Recurrent Neural Networks and Micro-Genetic Algorithms for Estimation and Optimization: Application to Bioprocesses

用于估计和优化的循环神经网络和微遗传算法：在生物过程中的应用

• 批准号: 9118955
• 负责人: Muhammad Karim
• 金额: $ 20.74万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-03-15 至 1995-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9118955&HistoricalAwards=false
• 关键词: Recurrent; Neural; Networks; Micro; Genetic

This project is aimed at helping bioprocess monitoring, control, and optimization by providing a novel technique for on-line process variable estimation and optimization. Fermentation kinetic models seldom include the influence of environmental factors, like pH and temperature. The development of accurate quantitative models, which simulate how cells respond to various environmental changes, will help in better utilizing the chemical synthesis capabilities of cells. The proposed method will use environmental and physiological information available from on-line sensors to estimate concentration of species in a bioreactor. Based on these estimates, the optimization scheme will provide the best environmental conditions which will drive the cells to their maximum productivity.

该项目旨在帮助生物过程监测， 通过提供一种新的技术， 用于在线过程变量估计和优化。 发酵动力学模型很少考虑 环境因素，如pH值和温度。 的 建立精确的定量模型， 模拟细胞如何对各种环境 改变，将有助于更好地利用化学品 细胞的合成能力。 所提出的方法将 利用环境和生理信息 从在线传感器来估计物种的浓度 在生物反应器中。 根据这些估计， 优化方案将提供最佳的环境 这些条件将使细胞达到最大值 生产力