Process Modeling and Optimization of Biotechnology Systems Using Parameter Function Neural Networks

该项目旨在开发一个新的框架，使用神经网络和基本的过程知识来建模，优化和控制动态生物技术过程。该研究小组最近提出了参数函数神经网络作为一种有效的手段，使用基本的过程知识和神经网络来模拟动态过程。他们使用参数函数概念，使用可从巴克斯特血红蛋白治疗公司获得的实验数据，对通过诱导重组细菌生产血红蛋白的工业重要过程进行建模。在一般情况下，本项目研究的可行性，使用参数函数神经网络建模技术，成功地模拟一个复杂的工业生物技术发酵，并找到新的改进的操作策略，该过程。该项目的预期结果可能对工业过程的建模和优化产生广泛的影响，特别是补料分批生物反应器的生物技术过程。

This project aims to develop a new framework for using neural networks and fundamental process knowledge to model, optimize and control dynamic biotechnology processes. This research group has recently proposed parameter function neural networks as an effective means of using both fundamental process knowledge and neural networks to model dynamic processes. They use the parameter function concept to model the industrially important process of the production of hemoglobin though induced recombinant bacteria using experimental data available from Baxter Hemoglobin Therapeutics. In general, this project investigates the feasibility of using the parameter function neural network modeling technique to successfully model a complex industrial biotechnology fermentation and find new improved operational strategies for that process. The anticipated results of this project may have widespread impact on modeling and optimization of industrial processes, especially the biotechnology processes of fed-batch bioreactors.