一种新的数据驱动模型用于预测和适应性控制微藻种植的赛道反应堆中的pH值
M Caparroz1, J L Guzmán1, M Berenguel1
1University of Almería, Department of Informatics, ceiA3, CIESOL, Ctra. Sacramento, Almería 04120, Spain.
New biotechnology
|April 14, 2024
概括
一个新的数据驱动模型预测了微藻光生物反应器中的pH值变化,将光合作用和二氧化碳注射效应分开. 这个模型适应不断变化的条件,改善pH控制策略.
科学领域:
- 生物技术是生物技术.
- 环境工程 环境工程
- 化学工程是化学工程的重要组成部分.
背景情况:
- 保持最佳pH值对于光生物反应器中微藻种植至关重要.
- pH值波动受到生物活动 (光合作用) 和操作参数 (CO2注射) 的影响.
- 现有的模型往往缺乏适应动态环境和操作条件的适应性.
研究的目的:
- 开发一种新的数据驱动模型,用于估计和预测淡水赛道光生物反应堆中的pH动态.
- 区分和建模光合作用和二氧化碳注入对pH值变化的贡献.
- 根据开发的pH模型创建一个自适应性控制策略.
主要方法:
- 使用反应堆测量开发了一个数据驱动模型.
- 该模型将pH动态分解为光合作用驱动和CO2驱动的组件.
- 采用决策树算法来捕捉基于系统变量 (太阳辐射,温度,中等水平) 的模型参数变化.
- 该模型在半工业赛道反应堆中经过100天的验证.
- 设计了一个自适应控制算法,并进行了实验测试.
主要成果:
- 数据驱动的模型准确地估计和预测了pH动态.
- 该模型成功地区分了由光合作用和二氧化碳注入引起的pH值变化.
- 决策树算法有效地捕获了与环境和操作因素相关的参数变化.
- 与经典的固定参数方法相比,自适应控制算法表现出更好的性能.
结论:
- 拟议的数据驱动模型提供了一个强大的方法来理解和预测光生物反应器中的pH行为.
- 根据动态pH模型进行的自适应控制策略,为微藻种植提供了增强的过程控制.
- 这种方法对优化光生物反应器运行和提高生物质生产率有重大影响.
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