人工智能和图像处理的应用,用于种植Chlorella sp. 使用管状光生物反应器
Thananop Tummawai1, Thongchai Rohitatisha Srinophakun2, Surapol Padungthon3
1Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand.
ACS omega
|November 25, 2024
概括
这项研究开发了一个智能微藻种植系统,使用人工智能和物联网进行实时监控. 持续24小时的照明显著提高了Chlorella sp.中的生物质生产率. 种植,种植,种植,种植.
科学领域:
- 生物技术是生物技术.
- 农业工程 农业工程
- 环境科学 环境科学
背景情况:
- 微藻种植对于生物燃料,食品和环境修复的可持续生产至关重要.
- 对于微藻生长的传统监测方法往往是侵入性的,劳动密集的,并提供延迟的反.
- 整合先进技术可以优化微藻生产系统,以提高效率和可持续性.
研究的目的:
- 建立一个智能,闭环光生物反应器 (PBR) 系统,用于实时,非侵入性监测和管理Chlorella sp. 增长. 增长. 增长. 这就是增长.
- 利用计算流体动力学 (CFD),物联网 (IoT),人工智能 (AI) 和图像处理来优化微藻种植.
- 开发机器学习 (ML) 模型,用于预测和改善藻类养殖条件.
主要方法:
- 设计了一个封闭的管状光生物反应器,配备7个传感器,测量温度,pH值,光强度,EC值,流量,氧气和光持续时间.
- 一个ESP8266微控制器管理传感器数据和系统操作,而ESP32摄像头捕获图像用于增长评估.
- 机器学习模型,包括 eXtreme Gradient Boosting (XGBoost),在602个样本的数据集上进行了训练,在不同的光周期下.
主要成果:
- 连续24小时照明导致生物质生产率增加了7.19%,而12小时周期的生物质生产率增加了2.09%.
- 功能重要性分析确定温度和光强度是最重要的生长参数.
- 该XGBoost模型实现了R2 = 0.9997的高准确度,用于预测微藻生长.
结论:
- 智能技术,包括人工智能和物联网,显著提高了微藻生产的效率和可持续性.
- 实时,非侵入性监测和预测建模是优化Chlorella sp.的关键. 种植,种植,种植,种植.
- 这种先进的系统有可能在可再生能源,粮食安全和环境管理等领域应用.
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