On‐line optimization of biotechnological processes: I. Application to open algal pond

A new on‐line optimization and control procedure applicable to biotechnological systems for which a precise mathematical model is unavailable has been developed and tested. The proposed approach is based on an online search for optimum operating conditions by an automatic system using a modified simplex algorithm to which several features have been added to permit real time operation. The simplex algorithm is the upper level of a hierarchical software package in which the other levels are cost evaluation, control, data acquisition, and signal processing. The optimization method was tested in a laboratory minipond for the cultivation of Spirulina platensis. The controlled parameters were light intensity, optical density, pH, and temperature. The proposed optimization method can be applied to other biological processes provided that the pertinent variables can be measured and controlled and the cost function can be defined mathematically.

[1]  P. Ryan,et al.  Simplex techniques for nonlinear optimization , 1980 .

[2]  Hugo Guterman,et al.  On line optimization of an arbitrary process by a personal microcomputer system: Application in algae ponds , 1985 .

[3]  W. A. Knorre,et al.  Optimal substrate profile for antibiotic fermentations , 1981 .

[4]  H. Guterman,et al.  Exchange rates of O2 and CO2 between an algal culture and atmosphere , 1987 .

[5]  S. Deming,et al.  Simplex optimization of analytical chemical methods , 1974 .

[6]  S. Nakai.  Comparison of Optimization Techniques for Application to Food Product and Process Development , 1982 .

[7]  JERMFERM: A fermentation process development game , 1978 .

[8]  D. Betteridge,et al.  Reflections on the modified simplex-I. , 1985, Talanta.

[9]  Israel Saguy.  Utilization of the “complex method” to optimize a fermentation process , 1982, Biotechnology and bioengineering.

[10]  Denis Dochain,et al.  Adaptive identification and control algorithms for nonlinear bacterial growth systems , 1984, Autom..

[11]  R. Zall,et al.  Optimizing alcohol production from whey using computer technology , 1985, Biotechnology and bioengineering.

[12]  A. Richmond,et al.  An automatic method for on‐line estimation of the photosynthetic rate in open algal ponds , 1985, Biotechnology and bioengineering.

[13]  G. Kateman,et al.  The symmetry-controlled simplex optimization procedure , 1983 .

[14]  Charles L. Cooney,et al.  Computer control of bakers' yeast production , 1979 .

[15]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[16]  S Shioya,et al.  Optimal quality control of bakers' yeast fed‐batch culture using population dynamics , 1982, Biotechnology and bioengineering.

[17]  C L Cooney,et al.  Growth monitoring and control in complex medium: A case study employing fed‐batch penicillin fermentation and computer‐aided on‐line mass balancing , 1983, Biotechnology and bioengineering.

[18]  Michimasa Kishimoto,et al.  Application of a statistical procedure for the control of yeast production , 1984, Biotechnology and bioengineering.

[19]  D. Naidu,et al.  Optimal Control Systems , 2018 .

[20]  E. Pungor,et al.  Automated fermentation equipment II. Computer–fermentor system , 1981 .

[21]  B. Cordenunsi,et al.  Mathematical model for the alcoholic fermentation in batch culture: Comparison between complete and incomplete factorial (33) designs , 1985 .

[22]  Petar V. Kokotovic,et al.  Optimal Control of Bacterial Growth , 1972 .

[23]  S. Ben-Yaakov,et al.  Automatic on‐line growth estimation method for outdoor algal biomass production , 1989, Biotechnology and bioengineering.

[24]  Charles L. Cooney.  Strategies for Optimizing Microbial Growth and Product Formation , 1983 .

[25]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .