond plant undershoot MEE of 28.11%.The results show that, PSO with inertia weight w provides additional tuning better than PID control by ZN method of max criteria over shoot, rise time and settling time of 5.38%, 3.05 seconds, 10.1 seconds, compared to tuning PID control  method with PSO (3.35%, 0.01 seconds, 6.10 seconds).

[1]  Vivek Kumar,et al.  Dynamic response of split feed multi-effect evaporators using Mathematical Modelling and Simulation , 2012 .

[2]  Yusuf Hendrawan,et al.  Neural-Genetic Algorithm as Feature Selection Technique for Determining Sunagoke Moss Water Content , 2010 .

[3]  Yih-Lon Lin,et al.  A particle swarm optimization approach to nonlinear rational filter modeling , 2008, Expert Syst. Appl..

[4]  Yusuf Hendrawan,et al.  Neural-Discrete Hungry Roach Infestation Optimization to Select Informative Textural Features for Determining Water Content of Cultured Sunagoke Moss , 2011 .

[5]  Yusuf Hendrawan,et al.  Precision irrigation for Sunagoke moss production using intelligent image analysis. , 2009 .

[6]  Yusuf Hendrawan,et al.  Non-destructive sensing for determining Sunagoke moss water content -bio-inspired approaches- , 2011 .

[7]  Toshiharu Sugie,et al.  Robust PID controller tuning based on the constrained particle swarm optimization , 2008, Autom..

[8]  Slamet Wahyudi,et al.  Numerical Methods for Predicting Melting through the Gap Applied to the One-Dimensional Stefan Problem Using Taylor Series , 2013 .

[9]  Boumediène Allaoua,et al.  Setting Up PID DC Motor Speed Control Alteration Parameters Using Particle Swarm Optimization Strategy , 2009 .

[10]  Sawsan Morkos Gharghory,et al.  Optimal Tuning of PID Controller using Adaptive Hybrid Particle Swarm Optimization Algorithm , 2012, Int. J. Comput. Commun. Control.

[11]  Zwe-Lee Gaing,et al.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004 .

[12]  Abolfazl Jalilvand,et al.  OPTIMAL TUNING OF PID CONTROLLER PARAMETERS ON A DC MOTOR BASED ON ADVANCED PARTICLE SWARM OPTIMIZATION ALGORITHM , 2011 .

[13]  Yusuf Hendrawan,et al.  Determining an ANN pre-treatment algorithm to predict water content of moss using RGB intensities , 2011 .

[14]  Zwe-Lee Gaing.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[15]  A. M. El-Zonkoly,et al.  Optimal tuning of power systems stabilizers and AVR gains using particle swarm optimization , 2006, Expert Syst. Appl..

[16]  Nelendran Pillay,et al.  A particle swarm optimization approach for tuning of SISO PID control loops , 2008 .

[17]  Hans Zwart,et al.  State Space Representation , 2012 .

[18]  Yusuf Hendrawan,et al.  Neural-Intelligent Water Drops algorithm to select relevant textural features for developing precision irrigation system using machine vision , 2011 .

[19]  C. Geankoplis.  Transport processes and unit operations , 1978 .

[20]  M Karimi,et al.  NONLINEAR MODELING AND CASCADE CONTROL DESIGN FOR MULTI EFFECT FALLING FILM EVAPORATORS , 2006 .

[21]  Yusuf Hendrawan,et al.  Bio-inspired feature selection to select informative image features for determining water content of cultured Sunagoke moss , 2011, Expert Syst. Appl..

[22]  Stephen A. Billings,et al.  International Journal of Control , 2004 .