Roadmap towards Smart Wastewater Treatment Facilities
[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 treatme...
| Autores: | , , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/206088 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/206088 |
| Access Level: | acceso abierto |
| Palabra clave: | Control Data analytics Digitalization Metadata Wastewater treatment plant Water resource recovery facility |
| Sumario: | [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 |
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