Evaluation of the Performance of a Revolving Algae Biofilm System for Recovering Nitrogen and Phosphorus from Municipal Wastewater

Kunetz, Thomas E.; Kumar, Kuldip; Gross, Martin; Wen, Zhiyou

doi:10.2175/193864716819713088

Cited by 2 publications

(2 citation statements)

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“…The MWRDGC has a strong history of conducting research and development activities and partnering with university‐based researchers to investigate the many opportunities for discovery presented by its facilities (Kunetz, Kumar, Gross, & Wen, ). The installation of H 2 S sensors at the TCR prompted MWRDGC researchers to consider how recent innovations in data science might be applied to the large amount of data being logged.…”

Section: Introductionmentioning

confidence: 99%

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach

Mulrow

Kshetry²,

Brose

et al. 2019

Water Environment Research

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Odorous compound emissions and odor complaints from the public are rising concerns for agricultural, industrial, and water resource recovery facilities (WRRFs) near urban areas. Many facilities are deploying sensors that measure malodorous compounds and other factors related to odor creation and dispersion. Focusing on the Metropolitan Water Reclamation District of Greater Chicago's (MWRDGCs) Thornton Composite Reservoir (7.9 billion gallon capacity), we used meteorological, operational, and H2S sensor data to train a 3‐day advance‐warning predictor of local odor complaints, so as to implement targeted odor prevention measures. Using a machine learning approach, we bypassed difficulties in modeling both physical dispersion and human perception of odors. Utilizing random forest algorithms with varied settings and input attributes, we find that a small network of H2S sensors, meteorological data, and operational data are able to predict odor complaints three days in advance with greater than 60% accuracy and less than 25% false‐positive rates, exceeding MWRDGC’s standards required for full‐scale deployment. Practitioner points A random forest algorithm trained on H2S, weather, and operations data successfully predicted odor complaints surrounding a large composite reservoir. Thirty‐two data attribute combinations were tested. It was found that H2S sensor data alone are insufficient for predicting odor complaints. The best predictor was a Random Forest Classifier trained on weather, operational, and H2S readings from the reservoir corner locations. This study demonstrates odor complaint prediction capability utilizing a limited set of data sources and open‐source machine learning techniques. Given a small network of H2S sensors and organized data management, WRRFs and similar facilities can conduct advance‐warning odor complaint prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach

Mulrow

Kshetry²,

Brose

et al. 2019

Water Environment Research

Self Cite

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“…Also the biomass can be harvested through scraping from the attachment material, which greatly reduced the cost compared to the centrifugation harvested processes (Gross et al, 2016). Recently, the RAB system has been successfully implemented in Metropolitan Water Reclamation District (MWRD) of Greater Chicago to remove nitrogen, phosphorus and metals from sludge thickening supernatant in MWRD facility (Kunetz et al, 2016, Zhao et al, 2018.…”

Section: Introductionmentioning

confidence: 99%

Treatment of acidic sulfate-containing wastewater using revolving algae biofilm reactors: Sulfur removal performance and microbial community characterization

Zhou

Sheng

Zhao

et al. 2018

Bioresource Technology

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Industries such as mining operations are facing challenges of treating sulfur-containing wastewater such as acid mine drainage (AMD) generated in their plant. The aim of this work is to evaluate the use of a revolving algal biofilm (RAB) reactor to treat AMD with low pH (3.5-4) and high sulfate content (1-4 g/L). The RAB reactors resulted in sulfate removal efficiency up to 46% and removal rate up to 0.56 g/L-day, much higher than those obtained in suspension algal culture. The high-throughput sequencing revealed that the RAB reactor contained diverse cyanobacteria, green algae, diatoms, and acid reducing bacteria that contribute the sulfate removal through various mechanisms. The RAB reactors also showed a superior performance of COD, ammonia and phosphorus removal. Collectively, the study demonstrated that RAB-based process is an effective method to remove sulfate in wastewater with small footprint and can be potentially installed in municipal or industrial wastewater treatment facilities.

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Evaluation of the Performance of a Revolving Algae Biofilm System for Recovering Nitrogen and Phosphorus from Municipal Wastewater

Cited by 2 publications

References 0 publications

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach

Treatment of acidic sulfate-containing wastewater using revolving algae biofilm reactors: Sulfur removal performance and microbial community characterization

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