Implementation of a decision support system for sewage sludge management

In this work, a decision support system (DSS) coupled with wastewater treatment plant (WWTP) simulator tool that uses a hierarchical set of key performance indicators (KPIs) to provide an assessment of the performance of WWTP systems is presented. An assessment of different Scenarios in a real WWTP case study, each consisting of a different set of sludge line technologies and derived combinations, was successfully conducted with the developed DSS–WWTP simulator, based on Scenario simulation and hierarchical KPI analysis. The test carried out on the selected WWTP showed that although thermal valorisation and thermal hydrolysis showed similar (the best) economic viability, the latter showed additional benefits, including synergies related to improving the thermal balance of the overall WWTP even when considering other technologies. On the other hand, biogas-upgrading technologies allowed reduction of emissions, but with higher costs and thermal demands. The usage of this tool may allow the development of proposals for technological priorities as a pathway to the transition to circular economy based on the management criteria of the correspondent sanitation system.This work is supported by DAM (Depuración de Aguas del Mediterráneo) and by the Industrial Doctorate Programme (ref. 2017-DI-048) of the Catalan Agency of University and Research Grants Management (AGAUR).Peer ReviewedPostprint (published version

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

The fourth-revolution in the water sector encounters the digital revolution

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.Manel Garrido-Baserba and Diego Rosso acknowledge the United States Department of Energy (CERC-WET US Project 525 2.5). Lluís Corominas acknowledges the Ministry of Economy and competitiveness for the Ramon and Cajal grant (RYC2013-465 14595) and the following I3. We thank Generalitat de Catalunya through Consolidated Research Group 2017 SGR 1318. ICRA researchers acknowledge funding from the CERCA program.Peer ReviewedPostprint (author's final draft

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

The digital revolution in the urban water cycle and its ethical–political implications: a critical perspective

The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications.Peer ReviewedPostprint (published version

Comparison of optimisation algorithms for centralised anaerobic co-digestion in a real river basin case study in Catalonia

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.Peer ReviewedPostprint (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

Reasoning about river basins: WaWO+ revisited

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper characterizes part of an interdisciplinary research effort on Artificial Intelligence (AI) techniques and tools applied to Environmental Decision-Support Systems (EDSS). WaWO+ the ontology we present here, provides a set of concepts that are queried, advertised and used to support reasoning about and the management of urban water resources in complex scenarios as a River Basin. The goal of this research is to increase efficiency in Data and Knowledge interoperability and data integration among heterogeneous environmental data sources (e.g., software agents) using an explicit, machine understandable ontology to facilitate urban water resources management within a River Basin.Peer ReviewedPostprint (author's final draft

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

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