The main contributions of the book are:
• A new neural softsensor is proposed for on-line biomass prediction requiring only the value of DO, feed rate and volume to be measured.
• A novel cascade neural model is developed for modelling the fed-batch fermentation processes. It provides a reliable and efficient representation of the system to be modelled for optimization purposes.
• A new cost-effective methodology, which combine GAs and dynamic neural networks, is established to successfully model and optimize the fed-batch fermentation processes without a priori knowledge and detailed kinetics models.
• A new strategy for on-line identification and optimization of fed-batch fermentation processes is proposed using GAs.
• Modified GAs are presented to achieve fast convergence rates as well as global solutions.
• A comparison of a GA and DP has shown that the GA is more powerful for solving high order nonlinear dynamic constrained optimization problems.
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