Model predictive control of water quality in drinking water distribution systems considering disinfection by-products

The shortage in water resources have been observed all over the world. However, the safety of drinking water has been given much attention by scientists because the disinfection will react with organic matters in drinking water to generate disinfection by-products (DBPs) which are considered as the cancerigenic matters. Although much research has been carried out on the water quality control problem in DWDS, the water quality model considered is linear with only chlorine dynamics. Compared to the linear water quality model, the nonlinear water quality model considers the interaction between chlorine and DBPs dynamics. The thesis proposes a nonlinear model predictive controller which utilises the newly derived nonlinear water quality model as a control alternative for controlling water quality. EPANET and EPANET-MSN are simulators utilised for modelling in the developed nonlinear MPC controller. Uncertainty is not considered in these simulators. This thesis proposes the bounded PPM in a form of multi-input multi-output to robustly bound parameters of chlorine and DBPs jointly and to robustly predict water quality control outputs for quality control purpose. The methodologies and algorithms developed in this thesis are verified by applying extended case studies to the example DWDS. The simulation results are presented and critically analysed

Robust adaptive model predictive control for intelligent drinking water distribution systems

Large-scale complex systems have large numbers of variables, network structure of interconnected subsystems, nonlinearity, spatial distribution with several time scales in its dynamics, uncertainties and constrained. Decomposition of large-scale complex systems into smaller more manageable subsystems allowed for implementing distributed control and coordinations mechanisms. This thesis proposed the use of distributed softly switched robustly feasible model predictive controllers (DSSRFMPC) for the control of large-scale complex systems. Each DSSRFMPC is made up of reconfigurable robustly feasible model predictive controllers (RRFMPC) to adapt to different operational states or fault scenarios of the plant. RRFMPC reconfiguration to adapt to different operational states of the plant is achieved using the soft switching method between the RRFMPC controllers. The RRFMPC is designed by utilizing the off-line safety zones and the robustly feasible invariant sets in the state space which are established off-line using Karush Kuhn Tucker conditions. This is used to achieve robust feasibility and recursive feasibility for the RRFMPC under different operational states of the plant. The feasible adaptive cooperation among DSSRFMPC agents under different operational states are proposed. The proposed methodology is verified by applying it to a simulated benchmark drinking water distribution systems (DWDS) water quality control

Embracing Analytics in the Drinking Water Industry

Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established ìrule-of-thumbî and weak heuristic approaches ñ and an analytics viewpoint, approach, and culture is key to this industry transformation

Inferential measurement and control of ballast water treatment system

As a result of interaction with the surrounding environment, shipping has become one of the vectors of bio-invasion across the globe. Ballast water is one of the means of bio-invasion from shipping through which microorganisms break through natural barriers and establish in a new location. Shipboard treatment systems are predominately considered as mitigating measures for bio-invasion via a ballast water system. Currently shipboard performance monitoring of ballast water treatment systems, and thus assessment of discharge quality of ballast water as required by the Convention, depends on off-line laboratory assays with long delay analysis. Lack of online measurement sensors to assess the viability of microorganisms after treatment has made monitoring and thus control of ballast water treatment systems difficult. In this study, a methodology was developed, through a mathematical algorithm, to provide an inferential model-based measurement system in order to monitor and thus control non-observable ballast water systems. In the developed inferential measurement the primary output of the treatment system is inferred by using easy to measure secondary output variables and a model relating these two outputs. Data-driven modeling techniques, including Artificial Neural Networks (ANN), were used to develop an estimator for the small scale UV treatment system based on the data obtained from conducted experiments. The results from ANN showed more accuracy in term of Root Mean Squared Error (RMSE) and Linear Correlation Coefficient (LCC) when compared to the other techniques. The same methodology was implemented to a larger scale treatment system comprising micro-filter and UV reactor. A software-based inferential measurement for online monitoring of the treatment system was then developed. Following monitoring, inferential control of the treatment setup was also accomplished using direct inverse control strategy. A software-based “Decision Making Tool” consisted of two intelligent inverse models, which were used to control treatment flow rate and maintain the effective average UV dose. The results from this study showed that software-based estimation of treatment technologies can provide online measurement and control for ballast water system.EThOS - Electronic Theses Online ServiceEuropean funded project “BaWaPla”GBUnited Kingdo

Évaluation de la validité des modèles de risque pour prédire l’incidence des gastroentérites d’origine hydrique au Québec

Les analyses de risque microbiologique, dont l'ÉQRM (évaluation quantitative du risque microbien) proposent de nouvelles techniques pour évaluer les conséquences sanitaires liées à la contamination microbiologique de l'eau potable. Ces modèles intègrent les données physico-chimiques et microbiologiques des usines de traitement d'eau pour quantifier un risque à la santé. Le projet visait à évaluer le lien entre le risque estimé selon un modèle ÉQRM et l’incidence de giardiase observée. Les banques de données des maladies à déclaration obligatoire et d’INFO-SANTÉ ont été utilisées pour comparer le résultat de l’analyse de risque à celui des analyses épidémiologiques. Les municipalités considérées les plus à risque par l'ÉQRM ont une incidence de gastroentérite et de parasitoses plus élevée. Cependant, l'ampleur du risque prédit ne correspond pas à celui observé. Il est souhaitable que les modèles d’ÉQRM incorporent des données populationnelles pour prédire avec une plus grande exactitude le risque épidémiologique

Measuring Risks of Interdependencies in Enterprise Systems: An Application to Ghana’s Salt Enterprise

This dissertation describes the use of Functional Dependency Network Analysis (FDNA) for modeling risks resulting from dependencies among elements of enterprise systems with application to salt processing enterprise in Ghana. FDNA was developed to model dependencies among members of enterprise systems by highlighting two dimensions of dependency: strength and criticality. Nonetheless, the concepts and analytics for these two dimensions of dependencies needed further development and generalization in the context of project management and systems development in developing countries. Managing risks within the interdependency in enterprise systems through integration will help improve global economic growth. Coherent theory for enterprise integration must be developed, especially in developing countries like Ghana. The significance of this dissertation is the further development of theoretical concept that can be used to analyze dimensions of dependencies in enterprise systems. This model development is contingent upon the strength and criticality dimensions of dependencies in enterprise systems as they apply to project management and the development of enterprise systems. The research covers empirical investigation of the complexities and of enterprise risk management in the Sub-Saharan region for the appropriateness of using the FDNA concept to develop the salt processing enterprise in Ghana

Sustainable Environmental Solutions

This book collects research activities focused on the development of new processes to replace obsolete practices that are often highly invasive, unsustainable, and socially unacceptable.Taking inspiration from real problems and the need to face real cases of contamination or prevent potentially harmful situations, the development and optimization of ‘smart’ solutions, i.e., sustainable not only from an environmental point of view but also economically, are discussed in order to encourage, as much as possible, their actual implementation