通过贝叶斯分析改善ADM1预测,用于持续无氧消化
Yanxin Liu1, Ying Jiang2, Nasreen Nasar2
1Faculty of Engineering and Applied Sciences, Cranfield University, College Road, Cranfield, MK43 0AL, UK; Faculty of Environment, Science and Economy, University of Exeter, Stocker Road, Exeter, EX4 4PY, UK.
Journal of environmental management
|January 7, 2026
概括
本研究引入了贝叶斯框架,用于校准无氧消化模型No.1 (ADM1) 使用有限的初始数据. 这种方法提高了无氧消化器性能预测和优化,特别是在数据稀缺的环境中.
科学领域:
- 环境工程 环境工程
- 生物化学工程 生物化学工程
- 计算机建模 计算建模
背景情况:
- 无氧消化模型第1号 (ADM1) 校准具有挑战性,长期数据有限.
- 精确的模型校准对于优化无氧消化过程至关重要.
研究的目的:
- 开发一个贝叶斯推理框架,用于ADM1校准,仅使用初始的消化器性能数据.
- 为了使可靠的模型预测和风险知情设计在数据稀缺的设置.
主要方法:
- 开发了一个自定义的Python实现,集成全球灵敏度分析,贝叶斯校准和参数识别.
- 使用随机平衡设计-里叶振幅灵敏度测试 (RBD-FAST) 进行参数精细化.
- 根据现有ADM1研究得出的员工信息先验.
主要成果:
- 校准的ADM1数据的液压保留时间少于两次.
- 实现了pH值 (1.10%误差) 和总化学氧气需求 (5.38%误差) 的准确预测.
- 在63.14%的观测中,捕获的生物气生产趋势在95%可信度区间内.
结论:
- 贝叶斯框架使用有限的早期数据提供可靠的ADM1校准和预测.
- 与统一的先验相比,信息先验显著提高了预测准确性.
- 该方法支持提高无氧消化作业的安全性,可持续性和优化.
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