通过NJWTP的创新数据驱动建模和优化方法来提高沼气生产
Jingsong Duan1,2,3, Guohua Cao4,5, Guoqing Ma1,3,6,7
1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, 130022, Jilin, China.
Scientific reports
|February 9, 2025
概括
一个新的深度信念网络与增强的鱼优化算法 (DBN-BOOA) 显著提高了废水中的沼气生产. 这种数据驱动的方法优化了操作参数,以增加能量回收和减少污泥.
科学领域:
- 环境工程 环境工程
- 生物技术是生物技术.
- 数据科学数据科学数据科学
背景情况:
- 污水处理厂 (WWTP) 产生大量的污泥,这带来了处理挑战和能源回收的机会.
- 无氧消化是将有机废物转化为可再生能源生物气的关键过程.
- 优化无氧消化需要复杂的建模来管理复杂的生物和操作变量.
研究的目的:
- 开发和评估数据驱动的模型,通过废水污泥的无氧消化来增强生物气生产.
- 为了比较深信网络 (DBN),DBN与 Osprey优化算法 (DBN-OOA) 的性能,以及DBN与增强 Osprey优化算法 (DBN-BOOA) 的性能.
- 确定最佳的操作参数,以最大限度地提高生物气产量并最大限度地减少污泥产量.
主要方法:
- 在2016年至2018年期间从南京江南污水处理厂 (NJWTP) 收集了180个数据点.
- 开发并比较了三个机器学习模型:DBN,DBN-OOA和DBN-BOOA.
- 使用统计指标,包括相关系数 (R),根平均平方误差 (RMSE) 和一致指数 (IA) 来进行模型评估.
主要成果:
- 该DBN-BOOA模型实现了优越的性能,R=0.98,RMSE=0.41 m3/min,和IA=0.99.
- 在准确性和优化方面,DBN-BOOA显著优于独立的DBN和DBN-OOA模型.
- 确定了导致最大生物气产量为31.35 m3/min.的最佳参数.
结论:
- DBN-BOOA模型提供了一种高度准确和高效的方法来优化无氧消化和生物气生产.
- 这种数据驱动的方法不需要输入变量的预处理,这使得它适用于现实世界的应用.
- DBN-BOOA模型为废水处理厂运营商提供了一个用户友好的解决方案,以提高生物气产量和有效地管理污泥.
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