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水中絮体形态原位识别技术研究
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摘要
絮凝是水处理的重要操作单元之一,它反映脱稳后的胶体颗粒互相碰撞后,粘在一起形成永久性凝聚体的过程。絮凝过程进展的好坏将直接影响到水处理后续流程的工况,因而成为环境工程中重要的科技研究开发领域。关于絮凝的理论基础国外研究比较多,在以往的研究中,大多是把絮凝体系当作一个“黑箱”,只管混絮凝剂的投入和所产生絮凝效果的输出,即使考虑微观过程,也只是将所有的胶粒抽象为球形,这与实际所观察到的胶体和絮凝体的现象有较大的差别。尽管有的学者在理论推导和形成最终的数学表达式时引用了颗粒系数加以修正,但理论与试验结果仍难以一致。
     本课题应用硫酸铝絮凝剂和全铁微生物絮凝剂,以水中高岭土颗粒为去除对象,采用在线光学影像设备,在确定试验的最佳水力条件基础上对铝盐絮体和全铁絮体的动态絮凝形态变化过程进行了原位在线检测和分析。并设计了试验,分别对硫酸铝和全铁微生物絮凝剂,在变化投药量的基础上,通过对絮凝过程中的絮体进行大量在线图像拍摄,分析了絮凝过程中絮体分形维数值的变化规律。试验结果表明:在本试验的条件下,絮体的分形维数都在1.1~1.7之间,且数据点的相关性很好,说明絮体的形成具有“分形”特征。与沉后水浊度、沉后水颗粒总数联合分析表明:沉后水浊度会受到干扰颗粒的影响;而沉后水颗粒数则能很好的评价絮凝效果,原位在线检测出的絮体分形维数与沉后水颗粒数保持了较高的相关性。因而,该种原位检测结果可以较准确的反应水中絮体的形态特征,并作为反映絮凝效果的好坏的有效评价指标。
     同时对比分析静态影像法和原位动态检测试验法:静态法所采集的絮体代表性差,絮体破碎、重叠严重,导致获得的絮体形态特征与真实值相差甚远;而原位动态检测试验对于絮凝过程的再现性强,能真实地反映絮体絮凝形态的变化规律。首次提出利用最优絮体影像阀值进行絮体形态原位识别,并通过试验证明最优絮体影像阀值的存在。研究表明:对于每一张拍摄的絮体图像,总存在着这样一个阀值,由它计算出的絮体的面积和周长,并最终得出的分形维数值能够最准确地反映絮凝形态的变化规律,我们定义这个阀值为最优絮体影像阀值。
Flocculation process is one of the major operating units of wastewater treatment, it reflects the process of destabilized colloid collisions each other, stick together forming a permanent condensate. Evidently, flocculation process will have a direct influence on the treatment result of the succeeding technology units, so it becomes the key point of environmental research field. The theoretical basis about the flocculation researches rather more in foreign countries. But in the previous studies, most of the coagulation system were deemed to be a "black box", only considering the input of flocculant and the output flocculation effect. Even taking into account of micro-process, the people all abstracted the colloid in the water as the sphericity, this is quite different from the phenomenon of the colloid and flocculation that actually observed .Although some scholars modified the result by the grain coefficient when forming the theory and the final mathematical expression, but the theoretical and experimental results are still not consistent.
     In this paper, taking aluminum sulfate and microbial quantie as flocculant and particles of gaolintu in the water as the wipe off target, makes an in-situ online test and analysis on changing process of the dynamic flocculation form of aluminum salt and quantie by on-line TV-microscope base on the decided water condition. Through many experiments, we obtained the fractal dimension of aluminum sulfate and quantie on different dosage. The result shows: the fractal dimensions all distribute during 1.1-1.7 on different condition, and R2 of the data are bigger than 0.85. this suggested flocs are in accord with fractal character. Associating with the turbidity and sum of particles of settled water, we found: turbidity of settled water is inference by interference flocs; particles of settled water can make an effective and accurate evaluation of the flocculation effect. The fractal dimension detected by in-situ online test and sum of particles of settled water maintain a relatively high number of correlation. Thus, the result of this in-situ online test can response to the water floc morphology accurately, and can be a effective evaluation of the performance indicators to reflect the flocculation effect.
     Meanwhile, we apply the static and in-situ dynamic experiments to make a comparative analysis. The research shows that: The flocs picked in the static experiments is poorly represented, broken and overlapped very badly, all the reason above lead the floc morphological characteristics far from true fact; The in-situ dynamic experiments strongly reproduce the flocculation process and can truly reflect the floc morphological characteristics changes. In this thesis, using the optimal floc image threshold to make an identification of floc morphology is put forward for the first time, and the experiments also prove the existence of it. Research shows that: for each taken floc image, there is such a threshold, in which calculate the floc perimeter and area eventually that come to the fractal dimension values can be the most accurate reflection of morphological characteristics changes in laws, we defined the threshold for the optimal folc image threshold.
引文
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