Decision support systems (DSS) for wastewater treatment plants: a review of the state of the art

The use of decision support systems (DSS) allows integrating all the issues related with sustainable developmentin view of providing a useful support to solve multi-scenario problems. In this work an extensive review on theDSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide anupdated compendium on DSSs in view of supporting researchers and engineers on the selection of the mostsuitable method to address their management/operation/design problems. Results showed that DSSs weremostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective inorder to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based onquality and operational issues are very often applied to site-specific conditions. Finally, it would be important toencourage the development of more user-friendly DSSs to increase general interest and usability.This work is part of a research project supported by grant of the Italian Ministry of Education, University and Research (MIUR) through the Research project of national interest PRIN2012 (D.M. 28 December 2012 n. 957/Ric – Prot. 2012PTZAMC) entitled “Energy consumption and Greenhouse Gas (GHG) emissions in the wastewater treatment plants: a decision support system for planning and management – http://ghgfromwwtp.unipa.it” in which the first author is the Principal Investigator. In addition, some coauthors acknowledge the partial support of the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

Modeling Alternative Collaborative Governance Network Designs: An Agent-Based Model of Water Governance in the Lake Champlain Basin, Vermont

Published by Oxford University Press on behalf of the Public Management Research Association. With the widespread use of collaborative governance mechanisms for mitigating water pollution, an opportunity exists to test alternative institutional designs based on collaborative governance theory using computer simulation models, particularly when there is a clear relationship between governance networks, observable resource allocation decisions, and measurable outcomes. This is especially the case for wicked problems like nonpoint source water pollution where there are compelling questions regarding how best to design policies, allocate funds, and build administrative capacity to meet water quality standards. We present an agent-based model (ABM) of water governance for the Lake Champlain Basin to simulate the impacts of alternative collaborative governance arrangements on the development of suites of water quality projects. The ABM is connected or coupled with land use and phosphorus load accumulation models that are informed by existing hydrologic models, project datasets, and state-set load reduction targets. We find that regionally arranged collaborative governance in water quality project planning and implementation can lead to better water quality outcomes, thereby affirming one of the central premises of collaborative governance regime theory. We also find that externally mandated collaboration, as opposed to voluntary, self-initiated collaboration, can lead to better water quality outcomes, adding to our understanding of which type of collaborative governance arrangement is best suited to the specific contexts of this case. Further, without adequate administrative capacity in the form of human resources located in central network actors to manage project funds, administrative bottlenecks may form and money can go unspent. This research demonstrates the efficacy of using simulations of alternative institutional design for theory testing and tuning, and policy prototyping

AquaHet-PSO: An Informative Path Planner for a Fleet of Autonomous Surface Vehicles with Heterogeneous Sensing Capabilities based on Multi-Objective PSO

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivativesThe importance of monitoring and evaluating the quality of water resources has significantly grown over time. To achieve this effectively, an option is to employ an intelligent monitoring system capable of measuring the physical and chemical parameters of water. Surface vehicles equipped with sensors for measuring water quality parameters offer a viable solution for these missions. This work presents a novel approach called AquaHet-PSO, which addresses the challenge of simultaneously monitoring multiple water quality parameters with several peaks of contamination using a heterogeneous fleet of autonomous surface vehicles. Each vehicle in the fleet possesses a different set of sensors, such as number of sensors and sensor types, which is the definition provided by the authors for a heterogeneous fleet. The AquaHet- PSO consists of three main phases. In the initial phase, the vehicles traverse the water resource to obtain preliminary models of water quality parameters. These models are then utilized in the second phase to identify potential contamination areas and assign vehicles to specific action zones. In the final phase, the vehicles focus on a comprehensive characterization of the parameters. The proposed system combines several techniques, including Particle Swarm Optimization and Gaussian Processes, with the integration of genetic algorithm to maximize the distances between the initial positions of vehicles equipped with identical sensors, and a distributed communication system in the final phase of the AquaHet-PSO. Simulation results in the Ypacarai lake demonstrate the effectiveness and efficiency of AquaHet-PSO in generating accurate water quality models and detecting contamination peaks. The proposed method demonstrated improvements compared to the lawnmower approach. It achieved a remarkable 17% improvement, on r-squared data, in generating complete models of water quality parameters throughout the lake. In addition, it achieved a 230% improvement in accurate characterization of high pollution areas and a 24% increase in pollution peak detection specifically for heterogeneous fleets equipped with four or more identical sensors.Ministerio de Ciencia e Innovación PID2021-126921OB-C21 TED2021-131326BC21Universidad de Sevill

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

A Systems Approach to Point Source Indication of Metformin Found in Local Water Systems – the Case of Milwaukee County

Pharmaceutical pollutants are present in traceable concentrations in Milwaukee County water system and Lake Michigan. The actual point sources and nature of entry into the water system is difficult to determine with certainty. Pharmaceuticals have been found to persist at the South Shore Wastewater Treatment Facility (SSWTF) in Milwaukee, Wisconsin. The highest concentration was found to be for the pharmaceutical drug metformin. Metformin is a first line drug for the treatment of type 2 diabetes mellitus. The broad goal of this exploratory study; the first of its kind, is to correlate trace concentrations of drugs to the point sources. Particularly, we have analyzed the demographic and geographical location of the population in Milwaukee County as potential contributors of drugs in the waste water system. The study uses metformin as a pilot study. The objectives of the Thesis are as follows: Objective 1: Analyze the current status of quantifiable pharmaceuticals in Milwaukee County water Objective 2: Analyze Milwaukee County’s geographic, demographic and socio-economic status to determine and quantify the main population agent attributes to be used in objective 3. Objective 3: Build an Agent Based Simulation Model that incorporates county residents as API point source agents, whose attributes as listed in objective 2 to determine their degree of contribution towards APIs in the waterways. We were able to develop a working model and verify and validate the results through publicly available empirical studies and records. No studies were found utilizing simulation modeling to determine specific sources of pharmaceutical drugs entering the water system. No studies were found measuring the effect of age, race, income and geographical location of potential drug contributors on pharmaceuticals found in the water system. In this study, the results for the total number of diabetics in Milwaukee County was determined to be within an acceptable margin of error specified in the model. The model results indicate approximately 91,353 type 2 diabetics in Milwaukee County which is corroborated by Wisconsin’s department of health reports indicating 93,020 hence the results are within 2% margin of error. The adjusted contributors that account for the measured concentration of metformin at the SSWTF were determined to be within the range of concentrations measured in the influent stream – Minimum: 3,200ng/L, Median 55,000ng/L, Maximum 100,000ng/L. The model results indicate that 76,899.3ng/L concentration present from the contributors which is within range of the actual measured concentration of metformin at the SSWTF influent stream. The model output includes geographical, income, race and age demographics of the contributors by count for each of the 34 zip codes in Milwaukee County. Results analysis indicate that a geographical, racial and socioeconomic difference exist in contributors overall for Milwaukee County. The results also confirm the prevalence of the drug metformin entering the water system while identifying a zip code level detail of individual contributors. This Thesis is an exploratory test bed example showing that agent based modeling can be a valuable tool for industrial engineers and operation research when dealing with geospatial problems that exhibit variability through agency

A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. © 2022, The Author(s)