Modeling light and temperature influence on ammonium removal by Scenedesmus sp. under outdoor conditions

[EN] The ammonium removal rate of the microalga Scenedesmus sp. was studied under outdoor conditions. Microalgae were grown in a 500 L flat-plate photobioreactor and fed with the effluent of a submerged anaerobic membrane bioreactor. Temperature ranged between 9.5 WC and 32.5 WC and maximum light intensity was 1,860 μmol·m2·s1. A maximum specific ammonium removal rate of 3.71 mg NH4 þ-N·g TSS1·h1 was measured (at 22.6 WC and with a light intensity of 1,734 μmol·m2·s1). A mathematical model considering the influence of ammonium concentration, light and temperature was validated. The model successfully reproduced the observed values of ammonium removal rate obtained and it is thus presented as a useful tool for plant operation.This research work has been supported by the Spanish Ministry of Education, Culture and Sports (CTM2011-28595-C02-01/02) jointly with the European Regional Development Fund (ERDF) and Generalitat Valenciana (ACOMP2013/203), which are gratefully acknowledged. This research was also supported by the Spanish Ministry of Education, Culture and Sport via a pre-doctoral FPU fellowship to the first author (AP2009-4903). The authors also gratefully acknowledge the support from the water management entities of the Generalitat Valenciana (EPSAR).Ruiz Martinez, A.; Serralta Sevilla, J.; Seco Torrecillas, A.; Ferrer, J. (2016). Modeling light and temperature influence on ammonium removal by Scenedesmus sp. under outdoor conditions. Water Science & Technology. 74(8):1964-1970. https://doi.org/10.2166/wst.2016.383S19641970748Åkerström, A. M., Mortensen, L. M., Rusten, B., & Gislerød, H. R. (2014). Biomass production and nutrient removal by Chlorella sp. as affected by sludge liquor concentration. Journal of Environmental Management, 144, 118-124. doi:10.1016/j.jenvman.2014.05.015Bernard, O., & Rémond, B. (2012). Validation of a simple model accounting for light and temperature effect on microalgal growth. Bioresource Technology, 123, 520-527. doi:10.1016/j.biortech.2012.07.022Brennan, L., & Owende, P. (2010). Biofuels from microalgae—A review of technologies for production, processing, and extractions of biofuels and co-products. Renewable and Sustainable Energy Reviews, 14(2), 557-577. doi:10.1016/j.rser.2009.10.009Broekhuizen, N., Park, J. B. K., McBride, G. B., & Craggs, R. J. (2012). Modification, calibration and verification of the IWA River Water Quality Model to simulate a pilot-scale high rate algal pond. Water Research, 46(9), 2911-2926. doi:10.1016/j.watres.2012.03.011Giménez, J. B., Robles, A., Carretero, L., Durán, F., Ruano, M. V., Gatti, M. N., … Seco, A. (2011). Experimental study of the anaerobic urban wastewater treatment in a submerged hollow-fibre membrane bioreactor at pilot scale. Bioresource Technology, 102(19), 8799-8806. doi:10.1016/j.biortech.2011.07.014Kurano, N., & Miyachi, S. (2005). Selection of microalgal growth model for describing specific growth rate-light response using extended information criterion. Journal of Bioscience and Bioengineering, 100(4), 403-408. doi:10.1263/jbb.100.403McGinn, P. J., Dickinson, K. E., Park, K. C., Whitney, C. G., MacQuarrie, S. P., Black, F. J., … O’Leary, S. J. B. (2012). Assessment of the bioenergy and bioremediation potentials of the microalga Scenedesmus sp. AMDD cultivated in municipal wastewater effluent in batch and continuous mode. Algal Research, 1(2), 155-165. doi:10.1016/j.algal.2012.05.001Reynolds, C. S. (2006). The Ecology of Phytoplankton. doi:10.1017/cbo9780511542145Rouzic, B. L., & Bertru, G. (1997). Phytoplankton community growth in enrichment bioassays: Possible role of the nutrient intracellular pools. Acta Oecologica, 18(2), 121-133. doi:10.1016/s1146-609x(97)80069-0Ruiz-Martinez, A., Serralta, J., Pachés, M., Seco, A., & Ferrer, J. (2014). Mixed microalgae culture for ammonium removal in the absence of phosphorus: Effect of phosphorus supplementation and process modeling. Process Biochemistry, 49(12), 2249-2257. doi:10.1016/j.procbio.2014.09.002Ruiz-Martínez, A., Serralta, J., Romero, I., Seco, A., & Ferrer, J. (2015). Effect of intracellular P content on phosphate removal in Scenedesmus sp. Experimental study and kinetic expression. Bioresource Technology, 175, 325-332. doi:10.1016/j.biortech.2014.10.081Ruiz-Martínez, A., Serralta, J., Seco, A., & Ferrer, J. (2015). Effect of temperature on ammonium removal in Scenedesmus sp. Bioresource Technology, 191, 346-349. doi:10.1016/j.biortech.2015.05.070Singh, G., & Thomas, P. B. (2012). Nutrient removal from membrane bioreactor permeate using microalgae and in a microalgae membrane photoreactor. Bioresource Technology, 117, 80-85. doi:10.1016/j.biortech.2012.03.125Wang, B., & Lan, C. Q. (2011). Biomass production and nitrogen and phosphorus removal by the green alga Neochloris oleoabundans in simulated wastewater and secondary municipal wastewater effluent. Bioresource Technology, 102(10), 5639-5644. doi:10.1016/j.biortech.2011.02.054Wang, L., Min, M., Li, Y., Chen, P., Chen, Y., Liu, Y., … Ruan, R. (2009). Cultivation of Green Algae Chlorella sp. in Different Wastewaters from Municipal Wastewater Treatment Plant. Applied Biochemistry and Biotechnology, 162(4), 1174-1186. doi:10.1007/s12010-009-8866-7Wu, Y.-H., Li, X., Yu, Y., Hu, H.-Y., Zhang, T.-Y., & Li, F.-M. (2013). An integrated microalgal growth model and its application to optimize the biomass production of Scenedesmus sp. LX1 in open pond under the nutrient level of domestic secondary effluent. Bioresource Technology, 144, 445-451. doi:10.1016/j.biortech.2013.06.065Wu, Y.-H., Hu, H.-Y., Yu, Y., Zhang, T.-Y., Zhu, S.-F., Zhuang, L.-L., … Lu, Y. (2014). Microalgal species for sustainable biomass/lipid production using wastewater as resource: A review. Renewable and Sustainable Energy Reviews, 33, 675-688. doi:10.1016/j.rser.2014.02.026Xin, L., Hong-ying, H., & Yu-ping, Z. (2011). Growth and lipid accumulation properties of a freshwater microalga Scenedesmus sp. under different cultivation temperature. Bioresource Technology, 102(3), 3098-3102. doi:10.1016/j.biortech.2010.10.05

PLS-based soft-sensor to predict ammonium concentration evolution in hollow fibre membrane contactors for nitrogen recovery

[EN] Hollow fibre membrane contactors (HFMC) have emerged as a promising technology for nitrogen-recovery that can be implemented in wastewater treatment plants (WWTPs) to promote circular economy. In this process, a hydrophobic membrane allows the transference of free-ammonia across the hollow fibres. During its operation, the ammonium concentration decreases, and real-time measurements would be of great value for process monitoring, optimization and control. Ammonium probes exist, but they are expensive and present noticeably maintenance costs. In this work, results from eight N-recovery experiments performed at different pH values using real supernatant of a full-scale anaerobic digester were analysed in terms of the time-evolution profiles of pH and total ammonium nitrogen (TAN). The pH revealed to carry relevant information related to the TAN concentration, as it decreased in the feed solution due to free ammonia stripping. The pH is an inexpensive-to measure process variable that can be routinely acquired in any WWTP. Therefore, a data-driven soft-sensor has been developed. It uses the pH, its derivative, and the pH increments after each reagent dosing as input signals, to estimate the TAN concentration via PLS. An extended PLS-model incorporating interaction terms, quadratic and cubic forms of the three input variables improved the TAN concentration estimation. The developed soft-sensor was able to accurately reproduce the evolution of TAN concentration (in the range 0-1000 mgNH(4)(+)-N/L with R-2 > 0.97 and RMSE < 40 mg/L) during the HFMC process operation, thus making it possible to monitor the process as well as enabling future development of different control and optimization strategies.This research was financially supported by the Spanish Ministry of Economy and Competitiveness (MINECO projects CTM2014-54980-C2-1/2-R and CTM2017-86751-C2-1/2-R) with the European Regional Development Fund (ERDF) as well as the Universitat Polite`cnica de Vale`ncia via a pre-doctoral FPI fellowship to Guillermo Noriega.Aguado García, D.; Noriega-Hevia, G.; Ferrer, J.; Seco, A.; Serralta Sevilla, J. (2022). PLS-based soft-sensor to predict ammonium concentration evolution in hollow fibre membrane contactors for nitrogen recovery. Journal of Water Process Engineering. 47:1-7. https://doi.org/10.1016/j.jwpe.2022.102735174

Effect of intracellular P content on phosphate removal in Scenedesmus sp. Experimental study and kinetic expression

The present work determines the effect of phosphorus content on phosphate uptake rate in a mixed culture of Chlorophyceae in which the genus Scenedesmus dominates. Phosphate uptake rate was determined in eighteen laboratory batch experiments, with samples taken from a progressively more P-starved culture in which a minimum P content of 0.11% (w/w) was achieved. The results obtained showed that the higher the internal biomass P content, the lower the phosphate removal rate. The highest specific phosphate removal rate was 6.5 mgPO4 P gTSS -1 h -1 . Microalgae with a P content around 1% (w/w) attained 10% of this highest removal rate, whereas those with a P content of 0.6% (w/w) presented 50% of the maximum removal rate. Different kinetic expressions were used to reproduce the experimental data. Best simulation results for the phosphate uptake process were obtained combining Steele equation and Hill function to represent the effect of light and intracellular phosphorus content, respectively.This research work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO, CTM2011-28595-C02-01/02) jointly with the European Regional Development Fund (ERDF) which are gratefully acknowledged.Ruiz Martínez, A.; Serralta Sevilla, J.; Romero Gil, I.; Seco Torrecillas, A.; Ferrer, J. (2015). Effect of intracellular P content on phosphate removal in Scenedesmus sp. Experimental study and kinetic expression. Bioresource Technology. 175:325-332. https://doi.org/10.1016/j.biortech.2014.10.08132533217

Interferência de imagens de apego em adultos com transtorno de personalidade borderline

Interpersonal problems of people with Borderline Personality Disorder (bpd) may accentuate emotional dysregulation. The aim of this research was to investigate how individuals with bpd react to images of Secure Attachment (sa) and Insecure Attachment (ia). Participants with bpd (n = 6) were compared to a control group (n = 6) matched in number, sex and age (M = 29 years, SD = 5,49). Both groups responded to the Parental Bonding Instrument (pbi). Psychologists selected the attachment images used (n = 8). Subsequently, participants with bpd and the control group performed an Emotional Interference (ei) task and explicitly evaluated attachment images. Individuals with bpd had significantly impaired parental bonding than the control group. The bpd group evaluated the aistimuli as substantially more represen - tative of an insecure attachment than the control group and the psychologists. There was no effect of ei on the applied task. The results of this study suggest that the insecure attachment of individuals with bpd influences the explicit evaluation of attachment images.Keywords: Borderline personality disorder, attachment theory, emotional interference. Los problemas interpersonales de personas con Trastorno de Personalidad Borderline (TBP) pueden acentuar la desregulación emocional. El objetivo de esta investigación fue estudiar de qué forma individuos con tpb reaccionan a las imágenes de Apego Seguro (AS) y Apego Inseguro (AI). Los participantes con tpb (n=6) fueron comparados a un grupo control (n=6) emparejados en número, sexo y edad (M=29 años, DP=5.49). Ambos grupos respondieron al Instrumento de Vínculo Parental (IVP). Las imágenes de apego utilizadas fueron seleccionadas por psicólogos (n=8). Posteriormente, los participantes con TBP y el grupo control realizaron una tarea de Interferencia Emocional (IE) y evaluaron explícitamente las imágenes de apego. Los individuos con TBP presentaron un vínculo parental significativamente más perjudicado que el grupo control. El grupo con tpb evaluó los estímulos de ai como significativamente más representativos de un apego del tipo inseguro que del grupo control y los psicólogos. No hubo efecto de ie en la tarea aplicada. Los resultados de este estudio sugieren que el apego inseguro de individuos con TBP influencia la evaluación explícita de imágenes de apego.Palabras clave: trastorno de personalidad borderline, teoría del apego, interferencia emocional.Os problemas interpessoais de pessoas com Transtorno de Personalidade Borderline (tpb) podem acentuar a desregulação emocional. O objetivo desta pesquisa foi investigar de que forma indivíduos com tpb reagem às imagens de Apego Seguro (as) e Apego Inseguro (ai). Os participantes com tpb (n=6) foram comparados a um grupo controle (n=6) pareados em número, sexo e idade (M=29 anos, DP=5.49). Ambos os grupos responderam ao Instrumento de Vínculo Parental (ivp). As imagens de apego utilizadas foram selecionadas por psicólogos (n=8). Posteriormente, os participantes com tpb e o grupo controle realizaram uma tarefa de Interferência Emocional (ie) e avaliaram explicitamente as imagens de apego. Indivíduos com tpb apresentaram um vínculo parental significativamente mais prejudicado do que o grupo controle. O grupo com tpb avaliou os estímulos de ai como significativamente mais representativos de um apego do tipo inseguro do que o grupo controle e os psicólogos. Não houve efeito de ie na tarefa aplicada. Os resultados deste estudo sugerem que o apego inseguro de indivíduos com tpb influencia na avaliação explícita de imagens de apego.Palavras-chave: transtorno de personalidade borderline, teoria do apego, interferência emocional

Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor

Ammonia-oxidizing bacteria (AOB) are very sensitive to environmental conditions and wastewater treatment plant operational parameters. One of the most important factors affecting their activity is pH. Its effect is associated with: NH3/NH4 þ and HNO2/NO2 chemical equilibriums and biological reaction rates. The aim of this study was to quantify and model the effect of pH and free nitrous acid (FNA) concentration on the activity of AOB present in a lab-scale partial nitritation reactor. For this purpose, two sets of batch experiments were carried out using biomass from this reactor. Fluorescent in situ hybridization analysis showed that Nitrosomona eutropha and Nitrosomona europaea species were dominant in the partial nitritation reactor (>94%). The experimental results showed that FNA inhibits the AOB activity. This inhibition was properly modelled by the noncompetitive inhibition function and the half inhibition constant value was determined as 1.32 mg HNO2-N L 1. The optimal pH for these AOB was found to be in the range 7.4 7.8. The pH inhibitory effect was stronger at high pH values than at low pH values. Therefore, an asymmetric inhibition function was proposed to represent the pH effect on these bacteria. A combination of two sigmoidal functions was able to reproduce the experimental results obtained.Claros Bedoya, JA.; Jiménez Douglas, E.; Aguado García, D.; Ferrer, J.; Seco Torrecillas, A.; Serralta Sevilla, J. (2013). Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor. Water Science and Technology. 67(11):2587-2594. doi:10.2166/wst.2013.132S25872594671

Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model

[EN] Plant-wide modelling can be considered an appropriate approach to represent the current complexity in water resource recovery facilities, reproducing all known phenomena in the different process units. Nonetheless, novel processes and new treatment schemes are still being developed and need to be fully incorporated in these models. This work presents a short chronological overview of some of the most relevant plant-wide models for wastewater treatment, as well as the authors' experience in plant-wide modelling using the general model BNRM (Biological Nutrient Removal Model), illustrating the key role of general models (also known as supermodels) in the field of wastewater treatment, both for engineering and research.Seco, A.; Ruano, MV.; Ruiz-Martínez, A.; Robles Martínez, Á.; Barat, R.; Serralta Sevilla, J.; Ferrer, J. (2020). Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model. Water Science & Technology. 81(8):1700-1714. https://doi.org/10.2166/wst.2020.056S17001714818Barat, R., Montoya, T., Seco, A., & Ferrer, J. (2011). Modelling biological and chemically induced precipitation of calcium phosphate in enhanced biological phosphorus removal systems. Water Research, 45(12), 3744-3752. doi:10.1016/j.watres.2011.04.028Barat, R., Serralta, J., Ruano, M. V., Jiménez, E., Ribes, J., Seco, A., & Ferrer, J. (2013). Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants. Water Science and Technology, 67(7), 1481-1489. doi:10.2166/wst.2013.004Batstone, D. J., Hülsen, T., Mehta, C. M., & Keller, J. (2015). Platforms for energy and nutrient recovery from domestic wastewater: A review. Chemosphere, 140, 2-11. doi:10.1016/j.chemosphere.2014.10.021Borrás F. L. 2008 Técnicas microbiológicas aplicadas a la identificación y cuantificación de organismos presentes en sistemas EBPR (Microbiological Techniques Applied to Identification and Quantification of Organisms Present in EBPR Systems). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Claros, J., Jiménez, E., Aguado, D., Ferrer, J., Seco, A., & Serralta, J. (2013). Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor. Water Science and Technology, 67(11), 2587-2594. doi:10.2166/wst.2013.132Copp, J. B., Jeppsson, U., & Rosen, C. (2003). TOWARDS AN ASM1 – ADM1 STATE VARIABLE INTERFACE FOR PLANT-WIDE WASTEWATER TREATMENT MODELING. Proceedings of the Water Environment Federation, 2003(7), 498-510. doi:10.2175/193864703784641207Dorofeev, A. G., Nikolaev, Y. A., Kozlov, M. N., Kevbrina, M. V., Agarev, A. M., Kallistova, A. Y., & Pimenov, N. V. (2017). Modeling of anammox process with the biowin software suite. Applied Biochemistry and Microbiology, 53(1), 78-84. doi:10.1134/s0003683817010100Drewnowski, J., Zaborowska, E., & Hernandez De Vega, C. (2018). Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs. E3S Web of Conferences, 30, 03007. doi:10.1051/e3sconf/20183003007Durán F. 2013 Modelación matemática del tratamiento anaerobio de aguas residuales urbanas incluyendo las bacterias sulfatorreductoras. Aplicación a un biorreactor anaerobio de membranas (Mathematical Model of Urban Wastewater Anaerobic Treatment Including Sulphate Reducing Bacteria. Application to an Anaerobic Membrane Bioreactor). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Ekama, G. A. (2009). Using bioprocess stoichiometry to build a plant-wide mass balance based steady-state WWTP model. Water Research, 43(8), 2101-2120. doi:10.1016/j.watres.2009.01.036EPA 2006 User's manual version 4.03 2006. Available from: https://www.epa.gov/ceam/minteqa2-equilibrium-speciation-model (accessed July 2019).Fernández-Arévalo, T., Lizarralde, I., Fdz-Polanco, F., Pérez-Elvira, S. I., Garrido, J. M., Puig, S., … Ayesa, E. (2017). Quantitative assessment of energy and resource recovery in wastewater treatment plants based on plant-wide simulations. Water Research, 118, 272-288. doi:10.1016/j.watres.2017.04.001Ferrer, J., Seco, A., Serralta, J., Ribes, J., Manga, J., Asensi, E., … Llavador, F. (2008). DESASS: A software tool for designing, simulating and optimising WWTPs. Environmental Modelling & Software, 23(1), 19-26. doi:10.1016/j.envsoft.2007.04.005Ferrer J., Seco A., Ruano M. V., Ribes J., Serralta J., Gómez T., Robles A. 2011 LoDif BioControl® Control Software, Intellectual Property. Main Institution: Universitat de València; Universitat Politècnica de València.Flores-Alsina, X., Corominas, L., Snip, L., & Vanrolleghem, P. A. (2011). Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies. Water Research, 45(16), 4700-4710. doi:10.1016/j.watres.2011.04.040Flores-Alsina, X., Arnell, M., Amerlinck, Y., Corominas, L., Gernaey, K. V., Guo, L., … Jeppsson, U. (2014). Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of The Total Environment, 466-467, 616-624. doi:10.1016/j.scitotenv.2013.07.046Flores-Alsina, X., Kazadi Mbamba, C., Solon, K., Vrecko, D., Tait, S., Batstone, D. J., … Gernaey, K. V. (2015). A plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models. Water Research, 85, 255-265. doi:10.1016/j.watres.2015.07.014Ge, Z. (2017). Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems, 171, 16-25. doi:10.1016/j.chemolab.2017.09.021Grau, P., de Gracia, M., Vanrolleghem, P. A., & Ayesa, E. (2007). A new plant-wide modelling methodology for WWTPs. Water Research, 41(19), 4357-4372. doi:10.1016/j.watres.2007.06.019Grau, P., Copp, J., Vanrolleghem, P. A., Takács, I., & Ayesa, E. (2009). A comparative analysis of different approaches for integrated WWTP modelling. Water Science and Technology, 59(1), 141-147. doi:10.2166/wst.2009.589Henze M., Gujer W., Mino T., van Loosdrecht M. C. M. 2000 Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical Report No.9. IWA Publishing, London, UK.Jeppsson, U., & Pons, M.-N. (2004). The COST benchmark simulation model—current state and future perspective. Control Engineering Practice, 12(3), 299-304. doi:10.1016/j.conengprac.2003.07.001Jeppsson, U., Rosen, C., Alex, J., Copp, J., Gernaey, K. V., Pons, M.-N., & Vanrolleghem, P. A. (2006). Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. Water Science and Technology, 53(1), 287-295. doi:10.2166/wst.2006.031Ji, X., Liu, Y., Zhang, J., Huang, D., Zhou, P., & Zheng, Z. (2018). Development of model simulation based on BioWin and dynamic analyses on advanced nitrate nitrogen removal in deep bed denitrification filter. Bioprocess and Biosystems Engineering, 42(2), 199-212. doi:10.1007/s00449-018-2025-xJiménez, E., Giménez, J. B., Ruano, M. V., Ferrer, J., & Serralta, J. (2011). Effect of pH and nitrite concentration on nitrite oxidation rate. Bioresource Technology, 102(19), 8741-8747. doi:10.1016/j.biortech.2011.07.092Jiménez, E., Giménez, J. B., Seco, A., Ferrer, J., & Serralta, J. (2012). Effect of pH, substrate and free nitrous acid concentrations on ammonium oxidation rate. Bioresource Technology, 124, 478-484. doi:10.1016/j.biortech.2012.07.079Kazadi Mbamba, C., Flores-Alsina, X., John Batstone, D., & Tait, S. (2016). Validation of a plant-wide phosphorus modelling approach with minerals precipitation in a full-scale WWTP. Water Research, 100, 169-183. doi:10.1016/j.watres.2016.05.003Kazadi Mbamba, C., Lindblom, E., Flores-Alsina, X., Tait, S., Anderson, S., Saagi, R., … Jeppsson, U. (2019). Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems. Water Research, 155, 12-25. doi:10.1016/j.watres.2019.01.048Liu, Y., Peng, L., Ngo, H. H., Guo, W., Wang, D., Pan, Y., … Ni, B.-J. (2016). Evaluation of Nitrous Oxide Emission from Sulfide- and Sulfur-Based Autotrophic Denitrification Processes. Environmental Science & Technology, 50(17), 9407-9415. doi:10.1021/acs.est.6b02202Lizarralde, I., Fernández-Arévalo, T., Brouckaert, C., Vanrolleghem, P., Ikumi, D. S., Ekama, G. A., … Grau, P. (2015). A new general methodology for incorporating physico-chemical transformations into multi-phase wastewater treatment process models. Water Research, 74, 239-256. doi:10.1016/j.watres.2015.01.031Lizarralde, I., Fernández-Arévalo, T., Manas, A., Ayesa, E., & Grau, P. (2019). Model-based opti mization of phosphorus management strategies in Sur WWTP, Madrid. Water Research, 153, 39-52. doi:10.1016/j.watres.2018.12.056Maere, T., Verrecht, B., Moerenhout, S., Judd, S., & Nopens, I. (2011). BSM-MBR: A benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Research, 45(6), 2181-2190. doi:10.1016/j.watres.2011.01.006Mannina, G., Ekama, G., Caniani, D., Cosenza, A., Esposito, G., Gori, R., … Olsson, G. (2016). Greenhouse gases from wastewater treatment — A review of modelling tools. Science of The Total Environment, 551-552, 254-270. doi:10.1016/j.scitotenv.2016.01.163Martí, N., Barat, R., Seco, A., Pastor, L., & Bouzas, A. (2017). Sludge management modeling to enhance P-recovery as struvite in wastewater treatment plants. Journal of Environmental Management, 196, 340-346. doi:10.1016/j.jenvman.2016.12.074Moretti, P., Choubert, J.-M., Canler, J.-P., Buffière, P., Pétrimaux, O., & Lessard, P. (2017). Dynamic modeling of nitrogen removal for a three-stage integrated fixed-film activated sludge process treating municipal wastewater. Bioprocess and Biosystems Engineering, 41(2), 237-247. doi:10.1007/s00449-017-1862-3Nagy, J., Kaljunen, J., & Toth, A. J. (2019). Nitrogen recovery from wastewater and human urine with hydrophobic gas separation membrane: experiments and modelling. Chemical Papers, 73(8), 1903-1915. doi:10.1007/s11696-019-00740-xNewhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019). Data-driven performance analyses of wastewater treatment plants: A review. Water Research, 157, 498-513. doi:10.1016/j.watres.2019.03.030Nopens, I., Batstone, D. J., Copp, J. B., Jeppsson, U., Volcke, E., Alex, J., & Vanrolleghem, P. A. (2009). An ASM/ADM model interface for dynamic plant-wide simulation. Water Research, 43(7), 1913-1923. doi:10.1016/j.watres.2009.01.012Nopens, I., Benedetti, L., Jeppsson, U., Pons, M.-N., Alex, J., Copp, J. B., … Vanrolleghem, P. A. (2010). Benchmark Simulation Model No 2: finalisation of plant layout and default control strategy. Water Science and Technology, 62(9), 1967-1974. doi:10.2166/wst.2010.044Ontiveros, G. A., & Campanella, E. A. (2013). Environmental performance of biological nutrient removal processes from a life cycle perspective. Bioresource Technology, 150, 506-512. doi:10.1016/j.biortech.2013.08.059Penya-Roja, J. M., Seco, A., Ferrer, J., & Serralta, J. (2002). Calibration and Validation of Activated Sludge Model No.2d for Spanish Municipal Wastewater. Environmental Technology, 23(8), 849-862. doi:10.1080/09593332308618360Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). A plant-wide energy model for wastewater treatment plants: application to anaerobic membrane bioreactor technology. Environmental Technology, 37(18), 2298-2315. doi:10.1080/09593330.2016.1148903Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). Economic and environmental sustainability of submerged anaerobic MBR-based (AnMBR-based) technology as compared to aerobic-based technologies for moderate-/high-loaded urban wastewater treatment. Journal of Environmental Management, 166, 45-54. doi:10.1016/j.jenvman.2015.10.004Rehman, U., Audenaert, W., Amerlinck, Y., Maere, T., Arnaldos, M., & Nopens, I. (2017). How well-mixed is well mixed? Hydrodynamic-biokinetic model integration in an aerated tank of a full-scale water resource recovery facility. Water Science and Technology, 76(8), 1950-1965. doi:10.2166/wst.2017.330Rieger L., Gillot S., Langergraber G., Ohtsuki T., Shaw A., Takacs I., Winkler S. 2012 Guidelines for Using Activated Sludge Models Scientific and Technical report No. 21. EWA Task Group on Good Modelling Practice. IWA Publishing Volume 11.Robles, A., Ruano, M. V., Ribes, J., Seco, A., & Ferrer, J. (2014). Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR). Journal of Membrane Science, 465, 14-26. doi:10.1016/j.memsci.2014.04.012Robles, A., Capson-Tojo, G., Ruano, M. V., Seco, A., & Ferrer, J. (2018). Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste. Waste Management, 80, 299-309. doi:10.1016/j.wasman.2018.09.031Ruano, M. V., Serralta, J., Ribes, J., Garcia-Usach, F., Bouzas, A., Barat, R., … Ferrer, J. (2012). Application of the general model ‘Biological Nutrient Removal Model No. 1’ to upgrade two full-scale WWTPs. Environmental Technology, 33(9), 1005-1012. doi:10.1080/09593330.2011.604877Seco, A., Ribes, J., Serralta, J., & Ferrer, J. (2004). Biological nutrient removal model No.1 (BNRM1). Water Science and Technology, 50(6), 69-70. doi:10.2166/wst.2004.0361Serralta, J., Ferrer, J., Borrás, L., & Seco, A. (2004). An extension of ASM2d including pH calculation. Water Research, 38(19), 4029-4038. doi:10.1016/j.watres.2004.07.009Shoener, B. D., Schramm, S. M., Béline, F., Bernard, O., Martínez, C., Plósz, B. G., … Guest, J. S. (2019). Microalgae and cyanobacteria modeling in water resource recovery facilities: A critical review. Water Research X, 2, 100024. doi:10.1016/j.wroa.2018.100024Solon, K., Flores-Alsina, X., Kazadi Mbamba, C., Ikumi, D., Volcke, E. I. P., Vaneeckhaute, C., … Jeppsson, U. (2017). Plant-wide modelling of phosphorus transformations in wastewater treatment systems: Impacts of control and operational strategies. Water Research, 113, 97-110. doi:10.1016/j.watres.2017.02.007Solon, K., Jia, M., & Volcke, E. I. P. (2019). Process schemes for future energy-positive water resource recovery facilities. Water Science and Technology, 79(9), 1808-1820. doi:10.2166/wst.2019.183Vanrolleghem, P. A., Rosen, C., Zaher, U., Copp, J., Benedetti, L., Ayesa, E., & Jeppsson, U. (2005). Continuity-based interfacing of models for wastewater systems described by Petersen matrices. Water Science and Technology, 52(1-2), 493-500. doi:10.2166/wst.2005.055

Biological Nutrient Removal Model Nº 2 (BNRM2): A general model for Wastewater Treatment Plants

This paper presents the plant-wide model Biological Nutrient Removal Model No. 2 (BNRM2). Since nitrite was not considered in the BNRM1, and this previous model also failed to accurately simulate the anaerobic digestion because precipitation processes were not considered, an extension of BNRM1 has been developed. This extension comprises all the components and processes required to simulate nitrogen removal via nitrite and the formation of the solids most likely to precipitate in anaerobic digesters. The solids considered in BNRM2 are: struvite, amorphous calcium phosphate, hidroxyapatite, newberite, vivianite, strengite, variscite, and calcium carbonate. With regard to nitrogen removal via nitrite, apart from nitrite oxidizing bacteria two groups of ammonium oxidizing organisms (AOO) have been considered since different sets of kinetic parameters have been reported for the AOO present in activated sludge systems and SHARON (Single reactor system for High activity Ammonium Removal Over Nitrite) reactors. Due to the new processes considered, BNRM2 allows an accurate prediction of wastewater treatment plant performance in wider environmental and operating conditions.This research work has been supported by the Spanish Research Foundation (CICYT Projects, PPQ2002-04043-C02, CTM2005-06919-C03-/TECNO) and Entidad Publica de Saneamiento de Aguas Residuales de la Comunidad Valenciana, which are gratefully acknowledged. This paper was presented at WWTmod2012 and the fruitful discussions are kindly acknowledged.Barat Baviera, R.; Serralta Sevilla, J.; Ruano García, MV.; Jiménez Douglas, E.; Ribes Bertomeu, J.; Seco Torrecillas, A.; Ferrer, J. (2013). Biological Nutrient Removal Model Nº 2 (BNRM2): A general model for Wastewater Treatment Plants. Water Science and Technology. 67(7):1481-1489. https://doi.org/10.2166/wst.2013.004S1481148967

OPRM1 influence on and effectiveness of an individualized treatment plan for prescription opioid use disorder patients

Screening for opioid use disorder should be considered in chronic non-cancer pain (CNCP) patients with long-term use of opioids. The aim of our study was to assess the effectiveness of an individualized treatment plan (ITP) for prescription opioid dependence that included screening of pharmacogenetic markers. An observational prospective study was performed using prescription opioid-dependent CNCP outpatients (n = 88). Patients were divided into nonresponders, responders, or high responders according to their response to the ITP. Genotyping of OPRM1 (A118G), OPRD1 (T921C), COMT (G472A), ABCB1 (C3435T), and ARRB2 (C8622T) was performed by real-time PCR. Our ITP achieved a significant reduction of the morphine equivalent daily dose (MEDD) in 64% of responders, including 33% of high responders. Nonopioid medication or buprenorphine use was significantly higher at final versus basal visit. 118-AA OPRM1 patients required significantly lower MEDD at basal and final visits. Our ITP showed effectiveness and security in reducing MEDD in opioid-dependent patients, with good conversion to buprenorphine that was more pronounced in 118-AA OPRM1 patients

Real-time control strategy for nitrogen removal via nitrite in a SHARON reactor using pH and ORP sensors

This paper presents a real-time control strategy for nitrogen removal via nitrite in a continuous flow SHARON reactor using on-line available and industrially feasible sensors (pH and ORP). The developed control strategy optimizes the length of aerobic and anoxic phases as well as the external carbon source addition. This strategy, implemented in a laboratory-scale SHARON reactor fed with synthetic wastewater and real dewatering sludge supernatant, was able to cope with step variations in influent flow rate and ammonium concentration. The main advantages of this control strategy over the traditional operation mode with fixed carbon source dosification and fixed length cycle operation were: better effluent quality (ammonia concentration decreased from 12 to 2 mg NH4–N L−1 and nitrogen removal efficiency raised from 95% to 98%) as result of the shorter cycle length: 2.9 h versus 4.0 h, and savings in external carbon addition: 1332 mg COD d−1 versus 2100 mg COD d−1.Financial support from MCYT (project CTM2005-06919-C03/TECN), Generalitat Valenciana (ACOMP06/144), and Universidad Politecnica de Valencia (UPV-FPI grant 2008-11 and PAID-06-06) are gratefully acknowledged.Claros Bedoya, JA.; Serralta Sevilla, J.; Seco Torrecillas, A.; Ferrer, J.; Aguado García, D. (2012). Real-time control strategy for nitrogen removal via nitrite in a SHARON reactor using pH and ORP sensors. Process Biochemistry. 47:1510-1515. https://doi.org/10.1016/j.procbio.2012.05.020S151015154

Exploring the limits of anaerobic biodegradability of urban wastewater by AnMBR technology

[EN] Anaerobic membrane bioreactors (AnMBRs) can achieve maximum energy recovery from urban wastewater (UWW) by converting influent COD into methane. The aim of this study was to assess the anaerobic biodegradability limits of urban wastewater with AnMBR technology by studying the possible degradation of the organic matter considered as non-biodegradable as observed in aerobic membrane bioreactors operated at very high sludge retention times. For this, the results obtained in an AnMBR pilot plant operated at very high SRT (140 days) treating sulfate-rich urban wastewater were compared with those previously obtained with the system operating at lower SRT (29 to 70 days). At 140 days SRT the organic matter biodegraded by the AnMBR system accounted for 64.4% of the influent COD (45.9% was removed by sulfate reducing bacteria (SRB), and only 18.5% was converted into methane, highlighting the strong competition between SRB and methanogenic archaea (MA) when treating sulfate-rich wastewater). Almost half of the methane produced (46%) was dissolved in the permeate and most of it was recovered by a degassing membrane. The organic matter biodegraded by the AnMBR system was similar to the influent anaerobic biodegradability determined by wastewater characterization assays (68.5% of the influent COD), indicating that nearly all the influent's biodegradable organic matter had been removed. This percentage of degraded COD was similar to that obtained in previous studies working at 70 days SRT, showing that the limit of anaerobic biodegradability was already reached in this SRT. The organic matter considered as non-biodegradable according to wastewater characterization assays therefore was not seen to degrade in the AnMBR pilot plant, even at very high SRT. Once the biodegraded COD is close to the influent's anaerobic biodegradability, increasing the SRT is not justified as it only leads to higher operational costs for the same biogas production. These findings support the use of mathematical models for AnMBR design since they accurately represent the behaviour of these systems in a wide range of operating conditions.This research project was supported by the Spanish Ministry of Economy and Competitiveness (MINECO, Project CTM2014-54980-C2-2-R). The authors are also grateful for the support received from the Generalitat Valenciana via CPI-16-155 fellowships.Seco Torrecillas, A.; Mateo-Llosa, O.; Zamorano-López, N.; Sanchis-Perucho, P.; Serralta Sevilla, J.; Martí Ortega, N.; Borrás Falomir, L.... (2018). Exploring the limits of anaerobic biodegradability of urban wastewater by AnMBR technology. Environmental Science: Water Research & Technology. 4(11):1877-1887. https://doi.org/10.1039/c8ew00313kS18771887411Li, W.-W., & Yu, H.-Q. (2011). From wastewater to bioenergy and biochemicals via two-stage bioconversion processes: A future paradigm. Biotechnology Advances, 29(6), 972-982. doi:10.1016/j.biotechadv.2011.08.012Shin, C., & Bae, J. (2018). Current status of the pilot-scale anaerobic membrane bioreactor treatments of domestic wastewaters: A critical review. Bioresource Technology, 247, 1038-1046. doi:10.1016/j.biortech.2017.09.002EEA , Performance of water utilities beyond compliance (Technical report No. 5/2014) , Luxemburg , 2014Martin, I., Pidou, M., Soares, A., Judd, S., & Jefferson, B. (2011). Modelling the energy demands of aerobic and anaerobic membrane bioreactors for wastewater treatment. Environmental Technology, 32(9), 921-932. doi:10.1080/09593330.2011.565806JEISON, D., & VANLIER, J. (2007). Cake formation and consolidation: Main factors governing the applicable flux in anaerobic submerged membrane bioreactors (AnSMBR) treating acidified wastewaters. Separation and Purification Technology, 56(1), 71-78. doi:10.1016/j.seppur.2007.01.022Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2014). The operating cost of an anaerobic membrane bioreactor (AnMBR) treating sulphate-rich urban wastewater. Separation and Purification Technology, 126, 30-38. doi:10.1016/j.seppur.2014.02.013Cashman, S., Ma, X., Mosley, J., Garland, J., Crone, B., & Xue, X. (2018). Energy and greenhouse gas life cycle assessment and cost analysis of aerobic and anaerobic membrane bioreactor systems: Influence of scale, population density, climate, and methane recovery. Bioresource Technology, 254, 56-66. doi:10.1016/j.biortech.2018.01.060Ozgun, H., Dereli, R. K., Ersahin, M. E., Kinaci, C., Spanjers, H., & van Lier, J. B. (2013). A review of anaerobic membrane bioreactors for municipal wastewater treatment: Integration options, limitations and expectations. Separation and Purification Technology, 118, 89-104. doi:10.1016/j.seppur.2013.06.036Lin, H., Peng, W., Zhang, M., Chen, J., Hong, H., & Zhang, Y. (2013). A review on anaerobic membrane bioreactors: Applications, membrane fouling and future perspectives. Desalination, 314, 169-188. doi:10.1016/j.desal.2013.01.019Giménez, J. B., Martí, N., Ferrer, J., & Seco, A. (2012). Methane recovery efficiency in a submerged anaerobic membrane bioreactor (SAnMBR) treating sulphate-rich urban wastewater: Evaluation of methane losses with the effluent. Bioresource Technology, 118, 67-72. doi:10.1016/j.biortech.2012.05.019Glória, R. M., Motta, T. M., Silva, P. V. O., Costa, P. da, Brandt, E. M. F., Souza, C. L., & Chernicharo, C. A. L. (2016). STRIPPING AND DISSIPATION TECHNIQUES FOR THE REMOVAL OF DISSOLVED GASES FROM ANAEROBIC EFFLUENTS. Brazilian Journal of Chemical Engineering, 33(4), 713-721. doi:10.1590/0104-6632.20160334s20150291Scherer, E., & Wichmann, K. (2000). Treatment of Groundwater Containing Methane - Combination of the Processing Stages Desorption and Filtration. Acta hydrochimica et hydrobiologica, 28(3), 145-154. doi:10.1002/1521-401x(200003)28:33.0.co;2-vD. Schippers and R.Schotsman , Recovery and beneficial use of water-based methane, Water21 , 2010 , pp. 34–35Crone, B. C., Garland, J. L., Sorial, G. A., & Vane, L. M. (2016). Significance of dissolved methane in effluents of anaerobically treated low strength wastewater and potential for recovery as an energy product: A review. Water Research, 104, 520-531. doi:10.1016/j.watres.2016.08.019Cookney, J., Mcleod, A., Mathioudakis, V., Ncube, P., Soares, A., Jefferson, B., & McAdam, E. J. (2016). Dissolved methane recovery from anaerobic effluents using hollow fibre membrane contactors. Journal of Membrane Science, 502, 141-150. doi:10.1016/j.memsci.2015.12.037Hatamoto, M., Yamamoto, H., Kindaichi, T., Ozaki, N., & Ohashi, A. (2010). Biological oxidation of dissolved methane in effluents from anaerobic reactors using a down-flow hanging sponge reactor. Water Research, 44(5), 1409-1418. doi:10.1016/j.watres.2009.11.021Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2013). Environmental impact of submerged anaerobic MBR (SAnMBR) technology used to treat urban wastewater at different temperatures. Bioresource Technology, 149, 532-540. doi:10.1016/j.biortech.2013.09.060Lubello, C., Caffaz, S., Gori, R., & Munz, G. (2009). A modified Activated Sludge Model to estimate solids production at low and high solids retention time. Water Research, 43(18), 4539-4548. doi:10.1016/j.watres.2009.08.001L. Cabrera , F.García-Usach , J.Ribes , A.Seco , J. J.Morenilla , F.Llavador and J.Ferrer , Estudio de la producción de fangos en bioreactores de membranas aerobios con elevados valores de tiempo de retención celular, Fangos y lodos , 2009 , vol. 7 , pp. 1–3Giménez, J. B., Robles, A., Carretero, L., Durán, F., Ruano, M. V., Gatti, M. N., … Seco, A. (2011). Experimental study of the anaerobic urban wastewater treatment in a submerged hollow-fibre membrane bioreactor at pilot scale. Bioresource Technology, 102(19), 8799-8806. doi:10.1016/j.biortech.2011.07.014Robles, Á., Durán, F., Ruano, M. V., Ribes, J., Rosado, A., Seco, A., & Ferrer, J. (2015). Instrumentation, control, and automation for submerged anaerobic membrane bioreactors. Environmental Technology, 36(14), 1795-1806. doi:10.1080/09593330.2015.1012180R. E. Moosbrugger , M. C.Wentzel , G. A.Ekama and G. R.Marais , Simple Titration Procedures to Determine H2CO3 * Alkalinity And Short-chain Fatty Acids In Aqueous Solutions Containing Known Concentrations Of Ammonium, Phosphate And Sulphide Weak Acid/Bases. WRC Report No. TT 57/92, UCT Research Report W 74 , 1992Metcalf & Eddy, Inc. , G.Tchobanoglous , F.Burton and H.David Stensel , Wastewater Engineering: Treatment and Reuse , McGraw-Hill Education , 2002D. A. Stahl and R.Amann , in Nucleic Acid Techniques in Bacterial Systematics, Sequencing and Hybridization Techniques in Bacterial Systematics , 1991 , pp. 205–248Crocetti, G., Murto, M., & Björnsson, L. (2006). An update and optimisation of oligonucleotide probes targeting methanogenic Archaea for use in fluorescence in situ hybridisation (FISH). Journal of Microbiological Methods, 65(1), 194-201. doi:10.1016/j.mimet.2005.07.007Daims, H., Brühl, A., Amann, R., Schleifer, K.-H., & Wagner, M. (1999). The Domain-specific Probe EUB338 is Insufficient for the Detection of all Bacteria: Development and Evaluation of a more Comprehensive Probe Set. Systematic and Applied Microbiology, 22(3), 434-444. doi:10.1016/s0723-2020(99)80053-8C. W. Gellings and K. E.Parmenter , Energy efficiency in fertilizer production and use. In Knowledge for Sustainable Development , Encyclopedia of Life Support Systems (EOLSS), Eolss Publisher , Oxford , 2004 , vol. II , pp. 419–450J. B. Giménez , Estudio del tratamiento anaerobio de aguas residuales urbanas en biorreactores de membrana (Doctoral Thesis) , Universitat de València , Valencia , 2014Giménez, J. B., Martí, N., Robles, A., Ferrer, J., & Seco, A. (2014). Anaerobic treatment of urban wastewater in membrane bioreactors: evaluation of seasonal temperature variations. Water Science and Technology, 69(7), 1581-1588. doi:10.2166/wst.2014.069Robles, A., Ruano, M. V., Ribes, J., & Ferrer, J. (2012). Sub-critical long-term operation of industrial scale hollow-fibre membranes in a submerged anaerobic MBR (HF-SAnMBR) system. Separation and Purification Technology, 100, 88-96. doi:10.1016/j.seppur.2012.09.010Robles, A., Ruano, M. V., Ribes, J., & Ferrer, J. (2013). Factors that affect the permeability of commercial hollow-fibre membranes in a submerged anaerobic MBR (HF-SAnMBR) system. Water Research, 47(3), 1277-1288. doi:10.1016/j.watres.2012.11.055Ferrer, J., Pretel, R., Durán, F., Giménez, J. B., Robles, A., Ruano, M. V., … Seco, A. (2015). Design methodology for submerged anaerobic membrane bioreactors (AnMBR): A case study. Separation and Purification Technology, 141, 378-386. doi:10.1016/j.seppur.2014.12.018Regueiro, L., Veiga, P., Figueroa, M., Alonso-Gutierrez, J., Stams, A. J. M., Lema, J. M., & Carballa, M. (2012). Relationship between microbial activity and microbial community structure in six full-scale anaerobic digesters. Microbiological Research, 167(10), 581-589. doi:10.1016/j.micres.2012.06.002Khan, M. A., Patel, P. G., Ganesh, A. G., Rais, N., Faheem, S. M., & Khan, S. T. (2018). Assessing Methanogenic Archaeal Community in Full Scale Anaerobic Sludge Digester Systems in Dubai, United Arab Emirates. The Open Microbiology Journal, 12(1), 123-134. doi:10.2174/1874285801812010123Reyes, M., Borrás, L., Seco, A., & Ferrer, J. (2014). Identification and quantification of microbial populations in activated sludge and anaerobic digestion processes. Environmental Technology, 36(1), 45-53. doi:10.1080/09593330.2014.934745Shin, C., McCarty, P. L., Kim, J., & Bae, J. (2014). Pilot-scale temperate-climate treatment of domestic wastewater with a staged anaerobic fluidized membrane bioreactor (SAF-MBR). Bioresource Technology, 159, 95-103. doi:10.1016/j.biortech.2014.02.06