ENDÜSTRİYEL BİYOLOJİK FERMANTASYON İŞLEMİ İÇİN DENGE OPTİMİZASYON ALGORİTMASIYLA KONTROLÖR TASARIMI

Year 2021, Volume: 9 Issue: 1, 164 - 179, 02.03.2021

Abdullah Ateş

https://doi.org/10.36306/konjes.738684

Abstract

Bu çalışmada yeni bir meta sezgisel optimizasyon yöntemi olan Denge Optimizasyon (DO) algoritması ile aşı üretiminde gerçekleştirilen biyolojik fermantasyon işleminde kullanılan karıştırıcı modelleri için PID kontrolörler tasarlanmıştır. Öncelikle Denge Optimizasyon algoritması PID kontrolör parametrelerini optimize edebilecek kabiliyete ulaştırılmıştır. Daha sonra algoritmanın çalışma performansına etki eden parametrelerde çalışma esnasında deneysel olarak ayarlanmıştır. Özellikle literatürde daha önceden karıştırıcı modelleri için analitik yöntemlerle tasarlanmış olan PID kontrolör yerine performansı daha iyi olan Denge Optimizasyon algoritmasıyla tasarlanmış PID kontrolörlerin kapalı çevrim sonuçları karşılaştırmalı olarak sunulmuştur. Bu sayede yeni bir algoritma olan denge optimizasyon algoritmasının gerçek mühendislik problemlerinde de kullanılabileceği ve analitik yöntemlere karşın daha iyi kontrol performansına sahip PID kontrolörler türetilebileceği gösterilmektedir.

Keywords

denge optimizasyon, biyolojik fermentasyon, PID, kontrolör

Controller Design with Equilibrium Optimization Algorithm for an industrial biological fermentation process

Abstract

In this study, PID controllers are designed for the mixer mathematical models for biological fermentation process in vaccine production with the Equilibrium Optimization (EO) algorithm, which is a new meta-heuristic optimization method. Firstly, Equilibrium Optimization algorithm has been provided with the ability to optimize PID controller parameters. Then, the parameters affecting the working performance of the algorithm were found experimentally during the study. In the literature, the closed loop results of PID controllers designed with the Equilibrium Optimization algorithm, which was previously designed with analytical methods for mixer system, have been presented comparatively. Thus, it is shown that the Equilibrium Optimization algorithm, which is a new algorithm, can also be used in real engineering problems and PID controllers with better control performance can be derived despite analytical methods.

Keywords

Equilibrium optimizer, biological fermentation, PID, controller

References

• Atashpaz-Gargari, Esmaeil, and Caro Lucas. 2007. “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition.” In 2007 IEEE Congress on Evolutionary Computation, CEC 2007,.
• Ateş, A., and C. Yeroglu. 2016. “Optimal Fractional Order PID Design via Tabu Search Based Algorithm.” ISA Transactions 60.
• Ateş, A., and C. Yeroğlu. 2018. “Modified Artificial Physics Optimization for Multi-Parameter Functions.” Iranian Journal of Science and Technology - Transactions of Electrical Engineering 42(4).
• Bazaraa, Mokhtar S., Hanif D. Sherali, and C. M. Shetty. 2005. Nonlinear Programming: Theory and Algorithms Nonlinear Programming: Theory and Algorithms.
• Dorigo, Marco, and Krzysztof Socha. 2007. “Ant Colony Optimization.” In Handbook of Approximation Algorithms and Metaheuristics,.
• Faramarzi, Afshin, Mohammad Heidarinejad, Brent Stephens, and Seyedali Mirjalili. 2020. “Equilibrium Optimizer: A Novel Optimization Algorithm.” Knowledge-Based Systems.
• Gandomi, Amir Hossein, Xin She Yang, and Amir Hossein Alavi. 2013. “Cuckoo Search Algorithm: A Metaheuristic Approach to Solve Structural Optimization Problems.” Engineering with Computers.
• Guo, Zhishi. 2002. “Review of Indoor Emission Source Models. Part 1. Overview.” Environmental Pollution.
• Karaboga, Dervis, and Bahriye Akay. 2009. “A Comparative Study of Artificial Bee Colony Algorithm.” Applied Mathematics and Computation.
• Kaveh, A., and S. Talatahari. 2010. “A Novel Heuristic Optimization Method: Charged System Search.” Acta Mechanica.
• Khan, Omar et al. 2018. “Optimized PID Controller for an Industrial Biological Fermentation Process.” Journal of Process Control.
• Kuhn, Harold W., and Albert W. Tucker. 2014. “Nonlinear Programming.” In Traces and Emergence of Nonlinear Programming,.
• Lin, Ming Hua, Jung Fa Tsai, and Chian Son Yu. 2012. “A Review of Deterministic Optimization Methods in Engineering and Management.” Mathematical Problems in Engineering.
• Mirjalili, Seyedali et al. 2017. “Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems.” Advances in Engineering Software.
• Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. 2014. “Grey Wolf Optimizer.” Advances in Engineering Software.
• Rabani, Yuval. 2007. “Linear Programming.” In Handbook of Approximation Algorithms and Metaheuristics,.
• Rao, R. V., V. J. Savsani, and D. P. Vakharia. 2011. “Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems.” CAD Computer Aided Design.
• Rashedi, Esmat, Hossein Nezamabadi-pour, and Saeid Saryazdi. 2009. “GSA: A Gravitational Search Algorithm.” Information Sciences.
• Yeroǧlu, C., and A. Ateş. 2014. “A Stochastic Multi-Parameters Divergence Method for Online Auto- Tuning of Fractional Order PID Controllers.” Journal of the Franklin Institute 351(5).
• Zhang, Chunlei, and Raúl Ordóñez. 2012. “Numerical Optimization.” In Advances in Industrial Control,.