模拟两阶段无氧现场废水卫生系统,通过机器学习预测废水可溶化学氧需求
Rajshree Mathur1, Meena Kumari Sharma1, K Loganathan2
1Department of Civil Engineering, Manipal University Jaipur, Jaipur, 303007, Rajasthan, India.
Scientific reports
|January 21, 2024
概括
这项研究使用机器学习预测无氧消化 (AD) 中的废水可溶化学氧需求 (SCOD). 人工神经网络被证明是最有效的,为废水处理中的早期水质预测提供了一种可靠的方法.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 计算机科学 计算机科学
背景情况:
- 无氧消化 (AD) 对于废水处理至关重要,但对操作因素非常敏感.
- 现实时间预测废水可溶化学氧需求 (SCOD) 是具有挑战性的,但对于工艺控制和降低成本至关重要.
- 复杂数学建模 (CMM) 为模拟AD和预测输出参数提供了一种可行的方法.
研究的目的:
- 使用基于机器学习的方法预测无氧消化过程中的废水SCOD.
- 评估不同机器学习算法的SCOD预测的有效性.
- 为可靠的早期水质预测提出一个综合建模方法.
主要方法:
- 利用各种机器学习算法:线性回归,决策树,随机森林和人工神经网络 (ANN).
- 使用无氧废水处理系统的现场数据进行训练和测试的模型.
- 将预测的SCOD值与实验数据进行比较,以评估模型性能.
主要成果:
- 人工神经网络在预测废水SCOD方面表现出最高的准确性.
- 据报道,ANN的平均绝对百分比误差为10.63%,R2得分为0.96.
- 该研究确定ANNs是该应用程序最有效的模拟技术.
结论:
- 机器学习,特别是ANN,提供了一种高效可靠的方法来预测废水在AD中的SCOD.
- 开发的综合建模方法可以在废水处理中预测早期的水质.
- 这项研究有助于改善废水处理厂的过程监测,控制和成本效益.
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