By Lei Zhi Chen
Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe significant di?culties of employing complicated keep watch over theories is the hugely nonlinear nature of the tactics. This ebook examines methods in response to arti?cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for tracking, modelling and optimization of fed-batch fermentation tactics. the most objective of a method keep an eye on is to maximise the ?nal product with minimal improvement and construction bills. This publication is interdisciplinary in nature, combining issues from biotechn- ogy, arti?cial intelligence, process identi?cation, method tracking, method modelling and optimum regulate. either simulation and experimental validation are played during this learn to illustrate the suitability and feasibility of proposed methodologies. a web biomass sensor is developed utilizing a - present neural community for predicting the biomass focus on-line with basically 3 measurements (dissolved oxygen, quantity and feed rate). effects convey that the proposed sensor is similar or perhaps more suitable to different sensors proposed within the literature that use greater than 3 measurements. Biote- nological procedures are modelled via cascading recurrent neural networks. it really is came across 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 price pro?les. Experimental result of the corresponding creation yields reveal that genetic algorithms are strong instruments for optimization of hugely nonlinear platforms. additionally, a c- bination of recurrentneural networks and genetic algorithms offers an invaluable and cost-e?ective technique for optimizing biotechnological tactics.
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Extra info for Modelling and Optimization of Biotechnological Processes
In Chapter 4, a softsensor is proposed using RNN for predicting biomass concentration from the measurement of DO, feed rate and volume. In this chapter, we intend to model the fed-batch fermentation of Saccharomyces cerevisiae from the input of feed rate to the output of biomass concentration by cascading two softsensors. 4 in Chapter 1. In this structure, besides the output feedback, the activation feedbacks are also incorporated into the network, and TDLs are used to handle the delays. A dynamic model is built by cascading two such extended RNNs for predicting biomass concentration.
The network with six hidden neurons was therefore chosen for the on-line biomass estimation because of the small prediction error and small size of the network. 5 0 0/0/0 0/1/1 0/2/1 0/3/1 0/4/1 1/1/1 1/2/1 1/3/1 1/4/1 Number of delays Fig. 4. Estimation root mean squared percentage error on testing data sets for neural networks with diﬀerent combinations of delays. ‘0/4/1’ indicates that input delay is zero, the number of output feedback delays are 1, 2, 3, and 4, the number of activation feedback delay is 1.
The network input delays: 1, 2; output feedback delays: 1, 2, 3, 4; activation feedback delay: 0. 3% 5 4 3 2 1 0 0 50 100 150 200 250 300 350 400 450 500 Time (minutes) Fig. 12. On-line biomass concentration prediction in a fed-batch baker’s yeast fermentation process. The network input delays: 1, 2; output feedback delays: 1, 2, 3, 4; activation feedback delay: 1. 1. The experimental results show that the highest predictive ability is obtained from the neural softsensor with two input delays, four output feedback delays and one activation feedback delay.