...
首页> 外文期刊>Clean technologies and environmental policy >Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives
【24h】

Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives

机译:机床学习算法对生物污水处理的应用:更新和观点

获取原文
获取原文并翻译 | 示例
           

摘要

Biological wastewater treatment using algae-bacteria consortia for nutrient uptake and resource recovery is a 'paradigm shift' from the mainstream wastewater treatment process to mitigate pollution and promote circular economy. The symbiotic relationship between algae and bacteria is complex in open or closed biological wastewater treatment systems. In this regard, machine learning algorithms (MLAs) have found to be advantageous to predict the uncertain performances of the treatment processes. MLAs have shown satisfactory results for effective real-time monitoring, optimization, prediction of uncertainties and fault detection of complex environmental systems. By incorporating these algorithms with online sensors, the transient operating conditions during the treatment process including disruptions or failures due to leaking pipelines, malfunctioning of bioreactors, unexpected fluctuations of organic loadings, flow rate, and temperature can be forecasted efficiently. This paper reviews the state-of-the-art MLA approaches for the integrated operation of biological wastewater treatment systems combining algal biomass production and nutrient recovery from municipal wastewater.
机译:使用藻类 - 细菌的生物废水治疗营养吸收和资源回收是一种“范式转变”,来自主流废水处理过程,减轻污染和促进循环经济。藻类和细菌之间的共生关系是开放或封闭的生物废水处理系统中的复合物。在这方面,已经发现机器学习算法(MLAS)是有利的,以预测治疗过程的不确定性能。 MLA表明了有效的实时监测,优化,对复杂环境系统的故障检测的有效实时监测,优化和故障检测的令人满意的结果。通过将这些算法与在线传感器结合,在处理过程中的瞬态操作条件包括泄漏管道引起的中断或失败,生物反应器的发生故障,有机载荷的意外波动,流速和温度可以有效地预测。本文综述了最先进的MLA方法,用于将藻类生物量产生和城市废水中的藻类生物量产生和养分回收结合的生物废水处理系统的综合运行方法。

著录项

  • 来源
    《Clean technologies and environmental policy》 |2021年第1期|127-143|共17页
  • 作者单位

    Graduate Institute of Environmental Engineering National Taiwan University Taipei 10617 Taiwan People's Republic of China;

    Department of Environmental Engineering and Water Technology IHE Delft Institute for Water Education PO Box 3015 2601 DA Delft The Netherlands;

    Department of Environmental Engineering and Water Technology IHE Delft Institute for Water Education PO Box 3015 2601 DA Delft The Netherlands;

    Department of Environmental Engineering and Water Technology IHE Delft Institute for Water Education PO Box 3015 2601 DA Delft The Netherlands;

    Department of Environmental Engineering and Water Technology IHE Delft Institute for Water Education PO Box 3015 2601 DA Delft The Netherlands;

    Department of Information and Computer Science School of Engineering and Applied Sciences National University of Mongolia Building #3 Baga toiruu-2 P.O. Box 46A/600 Sukhbaatar Ulaanbaatar 14200 Mongolia;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Algae-bacteria consortia; Machine learning algorithm; Biological wastewater treatment; Process optimization; Bioprocess modelling;

    机译:藻类细菌组成;机器学习算法;生物废水处理;过程优化;生物过程建模;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号