Korean Chemical Engineering Research

The Korean Institute of Chemical Engineers (한국화학공학회)
권호 표지
DOI QR코드

DOI QR Code

Sensitivity Analysis with Optimal Input Design and Model Predictive Control for Microalgal Bioreactor Systems

미세조류 생물반응기 시스템의 민감도분석을 위한 최적입력설계 및 모델예측제어

  • Yoo, Sung Jin (School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University) ;
  • Oh, Se-Kyu (School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University) ;
  • Lee, Jong Min (School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University)
  • 유성진 (서울대학교 화학생물공학부, 화학공정 신기술 연구소) ;
  • 오세규 (서울대학교 화학생물공학부, 화학공정 신기술 연구소) ;
  • 이종민 (서울대학교 화학생물공학부, 화학공정 신기술 연구소)
  • Received : 2012.09.19
  • Accepted : 2012.10.22
  • Published : 2013.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

Microalgae have been suggested as a promising feedstock for producing biofuel because of their potential of lipid production. In this study, a first principles ODE model for microalgae growth and neutral lipid synthesis proposed by Surisetty et al. (2010) is investigated for the purpose of maximizing the rate of microalgae growth and the amount of neutral lipid. The model has 6 states and 12 parameters and follows the assumption of Droop model which explains the growth as a two-step phenomenon; the uptake of nutrients is first occurred in the cell, and then use of intra-cellular nutrient to support cells growth. In this study, optimal input design using D-optimality criterion is performed to compute the system input profile and sensitivity analysis is also performed to determine which parameters have a negligible effect on the model predictions. Furthermore, model predictive control based on successive linearization is implemented to maximize the amount of neutral lipid contents.

미세조류는 바이오연료를 생산하기 위해 필요한 성분인 지방질의 생산성이 우수하기 때문에 바이오연료의 유망한 원료로서 최근 많은 주목을 받고 있다. 본 연구에서는, 이러한 미세조류의 성장 속도와 미세조류 내부의 지방의 함량이 최대가 되도록 하기 위한 목적으로, 미세조류의 성장과 지방의 생성을 설명하는 제일원리(first principle)에 근거한 상미분방정식(ODE) 모델에 대하여 조사하였다. 모델은 6개의 상태변수와 12개의 파라미터로 이루어져 있으며, 미세조류의 성장을 영양분의 흡수와 흡수된 영양분에 의한 성장으로 두 단계로 나누어 설명한 Droop 모델의 가정을 따른다. 본 연구에서는 민감도 분석(Sensitivity analysis)을 위한 최대의 정보를 줄 수 있는 입력 신호를 결정하기 위해 D-optimality criterion을 이용한 최적 입력 설계(Optimal input design)를 수행하였으며, 구하여진 입력 신호를 적용하여 민감도 분석을 수행하여 모델에 좀 더 중요한 파라미터를 결정하였다. 또한 미세조류의 성장속도와 지방의 함량이 최대가 되도록 하기 위하여 모델 예측 제어(MPC)를 수행하였다.

Keywords

References

  1. Yusuf, C., "Biodiesel from Microalgae, " Biotechnology Advances, 25, 294-306(2007). https://doi.org/10.1016/j.biotechadv.2007.02.001
  2. Miao, X. and Wu, Q., "Biodiesel Production from Heterotrophic Microalgal Oil," Bioresour. Technol., 97, 841-846(2006). https://doi.org/10.1016/j.biortech.2005.04.008
  3. Minowa, T., Yokoyama, S.-Y., Kishimoto, M. and Okakura, T., "Oil Production from Algal Cells of Dunaliella Tertiolecta by Direct Thermochemical Liquefaction," Fuel, 74, 1735-1738(1995). https://doi.org/10.1016/0016-2361(95)80001-X
  4. Monod, J., "La Technique de la Culture Continue, Theorie et Applications," Annales de Institute Pasteur Paris, 79, 390-410 (1950).
  5. Droop, M., "Vitamin B12 and Marine Ecology. IV. The Kinetics of Uptake, Growth and Inhibition in Monochrysis Lutheri," Journal of the Marine Biological Association of the United Kingdom, 48, 689-733(1968). https://doi.org/10.1017/S0025315400019238
  6. Caperon, J. and Meyer, J., "Nitrogen-limited Growth of Marine Phytoplankton-II. Uptake Kinetics and Their Role in Nutrient Limited Growth of Phytoplankton," Deep Sea Research and Oceanographic Abstracts, 19, 619-632(1972). https://doi.org/10.1016/0011-7471(72)90090-3
  7. Surisetty, K., Hoz Siegler, H. D. L., McCaffrey, W. C. and Ben- Zvi, A., "Model Re-parameterization and Output Prediction for a Bioreactor System," Chem. Eng. Sci., 65, 4535-4547(2010). https://doi.org/10.1016/j.ces.2010.04.024
  8. Zoltan Kalman, N., "Model Based Control of a Yeast Fermentation Bioreactor Using Optimally Designed Artificial Neural Networks," Chem. Eng. J., 127, 95-109(2007). https://doi.org/10.1016/j.cej.2006.10.015
  9. Lee, J. H. and Ricker, N. L., "Extended Kalman Filter Based Nonlinear Model Predictive Control," Ind. Eng. Chem. Res., 33, 1530-1541(1994). https://doi.org/10.1021/ie00030a013
  10. Yao, K. Z., Shaw, B. M., Kou, B., McAuley, K. B. and Bacon, D. W., "Modeling Ethylene/Butene Copolymerization with Multisite Catalysts: Parameter Estimability and Experimental Design," Polym. React. Eng., 11, 563-588(2003). https://doi.org/10.1081/PRE-120024426
  11. Lee, C. J., Prasad, V. and Lee, J. M., "Stochastic Nonlinear Optimization for Robust Design of Catalysts," Ind. Eng. Chem. Res., 50, 3938-3946(2011). https://doi.org/10.1021/ie102103w
  12. Mitchell, T. J., "An Algorithm for the Construction of "D-Optimal" Experimental Designs," Technometrics 16, 203-210(1974).
  13. Kim, K. A., Spencer, S., Albeck, J., Burke, J., Sorger, P., Gaudet, S. and Kim, D. H., "Systematic Calibration of a Cell Signaling Network Model," BMC Bioinformatics, 11, 202(2010). https://doi.org/10.1186/1471-2105-11-202
  14. Saltelli, A., Tarantola, S. and Chan, K. P. S. "A Quantitative Model- Independent Method for Global Sensitivity Analysis of Model Output," Technometrics 41, 39-56(1999). https://doi.org/10.1080/00401706.1999.10485594

Cited by

  1. Microalgae Cultivation Using LED Light vol.52, pp.1, 2014, https://doi.org/10.9713/kcer.2014.52.1.8
  2. Microalgal Oil Recovery by Solvent Extraction from Nannochloropsis oceanica vol.52, pp.1, 2014, https://doi.org/10.9713/kcer.2014.52.1.88
  3. An Analysis of Lipid Contents Produced from Three Different Microalgae Depending on the Lighting Period and Their Saccharification Conversion vol.53, pp.4, 2015, https://doi.org/10.9713/kcer.2015.53.4.468
  4. 탈지미세조류의 무효소 당화를 위한 마이크로파 전처리 조건 최적화 vol.56, pp.2, 2013, https://doi.org/10.9713/kcer.2018.56.2.229