Politecnico di Milano, DICA - Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci, 32, 20133, Milan, Italy.
Waterschap de Dommel, Postbus 10.001, Boxtel, NL-5280, DA, the Netherlands.
J Environ Manage. 2020 May 1;261:110219. doi: 10.1016/j.jenvman.2020.110219. Epub 2020 Mar 2.
Emission of NO represents an increasing concern in wastewater treatment, in particular for its large contribution to the plant's carbon footprint (CFP). In view of the potential introduction of more stringent regulations regarding wastewater treatment plants' CFP, there is a growing need for advanced monitoring with online implementation of mitigation strategies for NO emissions. Mechanistic kinetic modelling in full-scale applications, are often represented by a very detailed representation of the biological mechanisms resulting in an elevated uncertainty on the many parameters used while limited by a poor representation of hydrodynamics. This is particularly true for current NO kinetic models. In this paper, a possible full-scale implementation of a data mining approach linking plant-specific dynamics to NO production is proposed. A data mining approach was tested on full-scale data along with different clustering techniques to identify process criticalities. The algorithm was designed to provide an applicable solution for full-scale plants' control logics aimed at online NO emission mitigation. Results show the ability of the algorithm to isolate specific NO emission pathways, and highlight possible solutions towards emission control.
NO 的排放是废水处理中日益受到关注的问题，特别是因为其对工厂碳足迹（CFP）的大量贡献。鉴于可能会对废水处理厂的 CFP 引入更严格的法规，因此需要进行更先进的监测，并在线实施减少 NO 排放的策略。在全规模应用中，基于机制的动力学建模通常通过对生物机制的非常详细表示来表示，这导致在使用许多参数时存在很高的不确定性，而这些参数受到水动力学表示不佳的限制。当前的 NO 动力学模型尤其如此。在本文中，提出了一种将特定于工厂的动力学与 NO 生成联系起来的数据挖掘方法的全规模实施的可能性。针对全规模数据以及不同的聚类技术测试了一种数据挖掘方法，以识别过程的关键因素。该算法旨在为旨在在线减少 NO 排放的全规模工厂控制逻辑提供一种可行的解决方案。结果表明，该算法能够隔离特定的 NO 排放途径，并突出可能的排放控制解决方案。
