Crossing the death valley to transfer environmental decision support systems to the water market

Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development.The authors would like to thank the Catalan Water Agency (Agència Catalana de l’Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291.Peer ReviewedPostprint (published version

Addressing the evaluation of EDSS-maintenance

Daily operation and maintenance tasks are needed to guarantee the correct performance of constructed wetlands. The definition of these activities is a complex task since these actions vary according to the characteristics of each facility. To support the definition of these operation and maintenance protocols an Environmental Decision Support System (EDSS) has been constructed (EDSS-maintenance). The methodology used to develop EDSS-maintenance is based on the following five steps: environmental problem analysis, data and knowledge acquisition, model selection, model implementation and evaluation process. The first four steps have been finished; however, the evaluation process is ongoing. This document presents a new approach for this step: two numerical indices allow (a) verifying the performance of the EDSS-maintenance and (b) validating the compliance of the protocols with the user requirements. Moreover, another index enables an easy revision and improvement of the knowledge bases (problems, causes and actions) and so enhances the decision support system.Postprint (published version

Modeling the input-output behaviour of wastewater treatment plants using soft computing techniques

Wastewater Treatment Plants (WWTPs) control and prediction under a wide range of operating conditions is an important goal in order to avoid breaking of environmental balance, keep the system in stable operating conditions and suitable decision-making. In this respect, the availability of models characterizing WWTP behaviour as a dynamic system, is a necessary first step. However, due to the high complexity of the WWTP processes and the heterogeneity, incompleteness and imprecision of WWTP data, finding suitable models poses substantial problems. In this paper, an approach via soft computing techniques is sought, in particular, by experimenting with fuzzy heterogeneous time-delay neural networks to characterize the time variation of outgoing variables. Experimental results show that these networks are able to characterize WWTP behaviour in a statistically satisfactory sense and also that they perform better than other well-established neural network mode.Peer ReviewedPostprint (published version

Wastewater treatment improvement through an intelligent integrated supervisory system

This paper shows the result of years of work by a cooperative research group including chemical engineers, environmental scientists and computer scientists. This research has been focused on the development and implementation of new techniques for the optimisation of complex process management, mainly related to wastewater treatment plants (WWTP). The experience obtained indicates that the best approach is a Supervisory System that combines and integrates classical control of WWTP (automatic controller for maintaining a fixed dissolved oxygen level in the aeration tank, use of mathematical models to describe the process...) with the application of knowledge-based systems (mainly expert systems and case-based systems). The first part is an introduction to wastewater treatment processes and an explanation of the complexity of the management and control of such complex processes. The next section illustrates the architecture of the supervisory system and the work carried out to develop and build the expert system, the casebased system and the simulation model for implementation in a real plant (the Granollers WWTP). Finally, some results of the field validation phase of the Supervisory System when dealing with real situations in the plant are described.Aquest article mostra el resultat de la col·laboració portada a terme durant els darrers anys entre grups d'enginyeria química, enginyeria ambiental i intel·ligència artificial. El treball se centra en el desenvolupament de tècniques per a la millora i supervisió de processos complexos, especialment del tractament biològic d'aigües residuals. L'experiència demostra que la millor opció requereix desenvolupar un sistema supervisor que combini i integri tècniques de control clàssic (controlador automàtic del nivell d'oxigen dissolt en el reactor biològic, ús de models descriptius del procés, etc.) amb sistemes basats en el coneixement (concretament sistemes experts i sistemes basats en casos). El present article descriu la complexitat de la gestió del procés de tractament de les aigües residuals, l'arquitectura integrada que es proposa i el desenvolupament i la construcció de cadascun dels mòduls d'aquesta proposta per a la implementació real a l'estació depuradora d'aigües residuals de Granollers. Finalment, es detallen alguns resultats del procés de validació del seu funcionament enfront de situacions quotidianes de la planta

Decisions on urban water systems: some support

“Water is a scarce resource and its management has to be as effective as possible”. Most of us would certainly agree with this fine sounding phrase. But developing it and putting it into practice is not easy. Firstly, because we are already having problems identifying the meaning or interpretation we give to some words. For example, water as a resource. Water is not just a natural resource, it is the basis of the industrial sector, a generator of cultural heritage and a linchpin of society. And we sometimes use the term scarce when referring to a problem of distribution or overexploitation. In any case, this means that water management is very complex. This is because there are different agents involved and all of them have different interests; these interests are often contradictory and can lead to conflict. Everyone understands the concept of efficient management differently. Efficient: why and for whom? At the same time, we have to make decisions. Decisions that involve a way of managing the resource. For example, authorising (or not) a withdrawal from a water course, building (and how) a treatment plant or defining (what and in which range) the quality parameters guaranteeing its drinkability... These examples, and many more that we could cite, are some of the aspects on which a group of people are responsible for acting, deciding and getting the decisions implemented. The hypothesis presented in this book is that to achieve this efficient management there are no simple formulas or universal solutions. However, this does not mean that all solutions are equally correct. Experience shows us that some are better than others.Postprint (published version

Multidimensional research on university engagement using a mixed method approach

El compromiso o la implicación académica (engagement en inglés) delos estudiantes universitarios se ha convertido en un elemento fundamental desu bienestar y de su rendimiento académico y, además, está muy relacionadotambién con su futuro profesional y compromiso social. Es por esta razón quela definición de dicho concepto y la disposición de instrumentos de evaluacióny estrategias de análisis son imprescindibles para conocer las experiencias deaprendizaje que conducen a mejorar la implicación académica del estudiante.Para el desarrollo de esta investigación hemos utilizado una metodología mixtacuantitativa y cualitativa: análisis factorial exploratorio y confirmatorio, porun lado, y grupos de discusión mediante la técnica de los grupos nominales,por otro. Los objetivos que nos hemos planteado son tres: primero, ahondaren el modelo multidimensional del constructo engagement; segundo, validar uncuestionario que permita evaluar la percepción que tienen los estudiantes delas metodologías de aprendizaje que se utilizan en el aula, y tercero, comprobarla manejabilidad de los grupos nominales como método cualitativo de análisis.Los resultados demuestran que nuestra nueva propuesta ofrece un instrumentoestadísticamente válido para determinar las percepciones del propio engagementy un método cualitativo de utilización eficaz, eficiente y motivador para losestudiantes. Sin embargo, respecto a la multidimensionalidad del constructo, ycontrariamente al punto de vista teórico más aceptado que considera la existencia de tres dimensiones del engagement (comportamiento, cognición y emoción), nuestros resultados solamente revelan dos dimensiones (cognitivo-emocional y comportamental). En la discusión y comentarios damos posibles explicaciones a dicha contradicción.The commitment or academic implication (engagement) of universitystudents has become a fundamental element for their welfare and academicperformance and, furthermore, it is also related to their professional futureand social commitment. For this reason, the definition of the concept and theprovision of assessment strategies and tools are essential to know the learningexperiences that lead to enhancing the academic involvement of the students.To develop our research, we have used a mixed quantitative and qualitativemethodology: exploratory and confirmatory factor analysis on the one hand,and discussion groups using the nominal groups technique on the other hand.We have set three different objectives: first, to delve into the multidimensionalmodel of the construct; second, to validate a questionnaire that allows forevaluation of the students’ perception of the learning methodologies used inthe classroom; and third, to check the manageability of the nominal groupsas a qualitative method of analysis. The results demonstrate that our newproposal provides a statistically valid instrument aimed at determining theperceptions of own engagement and an effective, efficient and motivatingqualitative method for students. However, regarding the multidimensionalityof the construct, contrary to the more accepted theoretical point of view thatconsiders three dimensions of engagement (behaviour, cognition and emotion),our results only reveal two dimensions (cognitive-emotional and behavioural).In the discussion and comments section we give possible explanations for thiscontradiction

Development, implementation and evaluation of an activated sludge supervisory system for the Granollers WWTP

La present tesi pretén recollir l'experiència viscuda en desenvolupar un sistema supervisor intel·ligent per a la millora de la gestió de plantes depuradores d'aigües residuals., implementar-lo en planta real (EDAR Granollers) i avaluar-ne el funcionament dia a dia amb situacions típiques de la planta. Aquest sistema supervisor combina i integra eines de control clàssic de les plantes depuradores (controlador automàtic del nivell d'oxigen dissolt al reactor biològic, ús de models descriptius del procés...) amb l'aplicació d'eines del camp de la intel·ligència artificial (sistemes basats en el coneixement, concretament sistemes experts i sistemes basats en casos, i xarxes neuronals).Aquest document s'estructura en 9 capítols diferents. Hi ha una primera part introductòria on es fa una revisió de l'estat actual del control de les EDARs i s'explica el perquè de la complexitat de la gestió d'aquests processos (capítol 1). Aquest capítol introductori juntament amb el capítol 2, on es pretén explicar els antecedents d'aquesta tesi, serveixen per establir els objectius d'aquest treball (capítol 3). A continuació, el capítol 4 descriu les peculiaritats i especificitats de la planta que s'ha escollit per implementar el sistema supervisor.Els capítols 5 i 6 del present document exposen el treball fet per a desenvolupar el sistema basat en regles o sistema expert (capítol 6) i el sistema basat en casos (capítol 7). El capítol 8 descriu la integració d'aquestes dues eines de raonament en una arquitectura multi nivell distribuïda. Finalment, hi ha una darrer capítol que correspon a la avaluació (verificació i validació), en primer lloc, de cadascuna de les eines per separat i, posteriorment, del sistema global en front de situacions reals que es donin a la depuradoraThe present document wants to gather the experience obtained in the development of a Supervisory System for optimal WWTP management and control, its implementation in a real plant (Granollers WWTP) and its evaluation in the day-to-day operation with typical plant situations. This Supervisory System combines and integrates classical control of WWTP (automatic controller for maintaining a fixed dissolved oxygen level in the aeration tank, use of mathematical models to describe the process.) with the application of tools from the Artificial Intelligence field (knowledge-based systems, mainly expert systems and cased-based systems, and neural networks).This document has been structured into nine chapters. The first part is introductory with a review of the state-of-the-art in wastewater treatment control and supervision and the explanation of the complexity of WWTP management (chapter 1). This introductory chapter together with the second one, where the antecedents to the present thesis are reviewed, are good for the establishment of the objectives (chapter 3). Next, chapter 4 describes the peculiarities and specificities of the selected plant to implement the Supervisory System.Chapters 5 and 6 of the present document explain the work carried out to develop and build the knowledge base of the rule-based or expert system (chapter 6) and the case-based system (chapter 7). Chapter 8 illustrates the integration of these reasoning techniques into a distributed multi-layer architecture. Finally, there is a last chapter focused on the evaluation (verification and validation) of, first of all, each one of the techniques individually, and lately, of the overall Supervisory System when facing with real situations taking place in the wastewater treatment plant

Sustainable WWT Technology Selection Decision Support Framework - Role-playing game on modelling and DSS (problem description and use of EDSS for preliminary design)

Intervenció a càrrec de José Porro i Joaquim Matas sobre sistemes de suport al model qualitatiu i a la decisió aplicats al tractament sostenible de les aigües residuals, emmarcada dins la 'Water-2020 Training School Modelling and Decision Support Systems for Sustainable Wastewater Treatment', celebrat a Girona del 25 al 29 d'abril de 201

Qualitative modelling and decision support frameworks

Intervenció a càrrec de José Porro sobre sistemes de suport al model qualitatiu i a la decisió aplicats al tractament sostenible de les aigües residuals, emmarcada dins la 'Water-2020 Training School Modelling and Decision Support Systems for Sustainable Wastewater Treatment', celebrat a Girona del 25 al 29 d'abril de 201

Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector

With sustainable development as their overarching goal, Urban Water System (UWS) managers need to take into account all social, economic, technical and environmental facets related to their decisions. Decision support systems (DSS) have been used widely for handling such complexity in water treatment, having a high level of popularity as academic exercises, although little validation and few full-scale implementations reported in practice. The objective of this paper is to review the application of artificial intelligence methods (mainly machine learning) to UWS and to investigate the integration of these methods into DSS. The results of the review suggest that artificial neural networks is the most popular method in the water and wastewater sectors followed by clustering. Bayesian networks and swarm intelligence/optimization have shown a spectacular increase in the water sector in the last 10 years, being the latest techniques to be incorporated but overtaking case-based reasoning. Whereas artificial intelligence applications to the water sector focus on modelling, optimization or data mining for knowledge generation, their encapsulation into functional DSS is not fully explored. Few academic applications have made it into decision making practice. We believe that the reason behind this misuse is not related to the methods themselves but rather to the disassociation between the fields of water and computer engineering, the limited practical experience of academics, and the great complexity inherently presentThe authors would like to acknowledge the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007e2013 under REA agreement 289193 (SANITAS ITN). The authors thank the Spanish Ministry of Economy and Competitiveness (RYC-2013-14595, CTM2015-66892-R). The authors also acknowledge support from the Economy and Knowledge Department of the Catalan Government through the Consolidated Research Group (2014 SGR 291) - Catalan Institute for Water Research and (2014-SGR-1168)-LEQUIA-University of Giron