Ryan W. Holloway
Colorado School of Mines
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Featured researches published by Ryan W. Holloway.
Environmental Science & Technology | 2014
Ryan W. Holloway; Julia Regnery; Long D. Nghiem; Tzahi Y. Cath
A hybrid ultrafiltration-osmotic membrane bioreactor (UFO-MBR) was investigated for over 35 days for nutrient and trace organic chemical (TOrC) removal from municipal wastewater. The UFO-MBR system uses both ultrafiltration (UF) and forward osmosis (FO) membranes in parallel to simultaneously extract clean water from an activated sludge reactor for nonpotable (or environmental discharge) and potable reuse, respectively. In the FO stream, water is drawn by osmosis from activated sludge through an FO membrane into a draw solution (DS), which becomes diluted during the process. A reverse osmosis (RO) system is then used to reconcentrate the diluted DS and produce clean water suitable for direct potable reuse. The UF membrane extracts water, dissolved salts, and some nutrients from the system to prevent their accumulation in the activated sludge of the osmotic MBR. The UF permeate can be used for nonpotable reuse purposes (e.g., irrigation and toilet flushing). Results from UFO-MBR investigation illustrated that the chemical oxygen demand, total nitrogen, and total phosphorus removals were greater than 99%, 82%, and 99%, respectively. Twenty TOrCs were detected in the municipal wastewater that was used as feed to the UFO-MBR system. Among these 20 TOrCs, 15 were removed by the hybrid UFO-MBR system to below the detection limit. High FO membrane rejection was observed for all ionic and nonionic hydrophilic TOrCs and lower rejection was observed for nonionic hydrophobic TOrCs. With the exceptions of bisphenol A and DEET, all TOrCs that were detected in the DS were well rejected by the RO membrane. Overall, the UFO-MBR can operate sustainably and has the potential to be utilized for direct potable reuse applications.
Environmental Science: Water Research & Technology | 2015
Ryan W. Holloway; Andrea Achilli; Tzahi Y. Cath
The osmotic membrane bioreactor (OMBR) is a hybrid biological-physical treatment process that has been gaining interest for wastewater treatment and water reuse. The OMBR couples semi-permeable forward osmosis (FO) membranes for physiochemical separation with biological activated sludge process for organic matter and nutrient removal. The driving force for water production in OMBR is the osmotic pressure difference across the FO membrane between the activated sludge and a concentrated draw solution, which is made with inorganic or organic salts that have a high osmotic pressure at relatively low concentrations. The draw solution becomes diluted during OMBR treatment and may be reconcentrated using reverse osmosis, membrane distillation, or thermal distillation processes. The combination of processes in the OMBR presents unique opportunities but also challenges that must be addressed in order to achieve successful commercialization. These challenges include membrane fouling, elevated bioreactor salinity that hinders process performance, degradation of the draw solution by chemicals that diffuse through the FO membrane, and the potential for simultaneous water, mineral, and nutrient recovery. In this article, results from past and most recent OMBR studies are summarized and critically reviewed. Information about similar and more established technologies (e.g., traditional porous membrane bioreactors and FO) is included to help compare and contrast state-of-the-art technologies and the novel OMBR approach, and to elucidate practical configurations that should be considered in future OMBR research and development.
Environmental Science & Technology | 2016
D. Vuono; Julia Regnery; Dong Li; Zackary L. Jones; Ryan W. Holloway; Jörg E. Drewes
The role of abundant and rare taxa in modulating the performance of wastewater-treatment systems is a critical component of making better predictions for enhanced functions such as micropollutant biotransformation. In this study, we compared 16S rRNA genes (rDNA) and rRNA gene expression of taxa in an activated-sludge-treatment plant (sequencing batch membrane bioreactor) at two solids retention times (SRTs): 20 and 5 days. These two SRTs were used to influence the rates of micropollutant biotransformation and nutrient removal. Our results show that rare taxa (<1%) have disproportionally high ratios of rRNA to rDNA, an indication of higher protein synthesis, compared to abundant taxa (≥1%) and suggests that rare taxa likely play an unrecognized role in bioreactor performance. There were also significant differences in community-wide rRNA expression signatures at 20-day SRT: anaerobic-oxic-anoxic periods were the primary driver of rRNA similarity. These results indicate differential expression of rRNA at high SRTs, which may further explain why high SRTs promote higher rates of micropollutant biotransformation. An analysis of micropollutant-associated degradation genes via metagenomics and direct measurements of a suite of micropollutants and nutrients further corroborates the loss of enhanced functions at 5-day SRT operation. This work advances our knowledge of the underlying ecosystem properties and dynamics of abundant and rare organisms associated with enhanced functions in engineered systems.
Stochastic Environmental Research and Risk Assessment | 2016
Karen Kazor; Ryan W. Holloway; Tzahi Y. Cath; Amanda S. Hering
Multivariate statistical methods for online process monitoring have been widely applied to chemical, biological, and engineered systems. While methods based on principal component analysis (PCA) are popular, more recently kernel PCA (KPCA) and locally linear embedding (LLE) have been utilized to better model nonlinear process data. Additionally, various forms of dynamic and adaptive monitoring schemes have been proposed to address time-varying features in these processes. In this analysis, we extend a common simulation study in order to account for autocorrelation and nonstationarity in process data and comprehensively compare the monitoring performances of static, dynamic, adaptive, and adaptive–dynamic versions of PCA, KPCA, and LLE. Furthermore, we evaluate a nonparametric method to set thresholds for monitoring statistics and compare results with the standard parametric approaches. We then apply these methods to real-world data collected from a decentralized wastewater treatment system during normal and abnormal operations. From the simulation study, adaptive–dynamic versions of all three methods generally improve results when the process is autocorrelated and nonstationary. In the case study, adaptive–dynamic versions of PCA, KPCA, and LLE all flag a strong system fault, but nonparametric thresholds considerably reduce the number of false alarms for all three methods under normal operating conditions.
Water Research | 2007
Ryan W. Holloway; Amy E. Childress; Keith E. Dennett; Tzahi Y. Cath
Desalination | 2015
Ryan W. Holloway; Andrew Wait; Aline Fernandes da Silva; Jack Herron; Mark Schutter; Keith Lampi; Tzahi Y. Cath
Journal of Membrane Science | 2016
Ryan W. Holloway; Leslie Miller-Robbie; Mehul Patel; Jennifer R. Stokes; Junko Munakata-Marr; Jason Dadakis; Tzahi Y. Cath
Journal of Membrane Science | 2015
Ryan W. Holloway; Rudy Maltos; Johan Vanneste; Tzahi Y. Cath
Journal of Membrane Science | 2016
Elizabeth A. Bell; Ryan W. Holloway; Tzahi Y. Cath
Archive | 2014
Pravin S. Murkute; Tzahi Y. Cath; Ryan W. Holloway; John R. Herron; Keith Lampi; Andrew Wait; Walter L. Schultz