Chys Michael, Audenaert Wim T M, Vangrinsven Jan, Bauwens Michael, Mortier Séverine T F C, Van Langenhove Herman, Nopens Ingmar, Demeestere Kristof, Van Hulle Stijn W H
LIWET, Department of Industrial Biological Sciences, Ghent University Campus Kortrijk, Graaf Karel de Goedelaan 5, B-8500, Kortrijk, Belgium.
LIWET, Department of Industrial Biological Sciences, Ghent University Campus Kortrijk, Graaf Karel de Goedelaan 5, B-8500, Kortrijk, Belgium; BIOMATH, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000, Gent, Belgium.
Chemosphere. 2018 Apr;196:494-501. doi: 10.1016/j.chemosphere.2017.12.168. Epub 2017 Dec 27.
New robust correlation models for ozonation, based on UVA and fluorescence surrogate parameters and developed considering kinetic information, have been applied at pilot-scale. This model framework is validated with the aim for operators to control the ozone dose for the removal of trace organic contaminants (TrOCs) in effluents from full-scale municipal wastewater treatment plants. The inflected correlation model between ΔTrOCs and the surrogates predicts the removal of TrOCs (based on statistical evidence) solely using the 2nd order reaction rate constant with ozone (k) and in a more adequate manner than similar single correlation models. This allows the use of this new model for current and future TrOCs under investigation which is highly interesting when imposed discharge limits might include more and other TrOCs in future. The use of UVA might be preferable at the current timing for online monitoring of TrOC abatement as the model showed a good predictive power (based on statistical evidence and visual confirmation). Reliable online sensors are more widespread (and commercially) available compared to fluorescence sensors which are still under development, with the exception of a few examples. Nevertheless, the data processing of the fluorescence signals, isolating the different intensities associated with moieties reacting similarly to ozone might even increase the predictive power, given the lower degree of interference (i.e. less scattering).
基于紫外线（UVA）和荧光替代参数、并结合动力学信息开发的新型臭氧氧化稳健相关模型，已在中试规模上得到应用。该模型框架经过验证，旨在帮助操作人员控制臭氧剂量，以去除城市污水处理厂实际规模排放废水中的痕量有机污染物（TrOCs）。ΔTrOCs与替代参数之间的拐点相关模型（基于统计证据）仅使用与臭氧的二级反应速率常数（k）就能更准确地预测TrOCs的去除情况，比类似的单一相关模型更为适用。这使得该新模型可用于当前及未来正在研究的TrOCs，在未来排放限制可能包括更多及其他TrOCs的情况下，这一点非常重要。由于该模型显示出良好的预测能力（基于统计证据和直观确认），在当前阶段，使用UVA进行TrOC去除的在线监测可能更为可取。与仍在开发中的荧光传感器相比，可靠的在线传感器更为普遍（且有商业供应），少数情况除外。然而，考虑到干扰程度较低（即散射较少），对荧光信号进行数据处理，分离与类似臭氧反应部分相关的不同强度，甚至可能提高预测能力。
