用陶膜技术连续动的水箱生物反应器的神经模糊建模,用于处理石油炼油厂废水:印度阿萨姆州的一个案例研究
Tanushree Paul1, Ayushi Aggarwal2,3, Shishir Kumar Behera4,5
1Center for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India.
Bioprocess and biosystems engineering
|December 12, 2023
概括
这项研究使用了生物反应器来处理炼油厂的废水,实现了高化学氧需求 (COD) 清除和脂质生产. 神经模糊神经网络 (NF-NN) 模型准确地预测了这些结果,超过了传统的人工神经网络 (ANN).
科学领域:
- 生物技术和环境工程 生物技术和环境工程
- 废水处理技术 废水处理技术
- 生物反应器设计和优化
背景情况:
- 由于高化学氧气需求 (COD),石油炼油厂的废水给环境带来了挑战.
- 同时去除COD和生产有价值的副产品 (脂质) 是可持续废水管理的理想选择.
- 细胞回收和膜技术为生物反应器效率提供了先进的解决方案.
研究的目的:
- 评估持续水箱生物反应器 (CSTB) 的性能,用于处理石油炼油厂废水.
- 开发和比较人工智能 (AI) 模型,特别是人工神经网络 (ANN) 和神经模糊神经网络 (NF-NN),用于预测COD去除效率和脂质生产.
- 为了确定最佳的操作条件,最大限度地消除COD和脂质产量.
主要方法:
- 使用连续式水箱生物反应器 (CSTB) 采用陶膜技术和Rhodococcus opacus PD630用于废水处理.
- 采用人工神经网络 (ANN) 和神经模糊神经网络 (NF-NN) 模型来预测COD去除效率 (CODRE) 和脂质度.
- 测试了NF-NN的三个学习算法,贝叶斯规范化反向传播 (BR-BP) 显示出卓越的性能.
- 进行了灵敏度分析,以确定固体保留时间和生物质度对治疗结果的影响.
主要成果:
- 与ANN模型相比,NF-NN模型显示出更高的预测准确性,由更高的确定系数 (R2) 和更低的RMSE和MAPE值证明.
- 贝叶斯规范化反向传播 (BR-BP) 学习算法为NF-NN模型产生了最好的结果.
- 敏感性分析表明,保持固体保留时间在3575小时之间,生物质度在3.03.5g/L之间,始终实现高COD去除效率 (93%) 和脂质度 (2.8g/L).
结论:
- 神经模糊神经网络 (NF-NN) 模型,特别是与BR-BP算法,对于预测废水处理生物反应器系统的性能非常有效.
- 集成的CSTB和膜系统显示了同时减少COD和从石油炼油厂废水中回收脂质的巨大潜力.
- 优化的操作参数对于在生物修复过程中实现高效率和一致的结果至关重要.
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