Download Modelling and Optimization of Biotechnological Processes by Lei Zhi Chen PDF

By Lei Zhi Chen

Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe significant di?culties of employing complex keep an eye on theories is the hugely nonlinear nature of the strategies. This ebook examines techniques in keeping with arti?cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for tracking, modelling and optimization of fed-batch fermentation techniques. the most target of a method keep watch over is to maximise the ?nal product with minimal improvement and construction expenditures. This e-book is interdisciplinary in nature, combining subject matters from biotechn- ogy, arti?cial intelligence, method identi?cation, method tracking, strategy modelling and optimum keep an eye on. either simulation and experimental validation are played during this learn to illustrate the suitability and feasibility of proposed methodologies. an internet biomass sensor is built utilizing a - present neural community for predicting the biomass focus on-line with merely 3 measurements (dissolved oxygen, quantity and feed rate). effects convey that the proposed sensor is analogous or perhaps better to different sensors proposed within the literature that use greater than 3 measurements. Biote- nological techniques are modelled by way of cascading recurrent neural networks. it truly is stumbled on that neural versions may be able to describe the approaches with excessive accuracy. Optimization of the ?nal product is accomplished utilizing modi?ed genetic algorithms to figure out optimum feed fee pro?les. Experimental result of the corresponding creation yields show that genetic algorithms are robust instruments for optimization of hugely nonlinear platforms. in addition, a c- bination of recurrentneural networks and genetic algorithms presents an invaluable and cost-e?ective technique for optimizing biotechnological procedures.

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M = 10 and q = 1000). 10) The feed rate vector of the glucose is the first m elements and the feed rate vector of glutamine is the last m elements. 251 L/day for five days then followed by a zero rate. 045 L/day). 136 L/day). 7 show the corresponding histories of culture volume and MAb, respectively. The determination of the optimal varying feed rate trajectories for the multi-feed case required about three hours on a Pentium 100 using MATLAB GAOT software. 2 shows the effect of m on the final level of MAb.

Y Obtain fermentation parameters Initialize Population of Control Profiles Evaluate Objective Function J o Run Genetic Algorithm N Optimization Procedure Termination Condition Reached? Y Obtain optimal feed rate profile Run Fed-batch Culture Automatically Under The Control of Optimal Profile Applying The Optimal Control Profile END Fig. 1. Schematic diagram of the methodology proposed. 33 34 3 On-line Identification and Optimization were also given in Appendix A. Ten samples measured in equal time length were used as actual state variables.

3. Comparison between GA and DP. 6 Conclusions In this work, the seventh order system is used to describe the fed-batch culture of hybridoma cells and the GA is used to maximize the final MAb production. Optimal feed rate trajectories for a feed stream containing both glucose and glutamine (single-feed case), and separate feed streams of glucose and glutamine (multi-feed case) are searched for using the GA. As compared to the optimal constant feed rate, optimal varying feed rate trajectories are shown to improve the final MAb concentration by 10% for the single-feed rate case and by 39% for the multi-feed rate case.

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