New tuning method for generalized predictive control of the production of S-cerevisiae

Bursali N., Akay B., Ertunc S., Hapoglu H., Alpbaz M.

FOOD AND BIOPRODUCTS PROCESSING, cilt.79, sa.C1, ss.27-34, 2001 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 79 Sayı: C1
  • Basım Tarihi: 2001
  • Doi Numarası: 10.1205/09603080151123335
  • Dergi Adı: FOOD AND BIOPRODUCTS PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.27-34
  • Anahtar Kelimeler: generalized predictive control, bioprocess, temperature control, receding-horizon method, S. cerevisiae production, Box-Wilson optimization, SACCHAROMYCES-CEREVISIAE, RESPIRATORY CAPACITY, BAKERS-YEAST, GROWTH
  • Ankara Üniversitesi Adresli: Evet

Özet

In this study, S. cerevisiae was produced in a batch bioreactor in aerobic conditions and the growth medium temperature was controlled at its optimal value. In order to control the growth medium temperature, the Generalized Predictive Control (GPC) method was used. The process was described using an Auto Regressive Integrated Moving Average eXogenous (ARIMAX) model. Model parameters were determined by applying Pseudo Random Binary Sequence (PRBS) signals to the process and using Bierman's U-D Factorization Algorithm. By using statistical experimental design and Box-Wilson optimization methods, optimal values of the sampling time and weighting factor were determined. A two-level factorial experimental design technique was used to identify a statistical model. The predicted maximum, minimum and the base levels of sampling time and weighting factor were determined on the basis of the previous knowledge about the plant. It was proposed to determine the values of sampling time and weighting factor giving the best control performance. Integral Square Error (ISE) was selected as the optimization criterion. Growth medium temperature was controlled with very small levels of oscillation around the set point by using optimal controller parameters with GPC.