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ITS » Undergraduate Theses » Teknik Fisika S1
Posted by at 16/03/2011 09:58:40  •  2690 Views


Author :
LUFDIANSYAH, WAHYU ( 2405100057 )


Sistem Bioreaktor merupakan sistem yang kompleks dan mudah tidak stabil apabila mendapat gangguan dari luar. Sistem ini merupakan sistem MIMO yang melibatkan beberapa variabel diantaranya ialah pH dan laju gas metan sebagai variabel output Penelitian ini merancang sistem kontrol multivariabel yang dapat mengendalikan process variabel pH dan qM dengan memanipulasi laju dilusi dan laju bikabornat. Algoritma sistem kontrol yang digunakan adalah Generalized Predictive Control dengan menggunakan sistem identifikasi ARX untuk mendapatkan model proses Bioreaktor. Teknik optimasi yang digunakan adalah quadratic programming dan genetic algorithm. Hal ini bertujuan untuk mengetahui teknik optimasi mana yang lebih baik untuk diterapkan. Hasil yang didapatkan ialah GPC dengan teknik optimasi quadratic programming QP lebih unggul dibandingkan genetic algorithm GA. Max overshoot kondisi normal QP 1.5 sedangkan GA 6 uji beban naik QP 9 sedangkan GA 15 uji beban turun QP 0.9 sedangkan GA 3 uji set point turun QP 0.3 sedangkan GA 6.16 uji set point naik QP 0.9s sedangkan GA 6.16. Untuk settling time kondisi normal QP 15s sedangkan GA 65s uji beban naik. QP 25s sedangkan GA 30s uji beban turun QP 9s sedangkan GA 25s uji set point turun QP 7s sedangkan GA 40s uji set point naik QP 18s sedangkan GA 30s.


The bioreactor system is a complex system which easily unstable when a disturbance happen from outside. This system is a MIMO system that involves several variables such as pH and the rate of methane gas qM as an output variable This research is designing multivariable control systems that can control the process variables pH and qM by manipulating the dilution rate and the bikabornat rate. The control system algorithm used is Generalized Predictive Control using ARX as system identification to obtain bioreaktor process model. The optimization technique used is the quadratic programming and genetic algorithm. This optimization technique aims to find out where the better results obtained with the optimization technique The result is for max overshoot normal conditions QP 1.5 while the GA 6 rising load test QP 9 while the GA 15 down load test QP 0.9 while GA 3 Test set point down QP 0.3 while the GA 6.16 set point test ride QP 0.9 while the GA 6.16. For settling time the normal condition whereas QP 15s while the GA 65s rising load test. QP 25s while the GA 30s down load test QP 9s while the GA 25s the test set point down GA QP 7s while the 40s set point test ride QP 18s whereas GA 30s.

KeywordsBioreaktorr Anaerob; Generalized Predictive Control; Quadratic Programming; Algoritma Genetika
Subject:  Kontrol Prediktif
  1. Katherin Indriawati,ST.MT
Date Create: 16/03/2011
Type: Text
Format: pdf
Language: Indonesian
Identifier: ITS-Undergraduate-3100010040748
Collection ID: 3100010040748
Call Number: RSF 629.8 Luf p

Undergraduate Thesis, Physics Engineering, RSF 629.8 Luf p, 2010

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Copyright @2011 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library

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  1.  ITS-Undergraduate-13769-2405100057-Abstract_id.pdf - 353 KB
  2.  ITS-Undergraduate-13769-2405100057-Abstract_en.pdf - 405 KB
  3.  ITS-Undergraduate-13769-2405100057-Conclusion.pdf - 750 KB

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