A Multiple Objective Optimization Technique for Model Predictive Control in Slot Die Coating Process

2021 ◽  
Vol 47 (4) ◽  
pp. 379-387
Author(s):  
Yong Min Kim ◽  
Kyu Jong Lee ◽  
Min Sik Chu ◽  
Jihoon Kang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Technique ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Coating Process ◽  
Slot Die Coating ◽  
Slot Die

Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms

1999 ◽  
Author(s):  
Shapour Azar ◽  
Brian J. Reynolds ◽  
Sanjay Narayanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Set ◽  
Design Solution ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Continuous Variables ◽  
Optimal Set

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

Study on the Optimized Condition of the Slot-Die Coating Process

2015 ◽  
Vol 39 (9) ◽  
pp. 937-945
Author(s):  
Eung Soo Seo ◽  
Jungwoo Ye ◽  
Joong Kook Hwang ◽  
Jaesool Shim ◽  
Young Suck Chai
Keyword(s):  
Coating Process ◽  
Slot Die Coating ◽  
Slot Die

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

2017 ◽  
Vol 7 (3) ◽  
pp. 1538 ◽  
Author(s):  
Zakariah Yusuf ◽  
Norhaliza Abdul Wahab ◽  
Abdallah Abusam
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Membrane Fouling ◽  
Pid Controller ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Permeate Flux ◽  
Hybrid Particle Swarm Optimization

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.

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