基于机器学习的模型构建和确定同时硫化物和酸盐去除过程的主导因素
Hong Gao1, Bilong Chen1, Mahmood Qaisar2
1College of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, China.
Bioresource technology
|October 13, 2023
概括
逆向传播神经网络 (BPNN) 为同步硫化物和酸盐去除 (SSNR) 过程提供了卓越的水质预测. 这种人工智能方法准确预测废水质量,并确定优化废水处理的关键因素.
科学领域:
- 环境工程 环境工程
- 生物技术是生物技术.
- 人工智能在水处理中的应用
背景情况:
- 准确的水质预测对于有效的同步硫化物和酸盐去除 (SSNR) 来说至关重要.
- 传统的模型在废水处理中与复杂的微生物通路作斗争.
- 反向传播神经网络 (BPNN) 显示出模拟复杂生物过程的前景.
研究的目的:
- 为SSNR过程开发和评估一个通用的BPNN模型.
- 预测关键的水质参数,包括硫化物和酸盐的去除,以及副产品的形成.
- 确定影响SSNR流程性能的关键因素.
主要方法:
- 开发一个通用的反向传播神经网络 (BPNN) 模型.
- 模拟和预测硫化物去除,酸盐去除,元素硫生产和气生产.
- 与传统数学模型进行比较分析.
主要成果:
- BPNN模型在SSNR中表现出了对废水质量的强有力的预测性能.
- BPNN显著优于传统的回归和ANOVA模型.
- 确定了影响流程优化和控制的关键因素.
结论:
- BPNN是模拟和预测SSNR等废水处理过程的卓越工具.
- 人工智能集成提高了满足水质标准的效率.
- 开发的模型为SSNR流程优化提供了一个强大的框架.
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