基于混合模型及其校正策略的发酵过程实时优化研究

项目名称： 基于混合模型及其校正策略的发酵过程实时优化研究

项目编号： No.61304121

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 牛大鹏

作者单位： 东北大学

项目金额： 22万元

中文摘要： 为了提高发酵过程的生产水平和经济效益，有必要对其优化控制问题进行深入研究。现有的发酵过程优化方法多为基于机理模型的离线优化，由于发酵过程极其复杂且具有慢时变特性，经常出现模型失配及优化结果难以实施等问题。为此，本项目提出开展基于混合模型及其校正策略的发酵过程实时优化研究：根据发酵过程特性，提出采用分时段思想建立其基本机理模型、并利用集成建模方法建立经验模型的混合建模思路，同时对建模过程中的异常数据检测与处理问题进行深入探讨；基于模型预测可信度和参数灵敏度分析的思想，进行混合模型的校正；将模型预测可信度约束作为不等式约束加入发酵过程的动态实时优化模型中，开发高性能的优化算法求解优化问题，并结合可行性分析和迭代学习的思想制定优化结果的实施策略；在发酵实验系统的基础上开发生物发酵过程实时优化控制平台，对所提出的方法进行验证与完善，并面向典型的发酵工业生产过程进行推广应用。

中文关键词： 发酵；混合模型；校正策略；优化控制；参数灵敏度分析

英文摘要： In order to improve production levels and economic benefits of the fermentation process, it is necessary to deeply research its optimal control problems. Current methods for optimization of the fermentation process are mostly off-line, which are based on mechanistic models. However, the models are often not well-matched and the off-line optimization results are difficult to implement, because the fermentation process is extremely complex and has slow time-varying characteristics. In this project, based on hybrid model and its correction strategy, real-time optimization techniques for the fermentation process are proposed: according to specific characteristics of the fermentation process, its multi-phase mechanistic models are established and ensemble modeling methods are used to build the empirical model so as to obtain the hybrid model, while abnormal data detection and processing problems during modeling are deeply investigated; hybrid model correction is carried out on the basis of model prediction reliability and parameter sensitivity analysis; with model prediction reliability constraints being added into the fermentation dynamic real-time optimization model as inequality constraints, high-performance optimization algorithms are developed for the optimization problem, then the implementation strategy of th

英文关键词： fermentation；hybrid model；correction strategy；optimal control；parameter sensitivity analysis