Comparative Study: FPA Based Response Surface Methodology & ANOVA for the Parameter Optimization in Process Control

Comparative Study: FPA Based Response Surface Methodology & ANOVA for the Parameter Optimization in Process Control

Pijush Dutta* Sudip Mandal Asok Kumar

Dept. of Electronic & Communication Engineering, Global Institute of Management & Technology, Nadia, West Bengal, India

Dept. of Electronic & Communication Engineering, MCKV Institute of Engineering, Liluha, Howrah, India

Corresponding Author Email: 
pijushdutta009@gmail.com
Page: 
23-27
|
DOI: 
https://doi.org/10.18280/ama_c.730104
Received: 
8 March 2018
|
Accepted: 
10 April 2018
|
Published: 
31 March 2018
| Citation

OPEN ACCESS

Abstract: 

Optimization plays a key role in a process control industry to optimize and prediction of the system’s performance. Most of the process control are multi-variable and to control the parameters to optimized the system performance through the classical method is inflexible, unreliable and time-consuming. Thus, an alternative method will be more effective for parameter optimization & prediction. In this research investigates parameters affecting the liquid flow for the various studied. Design of Experiments based on metaheuristic algorithm is conducted for the analysis of influencing factors. Response surface methodology (RSM) & ANOVA are widely used as a mathematical and statistical tool for system performance optimization. RSM can be employed to optimize and analyze the effects of several independent factors on a treatment process to obtain the maximum output. This paper is to present a comprehensive review on the usability & effectiveness of RSM & ANOVA based on flower pollination algorithm for process parameters modelling and optimization of liquid flow processes. From the appraisal it indicates that the FPA based RSM is gives the more predicted output than the FPA based ANOVA is approximately 9.0389e-6.

Keywords: 

liquid flow process,experimental design & analysis, optimization,process parameter, RSM

1. Introduction
2. Result Analysis
3. Conclusion
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