一种新的建模方法用于预测室外以藻类为基础的废水处理系统中细丝藻的生长
Sulochana Pitawala1, Peter J Scales1, Gregory J O Martin1
1Algal Processing Group, Department of Chemical Engineering, The University of Melbourne, Parkville, 3010, Victoria, Australia.
Biotechnology and bioengineering
|January 31, 2025
概括
一个新的数学模型通过考虑光和温度,准确地预测了废水处理中的细丝藻 (FA) 生物质生产率. 包括时间依赖抑制在内,显著提高了模型准确性,这对于高效的系统设计至关重要.
科学领域:
- 环境生物技术环境生物技术
- 水生植物学 水生植物学
- 数学建模的数学建模
背景情况:
- 丝状藻类 (FA) 提供了由于可采集生物质的废水处理的潜力.
- 在现实世界系统中预测FA性能是具有挑战性的,因为环境的变化和文化复杂性.
- 需要准确的预测模型来有效设计和运行基于FA的废水处理系统.
研究的目的:
- 开发一种描述静态丝状藻类生物质生产率的数学模型.
- 调查光强度,温度和时间依赖的抑制对FA生产力的影响.
- 通过公布的实验数据验证模型的预测能力.
主要方法:
- 开发了静态FA培养 (马特和草) 的数学模型.
- 作为关键的操作参数,内置的落灯强度和温度.
- 使用已发表的数据验证模型,包括对时间依赖的抑制效应的分析.
主要成果:
- 该模型准确地预测了FA生物质生产率,当包括时间依赖抑制时.
- 预测在时间依赖抑制的实验值的10%以内.
- 排除时间依赖的抑制导致生物质生产率的估计过高了6倍.
结论:
- 开发的模型是优化FA培养设计和废水处理中的操作的宝贵工具.
- 计算时间依赖抑制对于准确的生产率预测至关重要.
- 该模型可以扩展,包括营养素和二氧化碳的可用性,以加强应用.
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