Application of FPA and ANOVA in the optimization of liquid flow control process

Application of FPA and ANOVA in the optimization of liquid flow control process

Pijush DuttaSudip Mandal Asok Kumar 

Dept. of Electronic & Communication Engineering, Global Institute of Mangement & Technology, Nadia, West Bengal 741102, India

Dept. of Electronic & Communication Engineering, MCKV Institute of Engineering, Liluha, Howrah 711204, India

Corresponding Author Email:
6 January 2018
23 March 2018
31 March 2018
| Citation



In process industry liquid flowrate is one of the important variable which need to be controlled in a process to obtain the better quality and reduce the cost of production. As the liquid Flow rate in a process industry depends upon a number of parameter so the process will give the unexpected output as it is caused by the improper setting of parameters. The improper parameter settings could threaten the processes. In this paper, we utilize the Flower Pollination Algorithm (FPA) methods and ANOVA to obtain the optimum conditions of a flowrate in a process industry and to gain the percentage of contributions of each parameter by. A verification test was carried out to inspect the optimum output among the ANOVA & FPA. For generating the objective function 120 sets of data is used in ANOVA while 18 sets of data are used for the verification purpose.


liquid flow process, optimization, ANOVA, FPA

1. Introduction
2. Literature Review
3. Experimetal Procedure
4. Result Analysis
5. Result & Performance Analysis
6. Conclusion
Matlab Code for Test

[1] Kalogirou S. (2003). artificial intelligent for the modelling and control of combustion processes: a review. Progress in Energy and Combustion Science 29(6): 515-566.

[2] Isermann R, Muller N. (2003). Design of computer controlled combustion engines. Mechatronics 13(10): 1067-1089.

[3] Hafner M, Schuler M, Nelles O, Isermann R. (2000). Fast neural netwroks for diesel engine control design. Control Engineering Practice 8(11): 1211-1221.

[4] Ashhab MS. (2004). A combination of neural net modelling and constrained optimization towards inverse control. 4th IASTED International Conference on Modelling, Simulation and Optimization, Kauai-USA 66-71.

[5] Slanvetpan T, Barat R, Stevens J. (2003). Process control of a laboratry combustor using artificial neural networks, Comput. Chem. Eng. 27(11): 1605-1616.

[6] Noeres C, Dadhe K, Gesthuisen R, Ngell S, Gorak A. (2004). Model based design ,control and optimization of catalytic distillation process. Chemical Engineering Process 43(3): 421-434.

[7] Datta A, Hareesh M, Kalra PK, Boom R. (1994). Adaptive Neural Net (ANN) models for desulphurization of hot metal ans steel. Steel Research International 65(11): 466-471.

[8] Karniel A, Meir R, Inbar GF (2001). Best estimated inverse versus inverse of the best estimator, Neural Networks 14(9): 1153-1159.

[9] Al-salaymeh (2001). Flow velocity and Volume flow rate sensor with a wide Bandwidth. PhD Dissertation. Technischen Fakultatder universitat Erlangen-Numberg.

[10] Pijush D, Asok K. (2017). Intelligent calibration technique using optimized fuzzy logic controller for controller for ultrasonic flow sensor. Mathematical Modelling of Engineering Problems 4(2): 91-94.

[11]  Yang XS, Mehmet K, He XS. (2013). Multi-objective Flower Algorithm for Optimization. International Conference on Computational Science, ICCS 2013. 

[12]  Walker M. (2009). How flowers conquered the world, BBC Earth News. 

[13] Yang XS. (2012). Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, Lecture Notes in Computer Science 7445: 240-249. 

[14] Gaganpreet K, Dr. DS. (2012). Pollination based optimization or color image segmentation. International Journal of Computer Engineering and Technology (IJCET) 3(2): 407-414. 

[15]  KS. (2012). Pollination based optimization. 6th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN2012). 

[16]  Waser N. (1986). Flower constancy: definition, cause and measurement. The American Naturalist 127(5): 593-603.

[17] Osama AR, Mohamed AB, Ibrahim EH. (2014). A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles. International Journal of Engineering Trends and Technology (IJETT) 7(3): 126-132. 

[18] Yang XS. (2010). Engineering optimization: an introduction with metaheuristic application. Wiley. 

[19] Abbass HA, Sarker R. (2002). The Pareto diffential evolution algorithm. Int. J. Artificial Intelligence Tools 11(4): 531-552. 

[20] Yang XS. Nature-inspired Metaheuristic Algorithms. Luniver Press. 

[21] Deb K. (2001). Multi-objective Optimization Using Evolutionary Algorithms. New York: John Wiley & Sons. 

[22] Wang G, Guo L. (2013). A novel hybrid bat algorithm with harmony search for global numerical optimization. Journal of Applied Mathematics 2013: 21. 

[23] Yang XS. (2010). A new metaheuristic bat-inspired algorithm, nature inspired cooperative strategies for optimization (NISCO 2010). Springer 284: 65-74. 

[24] Khan K, Sahai A. (2012). A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. I.J. Intelligent Systems and Applications 23-29. 

[25] Wasiulkabir M, Sakib N Sakib, Syed MRC, Shafiul Alam M. (2014). A novel adaptive bat algorithm to control explorations and exploitations for continuous optimization problems. International Journal of Computer Applications 94(13). 

[26] Rekaby A. (2013). Directed Artificial Bat Algorithm (DABA) a new bio-inspired algorithm. International Conference on Advances in Computing, Communications and Informatics (ICACCI), Cairo. 

[27] Selim Y, Ecir UK. (2013). Improved Bat Algorithm (IBA) on continuous optimization problems. Lecture Notes on Software Engineering 1(3): 279-283. 

[28] Huang GQ, Zhao W J, Lu QQ. (2013). Bat algorithm with global convergence for solving large scale optimization problem. Application Research of Computers 30(3): 1-10. 

[29] Asghar A, Abdul Raman AA, Wan Daud WMA. (2014). A comparison of central composite design and taguchi method for optimizing fenton process. The Scientific World Journal 2014.

[30] Bremhost K, Graham L.J.W. (1990). A fully compensated hot/cold wire anemometer system for unsteady flow velocity and temperature measurement. Measurement Science & Technology 1(5).

[31] Moh’d S, Ahmed Al S. (2006). Optimization of hot wire thermal flow sensor based on neural net model. Applied Thermal Engineering 26(8-9): 948-955.

[32] Pijush D, Asok K. (2017). Intelligent calibration technique using optimized fuzzy logic controller for ultrasonic flow sensor. Mathematical Modelling of Engineering Problems 4(2): 91-94.

[33] Satish Chandra B, Samik M. (2012). Study of a simplelinearization technique of a p-n junction type anemometer flow sensor. IEEE Transaction Instrumentation and Measurement 61(9): 545-2552.