一个通用的混合机器学习框架,用于通过有机废物的暗发酵来预测生物的生产
Nour Elislam Mougari1, Djamal Eddine Ghersi2, Farida Iachachene3,4
1Laboratory of Energy, Mechanics and Engineering (LEMI), Department of Mechanical Engineering, Faculty of Technology, University M'Hamed Bougara Boumerdes (UMBB), Frantz Fanon city, 35000, Boumerdes, Algeria. n.mougari@univ-boumerdes.dz.
Bioprocess and biosystems engineering
|November 8, 2025
概括
这项研究引入了一种新的混合模型,用于预测有机废物发酵的生物产量. 该方法使用动力参数和人工神经网络来获得准确和可解释的结果,推进可持续能源解决方案.
科学领域:
- 生物技术和生物工程 生物技术和生物工程
- 可持续能源生产 可持续能源生产
- 生物化学工程 生物化学工程
背景情况:
- 全球对可持续能源的需求推动了人们对从有机废物生产生物的兴趣.
- 准确预测生物产量对于过程优化和效率至关重要.
- 现有的方法在基质表征方面缺乏可解释性和生物学依据.
研究的目的:
- 开发一个强大的和可解释的生物产量预测框架.
- 将动态建模与混合贝叶斯优化-人工神经网络 (BO-ANN) 方法集成.
- 为了利用修改后的戈珀茨方程中的定量动力参数作为生物学上有意义的输入.
主要方法:
- 编制了过程变量 (温度,pH,停留时间,基质度) 和修改的戈珀茨运动参数的综合数据库.
- 采用贝叶斯优化 (BO) 来优化人工神经网络 (ANN) 架构.
- 使用5倍交叉验证进行模型概括评估和SHAP分析进行解释性.
主要成果:
- 在R2 = 0.9980,RMSE = 0.0117和MAE = 0.0062.2的情况下实现了出色的预测性能.
- 证明了拟议的混合BO-ANN模型的高精度和稳定性.
- SHAP分析证实了动态描述符在特征贡献中的相关性.
结论:
- 拟议的BO-ANN框架为生物产量预测提供了一个可扩展和可解释的工具.
- 使用动力参数的生物接地方法提高了预测准确度.
- 该框架支持设计更高效,更可持续的生物生产系统.
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