使用遗传编程对污染物降解和微生物生长的生物动力学建模的替代方法
Suganya Krishnan1, Chandrasekaran Sivapragasam1, Naresh K Sharma2
1Department of Civil Engineering, Centre for Water Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India.
Environmental technology
|January 29, 2025
概括
基因编程 (GP) 有效地模拟了微藻对的降解,优于传统方法. 这种数据驱动的方法在没有先前假设的情况下快速识别生物动力学原理,增强生态系统保护策略.
科学领域:
- 环境微生物学环境微生物学
- 生物技术是生物技术.
- 计算生物学是一种计算生物学.
背景情况:
- 传统的生物动力学模型需要大量的代谢数据和复杂的微分方程.
- 现有的降解机器学习模型缺乏数学模型的稳定性.
- 精确的建模对于优化污染物降解和微生物生长至关重要.
研究的目的:
- 应用基因编程 (GP) 来进行降解的强大的数学建模.
- 将GP的性能与传统的动力学方法进行比较.
- 为了证明GP能够从数据中推导出生物动力学原理的能力.
主要方法:
- 使用微藻Acutodesmus Obliquus进行降解实验.
- 采用基因编程 (GP) 作为主要的数据驱动建模技术.
- 应用了传统的动力学方法来确定特定增长率 (μmax) 和和常数 (K) 进行比较.
主要成果:
- 医生成功模拟了的降解,在216小时内实现了98%的去除.
- 在没有先验信息的情况下,GP开发了一种与莫诺德动力学相一致的模型.
- 与传统方法相比,GP表现出更高的预测准确度,由较低的RMSE和更高的R证明.
结论:
- 遗传编程为生物动力学建模提供了一个强大而高效的数据驱动的替代方案.
- 医生可以快速揭示基本的生物动力学原理,弥合数据和机械学理解之间的差距.
- 这种方法提高了污染物降解过程的优化,以保护生态系统.
相关概念视频
00:46Bioremediation
18.2K
Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.
18.2K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38