Prediction of enhanced coagulation with varied pre-oxidations for seasonal variations of cyanobacteria-dominated algae-laden water quality

IF 8 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Luming Ding , Zhiwei Zhou , Yanling Yang , Yuankun Liu , Fei Han , Wenqing Yu , Kaidi Xin , Chunqing Liu , Xing Li , Jiawei Ren
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引用次数: 0

Abstract

Enhanced coagulation with pre-oxidation is a cost-effective approach for managing seasonal water quality pollution caused by algal outbreaks and deaths. The synergistic application of pre-oxidants and coagulants, coupled with intelligent and precise dosage control, constitutes a prominent research focus in water treatment field. This study evaluates the removal of various pre-oxidants, including potassium permanganate (KMnO4), KMnO4 composites (PPC), and potassium ferrate (K2FeO4), in combination with coagulants like polyaluminum chloride (PACl) and aluminum sulfate (Al2(SO4)3). A machine learning algorithm, based on the least squares support vector machine (LSSVM), was developed to predict optimal dosages. After 15 months of source water quality monitoring, algal contaminations predominantly driven by cyanobacteria-dominated became worse particularly in the high temperature and algae period and autumn, which were positively correlated with UV254 and CODMn (p < 0.05). The doses of PACl and Al2(SO4)3 were between 100 μM and 120 μM (calculated as Al) across various periods to efficiently remove organic matter. Under optimal chemical dosages, pre-oxidation facilitated the protein-like substances removal. The removal efficiency of PPC surpassed that of KMnO4 and K2FeO4. The LSSVM model demonstrated superior predictive performance for dosages compared to other models like random forest (RF) and back propagation (BP) neural networks, with feature importance analysis identifying water temperature, UV254, and conductivity as the core parameters for real-time dosing systems. This study elucidated dosing strategies alongside algae contaminations removal associated with pre-oxidation enhanced coagulation while proposing a methodology for dynamically adjusting oxidant and coagulant dosages through real-time monitoring of both raw water quality and effluent from coagulation-precipitation processes, thereby providing novel insights into precise real-time dosing for chemicals in water treatment facilities.

Abstract Image

预测增强混凝与不同的预氧化对蓝藻为主的藻类满载水质的季节性变化
加强混凝与预氧化是一种具有成本效益的方法,用于管理季节性水质污染造成的藻类爆发和死亡。预氧化剂与混凝剂的协同应用,以及智能精准的投加控制,是目前水处理领域的一个突出研究热点。本研究评估了与聚氯化铝(PACl)和硫酸铝(Al2(SO4)3)等混凝剂联合去除各种预氧化剂,包括高锰酸钾(KMnO4)、KMnO4复合材料(PPC)和高铁酸钾(K2FeO4)。提出了一种基于最小二乘支持向量机(LSSVM)的机器学习算法来预测最佳剂量。水源水质监测15个月后,以蓝藻为主的藻类污染加剧,特别是在高温藻类期和秋季,与UV254和CODMn呈正相关(p <;0.05)。PACl和Al2(SO4)3的剂量在100 μM ~ 120 μM(以Al计算)之间,可有效去除有机物。在最佳化学剂量下,预氧化促进了蛋白质样物质的去除。PPC的去除率高于KMnO4和K2FeO4。与随机森林(RF)和反向传播(BP)神经网络等其他模型相比,LSSVM模型对剂量的预测性能优越,特征重要性分析确定水温、UV254和电导率是实时给药系统的核心参数。本研究阐明了与预氧化强化混凝相关的藻类污染去除的剂量策略,同时提出了一种通过实时监测原水水质和混凝沉淀过程出水来动态调整氧化剂和混凝剂剂量的方法,从而为水处理设施中化学品的精确实时剂量提供了新的见解。
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来源期刊
Journal of Environmental Management
Journal of Environmental Management 环境科学-环境科学
CiteScore
13.70
自引率
5.70%
发文量
2477
审稿时长
84 days
期刊介绍: The Journal of Environmental Management is a journal for the publication of peer reviewed, original research for all aspects of management and the managed use of the environment, both natural and man-made.Critical review articles are also welcome; submission of these is strongly encouraged.
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