2nd WDSA/CCWI Joint Conference

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https://riunet7pre.upv.es/handle/10251/205560

The Department of Hydraulic Engineering and Environment of the Universitat Politècnica de València (Valencia Tech) is pleased to invite you to the second edition of the WDSA/CCWI Joint Conference to be held in Valencia (Spain).

This conference will bring together professionals from municipalities, consulting firms, and universities to exchange ideas about the big challenges facing the water industry.

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Metadata: a must for the digital transition of wastewater treatment plants
(Editorial Universitat Politècnica de València, 2024-03-06) Aguado García, Daniel; Blumensaat, Frank; Baeza, Juan; Villez, Kris; Ruano, Mª Victoria; Samuelsson, Oscar; Plana, Queralt; Alferez, Janelcy; Dpto. de Ingeniería Hidráulica y Medio Ambiente; Instituto Universitario de Ingeniería del Agua y del Medio Ambiente; Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
[EN] The increment in the number and diversity of available (and affordable) sensors together with the advances in information and communications technologies have made it possible to routinely measure and collect large amounts of data at wastewater treatment plants (WWTPs). This enormous amount of available data has boosted the interest in applying sound data-driven solutions to improve the current normal daily operation of these facilities. However, to have a real impact in current operation practices, useful information from the massive amount of data available should be extracted and turned into actionable knowledge. Machine learning (ML) techniques can search into large amounts of data to reveal patterns that a priori are not evident. ML can be applied to develop high-performance algorithms useful for different tasks such as pattern recognition, anomaly detection, clustering, visualization, classification, and regression. These ML algorithms are very good for data interpolation, but its extrapolation capabilities are low. Hence, the data available for training these data-driven models require points covering the complete space for the independent variables. A significant amount of data is required for this purpose, but data of good quality. To transform big data into smart data, giving value to the massive amount of data collected, it is of paramount importance to guarantee data quality to avoid “garbage in – garbage out”. The reliability of on-line measurements is a hard challenge in the wastewater sector. Wastewater is a harsh environment and poses a significant challenge to achieve sensor accuracy, precision, and responsiveness during long-term use. Despite the huge amount of data that currently being recorded at WWTPs, in many cases nothing is yet being done with them (resulting in data graveyards). Moreover, the use of the data collected is indeed very limited due to the lack of documentation of the data generation process and the lack of data quality assessment. Metadata is descriptive information of the collected data, such as the original purpose, the data-generating devices, the quality, and the context. Metadata is needed to clearly identify the data that should be used for the development of data-driven models. These data should be selected from the same category. If we include data that shouldn’t be in the same data set because they were obtained under different operational conditions, this would lead to unreliable model predictions. ML algorithms learn from data, thus to be useful tools and to really improve the decision-making process in WWTP operation and control, representative, reliable, annotated and high-quality data are needed. Effective digitalization requires the cultivation of good meta-data management practices. Unfortunately, there are no wastewater-specific guidelines available to the production, selection, prioritization, and management of meta-data. To address this challenge, the IWA Task Group on Meta-Data Collection
Roadmap towards Smart Wastewater Treatment Facilities
(Editorial Universitat Politècnica de València, 2024-03-06) Aguado García, Daniel; Haimi, Henri; Mulas, Michela; Corona, Francesco; Dpto. de Ingeniería Hidráulica y Medio Ambiente; Instituto Universitario de Ingeniería del Agua y del Medio Ambiente; Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
[EN] To protect human health and natural ecosystems, wastewater treatment plants (WWTPs) have been traditionally designed to remove pollutants from wastewater. With remarkable success WWTPs have adapted to increasingly stringent discharge limits over the years. Nowadays, municipal wastewater treatment facilities are facing a double transition. On the one hand, the transition towards sustainability and the circular water economy, in which resource recovery from wastewater (water recovery, energy recovery and nutrient recovery) plays a fundamental role for its effective implementation. Note that the incorporation of any resource recovery process in a WWTP will immediately turn it into a water resource recovery facility (WRRF). On the other hand, the digital transition, which aims at making the operation of these facilities smart and that undoubtedly could have a synergistic effect together with the paradigm shift towards the effective implementation of circular water economy. To make our current facilities smart, there is a growing interest in finding the way to convert the collected process data into intelligent actions for improving their operation. This is not an easy task for many reasons: - the harsh environment in which the instrumentation has to work (corrosive, sludgy, biofilm formation with biological activity…), - almost complete absence of metadata that would make it easy the interpretation of the process data that it is being collected and that would enable its future use, - the almost complete absence of automated data quality assurance, required to avoid “garbage in – garbage out”- the ever-increasing number of process sensors available (data overload), that must be properly processed and made easily available for further use to make them useful- large amounts of data are collected and stored in databases but not wisely used, thus, resulting in data graveyards, - the excessive cost of nutrient and organic matter sensors/analysers which moreover are labour maintenance intensive, fact that restrict their availability to the range of large facilities, thus, they are not usually available for small size facilities (which are the vast majority). - the intelligent sensors and data-driven models must be maintainable by the plant workers (not by Data scientists), - the lack of process expertise in the development of the artificial intelligent tools, - plant operators are often accustomed to their operational routines and, therefore, cultural change is needed in the organization for successful digital transition and adopting new intelligent tools. The progress in computing capabilities together with the large amount of collected process data in WWTPs have created the perfect storm for the machine learning boom we are observing, but all the aforementioned issues can make the incredible digital transition opportunity that exists today completely lost. In an attempt to avoid this disaster, this paper tries to shed light on the path towards increasing
Sustainable wastewater treatment solutions for water-smart circular economy
(Editorial Universitat Politècnica de València, 2024-03-06) Aguado García, Daniel; Haimi, Henri; Mikola, Anna; Soares, Ana; Jeppsson, Ulf; Dpto. de Ingeniería Hidráulica y Medio Ambiente; Instituto Universitario de Ingeniería del Agua y del Medio Ambiente; Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
[EN] The Protection of aquatic water bodies and human health is a paramount objective accomplished by wastewater treatment systems. Traditionally, pollutants are managed and removed in wastewater treatment plants (WWTPs), following a paradigm in which wastewater is considered a waste. Wastewater treatment requires significant amounts of resources, such as energy and chemicals, while sludge is produced, requiring further treatment. A decade ago, a new paradigm emerged, suggesting that municipal wastewater is a source of resources, particularly reclaimed water, materials (e.g., nutrients) and energy. Many processes applicable for this new paradigm already existed, and others have been further developed (struvite-crystallization, membrane contactors, air-stripping, ionic exchange, electrodialysis, direct osmosis, etc.). Recently, resource recovery processes have been extensively developed and investigated to optimize their operation. Reclaimed water can be used for recharging aquifers, irrigation in agriculture and cooling applications. Potential risks posed by the use of reclaimed water – and of other recovered wastewater resources – must be assessed and managed during the lifecycle of the application. For example, membrane separation processes are recognised as suitable for this application to remove pathogens and particles to ensure water quality. Traditional WWTP design is based on effluent quality requirements and investment costs, with energy efficiency being only rarely considered. Larger facilities exhibit lower normalized electric consumption than smaller WWTPs, and older ones normally consume more than modern facilities (although is process dependent). For instance, in Spain it is possible to find facilities with specific electric consumptions 5-10 times higher than in modern and optimized facilities. This clearly reflects the great margin for potential energy savings. Electricity consumption at WWTPs can be reduced by improving the processes and their operation, as well as through mechanical equipment improvement. The aeration of the biological process is the major electricity consumer; thus, control strategies have been deployed to its optimization. Also, less oxygen-demanding process alternatives have been explored, like the simultaneous nitrification-denitrification operated at very low dissolved oxygen concentration. Partial nitritation and deammonification processes with low oxygen consumption per nitrogen load removed, are especially suited for treating supernatant from sludge dewatering units. However, these low energy solutions might have a downside with direct greenhouse gas GHG emissions, especially N2O. Anaerobic digestion of sludge, usually applied in large WWTPs, produces biogas that can generate both electricity and heat for local use or external use, through combined heat and power production, or liquefied biogas for external use. It is also possible to increase biogas production through co-digestion of external substrates, a

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