What are the new technology available for response surface methodology or other optimization technique. If any one working in optimization of fermentation technology please reply....
Many optimization techniques are available for optimization of fermentation medium and fermentation process conditions such as borrowing, component swapping, biological mimicry, one - factor - at - a- time, factorial design, Placket and Burman design, central composite, response surface methodology, evolutionary operation, evolutionary operation factorial design. artificial neural network, fuzzy logic and genetic algorithms. Each optimization technique has its own advantages and disadvantages. In bio-process industry it often needs to conduct optimization experiments because new mutants and strains are... For more details consult Research Journal of Microbiology Vol. 2 ( 3 ): 201 - 208, 2007.
The recent optimisation is by AI using MATLAB.....it's a free version and more accurate. You can refer this paper "Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models" as reference.
All the best
For the fluid aspects, computational fluid dynamics is gaining ground in fermentation. CFD is used to optimize reactor designs (effect of geometry on hold-up, mass transfer, substrate mixing, etc.). CFD has been used to find the optimum between minimal shear rate and proper cell suspension for cell cultures ( Louviere et al.). Siemens STAR-CCM+ offers an optimization add-on where you can automatically vary geometrical parameters to find pareto-fronts for optimization, e.g. energy input vs. mixing time, gassing rate vs. kla,...
In my own work I combined CFD with metabolic models to make in-silico assessments of productivity under non-ideal mixing conditions in fermentors, which can be used in optimizing lab-to-factory transfer and assessing potential yield losses with a model driven approach.
The evolving technologies in fermentation optimization are response surface methodology and artificial intelligence, just like Karanam said. However, if you carefully execute fermentation optimization experiment and obtain response surface model adjusted R2 value of up to 0.99, then there is very little need for artificial intelligence modeling and optimization.Oftentimes researchers are not careful or patient enough with RSM and so end up with a lot of noise which is what artificial intelligence has to remove during the training, testing and validation. Different algorithms have emerged which could be linked to ANN to facilitate optimization. A quick list include particle swarm optimization, genetic algorithm, ant colony optimization and differential evolution algorithm. Several fermentation parameters including nutrient comprising trace and major, fermentation conditions like temperature, pH, aeration and agitation as well as fermenter geometry. Follow this link Article Response surface modeling and optimization of major medium v...
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