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Statistical odor prediction models for supporting biosolids odor management.

机译:统计气味预测模型可支持生物固体气味管理。

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摘要

Biosolids are being beneficially recycled for agricultural purpose. Often, however, biosolids odors diminish marketability of biosolids, bring community opposition or, in the worst case, cause to ban the biosolids land application program. This dissertation is aiming to develop practical biosolids odor prediction models that can be applied for biosolids management on daily basis using the existing data available at the wastewater treatment plant and at the application sites as explanatory variables. Therefore, biosolids producer can use the plant odor predicting models to early detect and notify the hauling contractor when malodorous biosolids are anticipated. With the field odor models, malodorous products can be allocated accordingly to the appropriate sites in preventing the odor complaints from the communities.;First, biosolids odors prediction models at wastewater treatment plant were developed using linear regression analysis and categorical data analysis. Biosolids odor was predicted in terms of detection threshold (DT) concentration and class of biosolids odor (odorous or non-odorous). Variables influencing biosolids odor levels at the plant were the percent solids and temperature of biosolids, percentage of the gravity thickener solids (GT) in the blend tank, pH of the GT solids, concentration of the return activated sludge (RAS) at the secondary process, and number of centrifuges running.;Second, simulation and sensitivity analysis were conducted on the selected biosolids odor prediction model when uncertainty in the input variables was considered. Two variables (i.e., the number of centrifuges running and the percentage of GT solids in the blend tank) were identified as decision variable that could reduce the probability of producing odorous biosolids.;Last, a biosolids odors prediction model for use at field site was developed using ordered logit model. Various variables at the field site (i.e. weather conditions, odor measurement time of the day, wind condition, temperature, and inspector odor sensitivity) were included in the analysis. Finally, variables relating to field odor levels were the biosolids odor levels (detection threshold) at the plant, temperature at the reuse site, and wind conditions.
机译:生物固体被有利地回收用于农业目的。但是,生物固体气味常常会降低生物固体的可销售性,引起社区反对,或者在最坏的情况下,会导致禁止生物固体土地应用程序。本文旨在开发实用的生物固体气味预测模型,该模型可以利用废水处理厂和应用现场的现有数据作为解释变量,每天应用于生物固体管理。因此,当预计会产生恶臭的生物固体时,生物固体生产商可以使用植物气味预测模型来早期检测并通知运输承包商。通过田间气味模型,可以将恶臭产品分配到适当的位置,以防止社区产生异味。首先,使用线性回归分析和分类数据分析开发了废水处理厂的生物固体异味预测模型。根据检测阈值(DT)浓度和生物固体气味类别(有气味或无气味)预测了生物固体气味。影响工厂中生物固体气味水平的变量包括固体百分比和生物固体温度,混合罐中重力增稠剂固体(GT)的百分比,GT固体的pH值,二级过程中返回的活性污泥(RAS)的浓度其次,在考虑输入变量不确定性的情况下,对选定的生物固体气味预测模型进行了模拟和敏感性分析。确定了两个变量(即,离心机的运行数量和混合罐中GT固体的百分比)作为决策变量,可以减少产生有气味的生物固体的可能性。最后,在现场使用的生物固体气味预测模型是使用有序logit模型开发。分析中包括了现场的各种变量(即天气状况,一天中的气味测量时间,风况,温度和检查员的气味敏感性)。最后,与田间气味水平有关的变量是工厂的生物固体气味水平(检测阈值),再利用场所的温度和风况。

著录项

  • 作者

    Vilalai, Sirapong.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Engineering Civil.;Engineering Sanitary and Municipal.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 199 p.
  • 总页数 199
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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