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通过每月生物质预测系统实现适应性藻类培养

Hongxiang Yan¹, Song Gao², Mark S Wigmosta^1,3

¹Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.

Biotechnology and bioengineering

|December 6, 2025

概括

使用天气模型预测微藻生物量生产可以改善菌株和池深度的选择,提高产量15%并提高对环境变化的弹性.

科学领域:

可持续的生物燃料和生物产品
藻类种植和生物质生产
环境数据同化和预测.

背景情况:

户外微藻种植面临环境变化的挑战,影响生物燃料和生物产品的开发.
需要准确的预测来优化操作决策,如菌株选择和池管理.
现有的方法缺乏适应性微藻养殖的足够准确性.

研究的目的:

为微藻种植开发和评估一个实验性的每月生物质预测系统.
以指导运营决策,包括最佳应变和池深度的选择.
提高藻类生产系统的适应能力和耐候性.

主要方法:

使用生物质评估工具 (BAT) 与基于气候学 (NLDAS-2) 和多模型组合 (NMME) 的预测方法.
在亚利桑那州 (2020-2024) 的四个池深度 (15-30厘米) 评估了两种藻类菌株 (Picochlorum celeri,Tetraselmis striata) 的生物质生产策略.
与实际生物质产量相比,预测模型在预测最佳应变和池深度方面的准确性.

主要成果:

一个NMME模型在每月识别最佳应变和池深度时达到84%的准确度;NMME套件的准确度在74%至84%之间.
基于NLDAS-2气候学的方法获得了78%的准确性;菌株选择比池深度选择更准确 (高达92%).
基于预测的策略,与技术状态相比,平均生物质产量增加了15%,有些月超过40%的收益.

结论:

以预测为指导的战略显著提高了微藻生物质生产和运营效率.
开发的系统提供了一个可扩展和灵活的工具,用于适应性,耐候性藻类种植.
这种方法对于在可变环境条件下推进可持续生物燃料和生物产品开发至关重要.

关键词:

胡塞曼藻类生物质生长模型皮科克洛 (Picochlorum celeri) 是一种菜.三的条纹状 (Tetraselmis striata) 是一个月度生物质预测.池的水深是水的深度.菌株选择菌株选择

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