43 research outputs found

    Energy transfer from the freshwater to the wastewater network using a PAT-equipped turbopump

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    A new strategy to increase the energy efficiency in a water network exists using turbo pumps, which are systems consisting of a pump and a turbine directly coupled on a same shaft. In a turbo pump, the pump is fed by a turbine that exploits a surplus head in a freshwater network in order to produce energy for one system (wastewater) and reduce the excess pressure in another (drinking water). A pump as turbine (PAT) may be preferred over a classic turbine here due to its lower cost. The result of such a coupling is a PAT-pump turbocharger (P&P). In this research, the theoretical performance of a P&P plant is employed using data from a real water distribution network to exploit the excess pressure of a freshwater stream and to feed a pump conveying wastewater toward a treatment plant. Therefore, the P&P plant is a mixed PAT-pump turbocharger, operating with both fresh and wastewater. A new method to perform a preliminary geometric selection of the machines constituting the P&P plant has been developed. Furthermore, the plant operation has been described by means of a new mathematical model under different boundary conditions. Moreover, the economic viability of the plant has been assessed by comparison with a conventional wastewater pumping system working in ON/OFF mode. Therefore, the net present value (NPV) of the investment has been evaluated in both situations for different time periods. According to the economical comparison, the PAT-pump turbocharger represents the most economically advantageous configuration, at least until the useful life of the plant. Such convenience amounts to 175% up to a time period equal to 20 years

    Smart Water Management towards Future Water Sustainable Networks

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    [EN] Water management towards smart cities is an issue increasingly appreciated under financial and environmental sustainability focus in any water sector. The main objective of this research is to disclose the technological breakthroughs associated with water and energy use. A methodology is proposed and applied in a case study to analyze the benefits to develop smart water grids, showing the advantages offered by the development of control measures. The case study showed the positive results, particularly savings of 57 GWh and 100 Mm3 in a period of twelve years when different measures from the common ones were developed for the monitoring and control of water losses in smart water management. These savings contributed to reducing the CO2 emissions to 47,385 t CO2-eq. Finally, in order to evaluate the financial effort and savings obtained in this reference systems (RS) network, the investment required in the monitoring and water losses control in a correlation model case (CMC) was estimated, and, as a consequence, the losses level presented a significant reduction towards sustainable values in the next nine years. Since the pressure control is one of the main issues for the reduction of leakage, an estimation of energy production for Portugal is also presentedRamos, HM.; Mcnabola, A.; L√≥pez Jim√©nez, PA.; P√©rez-S√°nchez, M. (2020). Smart Water Management towards Future Water Sustainable Networks. Water. 12(1):1-13. https://doi.org/10.3390/w12010058S113121Sachidananda, M., Webb, D., & Rahimifard, S. (2016). A Concept of Water Usage Efficiency to Support Water Reduction in Manufacturing Industry. Sustainability, 8(12), 1222. doi:10.3390/su8121222Boyle, T., Giurco, D., Mukheibir, P., Liu, A., Moy, C., White, S., & Stewart, R. (2013). Intelligent Metering for Urban Water: A Review. Water, 5(3), 1052-1081. doi:10.3390/w5031052Ritzema, H., Kirkpatrick, H., Stibinger, J., Heinhuis, H., Belting, H., Schrijver, R., & Diemont, H. (2016). Water Management Supporting the Delivery of Ecosystem Services for Grassland, Heath and Moorland. Sustainability, 8(5), 440. doi:10.3390/su8050440P√©rez-S√°nchez, M., S√°nchez-Romero, F. J., & L√≥pez-Jim√©nez, P. A. (2017). Nexo agua-energ√≠a: optimizaci√≥n energ√©tica en sistemas de distribuci√≥n. Aplicaci√≥n ‚ÄėPostrasvase J√ļcar-Vinalop√≥‚Äô (Espa√Īa). Tecnolog√≠a y ciencias del agua, 08(4), 19-36. doi:10.24850/j-tyca-2017-04-02Howell, S., Rezgui, Y., & Beach, T. (2017). Integrating building and urban semantics to empower smart water¬†solutions. Automation in Construction, 81, 434-448. doi:10.1016/j.autcon.2017.02.004Mounce, S. R., Pedraza, C., Jackson, T., Linford, P., & Boxall, J. B. (2015). Cloud Based Machine Learning Approaches for Leakage Assessment and Management in Smart Water Networks. Procedia Engineering, 119, 43-52. doi:10.1016/j.proeng.2015.08.851Lombardi, P., Giordano, S., Farouh, H., & Yousef, W. (2012). Modelling the smart city performance. Innovation: The European Journal of Social Science Research, 25(2), 137-149. doi:10.1080/13511610.2012.660325Smart Cities: Strategic Sustainable Development for an Urban World. Sweden: School of Engineering, Blekinge Institute of Technology https://www.diva-portal.org/smash/get/diva2:832150/FULLTEXT01.pdfSmart Cities: Ranking of European Medium-Sized. Vienna, Austria: Centre of Regional Science (SRF), Vienna University of Technology http://www.smart-cities.eu/download/smart_cities_final_report.pdfHellstr√∂m, D., Jeppsson, U., & K√§rrman, E. (2000). A framework for systems analysis of sustainable urban water management. Environmental Impact Assessment Review, 20(3), 311-321. doi:10.1016/s0195-9255(00)00043-3Smart Water Grid. USA: Department of Civil and Environmental Engineering, Colorado State University http://www.engr.colostate.edu/~pierre/ce_old/Projects/Rising%20Stars%20Website/Martyusheva,Olga_PlanB_TechnicalReport.pdfSmart Metering Introduction. Obtained on 12 August 2015, from Alliance for Water Efficiency http://www.allianceforwaterefficiency.org/smart-meter-introduction.aspxNtuli, N., & Abu-Mahfouz, A. (2016). A Simple Security Architecture for Smart Water Management System. Procedia Computer Science, 83, 1164-1169. doi:10.1016/j.procs.2016.04.239Britton, T. C., Stewart, R. A., & O‚ÄôHalloran, K. R. (2013). Smart metering: enabler for rapid and effective post meter leakage identification and water loss management. Journal of Cleaner Production, 54, 166-176. doi:10.1016/j.jclepro.2013.05.018Sharvelle, S., Dozier, A., Arabi, M., & Reichel, B. (2017). A geospatially-enabled web tool for urban water demand forecasting and assessment of alternative urban water management strategies. Environmental Modelling & Software, 97, 213-228. doi:10.1016/j.envsoft.2017.08.009Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results.A Guide for Sensor Manufacturers and Water Utilities. Ohio: EPA‚ÄďEnvironmental Protection Agency https://www.epa.gov/sites/production/files/2015-06/documents/distribution_system_water_quality_monitoring_sensor_technology_evaluation_methodology_results.pdfSCADA: Supervisory Control and Data Acquision. USA: ISA‚ÄďThe Instrumentation, Systemas and Automation Society https://www.fer.unizg.hr/_download/repository/SCADA-Supervisory_And_Data_Acquisition.pdfCan we make water systems smarter? Opflow http://innovyze.com/news/showcases/SmartWaterNetworks.pdfGurung, T. R., Stewart, R. A., Beal, C. D., & Sharma, A. K. (2015). Smart meter enabled water end-use demand data: platform for the enhanced infrastructure planning of contemporary urban water supply networks. Journal of Cleaner Production, 87, 642-654. doi:10.1016/j.jclepro.2014.09.054Romano, M., & Kapelan, Z. (2014). Adaptive water demand forecasting for near real-time management of smart water distribution systems. Environmental Modelling & Software, 60, 265-276. doi:10.1016/j.envsoft.2014.06.016Samora, I., Franca, M. J., Schleiss, A. J., & Ramos, H. M. (2016). Simulated Annealing in Optimization of Energy Production in a Water Supply Network. Water Resources Management, 30(4), 1533-1547. doi:10.1007/s11269-016-1238-5Sanchis, R., D√≠az-Madro√Īero, M., L√≥pez-Jim√©nez, P. A., & P√©rez-S√°nchez, M. (2019). Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs. Water, 11(12), 2432. doi:10.3390/w11122432Alonso Campos, J. C., Jim√©nez-Bello, M. A., & Mart√≠nez Alzamora, F. (2020). Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms. Agricultural Water Management, 227, 105857. doi:10.1016/j.agwat.2019.105857Controlo Ativo de Perdas de √Āgua. Lisboa: EPAL‚ÄďEmpresa Portuguesa das √Āguas Livres http://www.epal.pt/EPAL/docs/default-source/epal/publica%C3%A7%C3%B5es-t%C3%A9cnicas/controlo-ativo-de-perdas-de-%C3%A1gua.pdf?sfvrsn=30Ndirangu, N., Ng‚Äôang‚Äôa, J., Chege, A., de Blois, R.-J., & Mels, A. (2013). Local solutions in Non-Revenue Water management through North‚ÄďSouth Water Operator Partnerships: the case of Nakuru. Water Policy, 15(S2), 137-164. doi:10.2166/wp.2013.117Romero, L., P√©rez-S√°nchez, M., & Amparo L√≥pez-Jim√©nez, P. (2017). Improvement of sustainability indicators when traditional water management changes: a case study in Alicante (Spain). AIMS Environmental Science, 4(3), 502-522. doi:10.3934/environsci.2017.3.50

    The co-development of HedgeDATE, a public engagement and decision support tool for air pollution exposure mitigation by green infrastructure

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    There is a lack of clear guidance regarding the optimal configuration and plant composition of green infrastructure (GI) for improved air quality at local scale. This study aimed to co-develop (i.e. with feedback from end-users) a public engagement and decision support tool, to facilitate effective GI design and management for air pollution abatement. The underlying model uses user-directed input data (e.g. road type) to generate output recommendations (e.g. plant species) and pollution reduction projections. This model was computerised as a user-friendly tool named HedgeDATE (Hedge Design for Abatement of Traffic Emissions). A workshop generated feedback on HedgeDATE, which we also discuss. We found that data from the literature can be synthesised to predict air pollutant exposure and abatement in open road environments. However, further research is required to describe pollutant decay profiles under more diverse roadside scenarios (e.g. split-level terrain) and to strengthen projections. Workshop findings validated the HedgeDATE concept and indicated scope for uptake. End-user feedback was generally positive, although potential improvements were identified. For HedgeDATE to be made relevant for practitioners and decision-makers, future iterations will require enhanced applicability and functionality. This work sets the foundation for the development of advanced GI design tools for reduced pollution exposure

    Quantification of ETS exposure in hospitality workers who have never smoked

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    <p>Abstract</p> <p>Background</p> <p>Environmental Tobacco Smoke (ETS) was classified as human carcinogen (K1) by the German Research Council in 1998. According to epidemiological studies, the relative risk especially for lung cancer might be twice as high in persons who have never smoked but who are in the highest exposure category, for example hospitality workers. In order to implement these results in the German regulations on occupational illnesses, a valid method is needed to retrospectively assess the cumulative ETS exposure in the hospitality environment.</p> <p>Methods</p> <p>A literature-based review was carried out to locate a method that can be used for the German hospitality sector. Studies assessing ETS exposure using biological markers (for example urinary cotinine, DNA adducts) or questionnaires were excluded. Biological markers are not considered relevant as they assess exposure only over the last hours, weeks or months. Self-reported exposure based on questionnaires also does not seem adequate for medico-legal purposes. Therefore, retrospective exposure assessment should be based on mathematical models to approximate past exposure.</p> <p>Results</p> <p>For this purpose a validated model developed by Repace and Lowrey was considered appropriate. It offers the possibility of retrospectively assessing exposure with existing parameters (such as environmental dimensions, average number of smokers, ventilation characteristics and duration of exposure). The relative risk of lung cancer can then be estimated based on the individual cumulative exposure of the worker.</p> <p>Conclusion</p> <p>In conclusion, having adapted it to the German hospitality sector, an existing mathematical model appears to be capable of approximating the cumulative exposure. However, the level of uncertainty of these approximations has to be taken into account, especially for diseases with a long latency period such as lung cancer.</p
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