Yuchuan Fan, Jie Zhuang, Michael Essington, Xi Zhang, Guanghui Hua, Jehangir Bhadha, Shaopan Xia, Xuanyu Lu, Jaehoon Lee
{"title":"Characterizing the role of hydraulic retention time on nitrate removal indices in denitrifying bioreactors by nonlinear models","authors":"Yuchuan Fan, Jie Zhuang, Michael Essington, Xi Zhang, Guanghui Hua, Jehangir Bhadha, Shaopan Xia, Xuanyu Lu, Jaehoon Lee","doi":"10.1016/j.eti.2023.103431","DOIUrl":null,"url":null,"abstract":"Denitrifying bioreactors (DNBRs) are a sustainable and cost-effective practice commonly used at the edge of fields to reduce nitrate from agricultural runoff. The hydraulic retention time (HRT) is a crucial variable that affects nitrate removal rate (NRR, g N m-3 d-1), nitrate removal efficiency (NRE, %), and nitrate concentration reduction per length (Nrd, mg N L-1 m-1). In this study, two nonlinear models, the developed Michaelis-Menten (MM) model and the Mitscherlich (MT) model, were developed to characterize the relationship between nitrate removal indices (NRR, NRE, and Nrd) and HRT. This study first utilizes nonlinear models to quantitatively understand the relationship between NRR, NRE, Nrd, and HRT. To verify the models, eight experiments were conducted under different conditions, including different scales (laboratory and field), media (woodchip, woodchip+biochar, woodchip+silage leachate, woodchip+biochar+silage leachate), and influent nitrate concentrations (6.8-70 mg N L-1). The results showed that the MT model outperformed the MM model and MT could accurately characterize the nitrate removal changes with HRT and provide the optimal HRT (HRTO). Overall, the model could be beneficial for designers and practitioners to optimize nitrate removal.","PeriodicalId":11899,"journal":{"name":"Environmental Technology and Innovation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Technology and Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.eti.2023.103431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Denitrifying bioreactors (DNBRs) are a sustainable and cost-effective practice commonly used at the edge of fields to reduce nitrate from agricultural runoff. The hydraulic retention time (HRT) is a crucial variable that affects nitrate removal rate (NRR, g N m-3 d-1), nitrate removal efficiency (NRE, %), and nitrate concentration reduction per length (Nrd, mg N L-1 m-1). In this study, two nonlinear models, the developed Michaelis-Menten (MM) model and the Mitscherlich (MT) model, were developed to characterize the relationship between nitrate removal indices (NRR, NRE, and Nrd) and HRT. This study first utilizes nonlinear models to quantitatively understand the relationship between NRR, NRE, Nrd, and HRT. To verify the models, eight experiments were conducted under different conditions, including different scales (laboratory and field), media (woodchip, woodchip+biochar, woodchip+silage leachate, woodchip+biochar+silage leachate), and influent nitrate concentrations (6.8-70 mg N L-1). The results showed that the MT model outperformed the MM model and MT could accurately characterize the nitrate removal changes with HRT and provide the optimal HRT (HRTO). Overall, the model could be beneficial for designers and practitioners to optimize nitrate removal.
反硝化生物反应器(dnbr)是一种可持续的、具有成本效益的做法,通常用于农田边缘,以减少农业径流中的硝酸盐。水力停留时间(HRT)是影响硝酸盐去除率(NRR, g N m-3 d-1)、硝酸盐去除率(NRE, %)和每长度硝酸盐还原浓度(Nrd, mg N L-1 m-1)的关键变量。本文建立了两种非线性模型,分别为Michaelis-Menten (MM)模型和Mitscherlich (MT)模型,用于表征硝酸盐去除指标(NRR、NRE和Nrd)与HRT之间的关系。本研究首先利用非线性模型定量理解了NRR、NRE、Nrd和HRT之间的关系。为了验证模型,在不同规模(实验室和现场)、不同介质(木片、木片+生物炭、木片+青贮渗滤液、木片+生物炭+青贮渗滤液)和进水硝酸盐浓度(6.8 ~ 70 mg N L-1)下进行了8项实验。结果表明,MT模型优于MM模型,MT能准确表征HRT对硝酸盐去除的影响,并提供最佳HRT (HRTO)。综上所述,该模型可为设计师和实践者优化硝酸盐去除提供参考。