Forbedring av koagulant-doseringskontroll i renseprosesser for vann og avløp

Chemical coagulation is one of the most important treatment processes in wastewater treatment and drinking water treatment. Defining the optimal coagulant dosage is a vital operation that decides the treatment efficiency and economy of the coagulation process. Chemical coagulation is a well-defined process where the optimal coagulant dosage is dependent on the influent quality, expressed by particle concentration, pH, temperature, colour or phosphate, alkalinity, etc. However, no conceptual model has been developed due to the complexity of this process and the research on coagulant dosage control has continued for decades (Ratnaweera and Fettig, 2015). Among all the avenues of research, the model predictive control based on online measurements is the most promising concept for coagulant dosage control. It presents various methods of model calibration and well-defined testing procedures. A Feed-Forward (FF) model based concept of a multi-parameter dosing control system for wastewater was originally proposed by Ratnaweera et al. (1994) and then improved upon by Lu (2003) and Rathnaweera (2010). According to previous results of full-scale tests, the multi-parameter dosing control system has proven to provide acceptable effluent quality and improved economy on most occasions in several wastewater treatment plants.Kjemisk felling er en av de viktigste enhetsprosessene i både avløps- og drikkevannsbehandling. Identifisering av optimal koagulantdose er sentralt i driften av koaguleringsprosessen, og avgjørende for både rensegraden og driftsøkonomien i prosessen. Kjemisk felling er en veldefinert prosess der den optimale koagulantdosen avhenger av kvaliteten på innkommende vann, gitt ved partikkelkonsentrasjon, pH, temperatur, farge eller fosfatinnhold, alkalinitet osv. Det finnes imidlertid ingen universielle konseptuell modell for å bestemme optimal dose ettersom prosessen er svært kompleks. Dette har ført til årtier med forskning på regulering av koagulantdosen (Ratnaweera og Fettig, 2015). Av de ulike forskningsretningene har prediktiv regulering basert på online målinger vist seg svært populært, og inkluderer forskjellige metoder for modellkalibrering og definerte testprosedyrer. Et konsept bestående av multi-parameter doseringsregulering for avløpsrensing ble opprinnelig foreslått av Ratnaweera et al. (1994) og forbedret av Lu (2003) og Rathnaweera (2010). Tidligere fullskala tester har vist at systemet for multi-parameter doseringsregulering gir akseptabel kvalitet på behandlet vann og forbedret driftsøkonomi i et antall avløpsbehandlingsanlegg.DOSCON A

Optimisation of membrane technology for water reuse

Increasing freshwater scarcity is making reclamation of wastewater effluent more economically attractive as a means of preserving freshwater resources. The use of an integrated membrane system (IMS), the combination of micro/ultra-filtration (MF/UF) followed by reverse osmosis (RO) membranes, represents a key process for municipal wastewater reuse. A major drawback of such systems is the fouling of both the MF/UF and RO membranes. The water to be treated by the IMS system varies from one wastewater treatment plant (WWTP) to another, and its fouling propensity changes correspondingly. It is thus preferable to conduct pilot trials before implementing a full-scale plant. This thesis aims to look at the sustainability of IMS technology dedicated to indirect potable reuse (IPR) in terms of fouling minimisation and cost via a 600 m3 .d- 1 pilot plant. Wastewater reuse plants, using IMS, as well as statistical methods for membrane optimisation were reviewed. Box-Behnken design was used to define optimum operating envelopes of the pilot plant for both the microfiltration and the reverse osmosis in terms of fouling minimisation. Same statistical method was used to enhance the efficiency of the MF cleaning-in place through bench-scale test. Data from the pilot plant MF process allow to determine relationship between reversible and irreversible fouling, and operating parameters and feed water quality. Life cycle cost analysis (LCCA) of the both trains (MF/RO/AOP and MF/AOP) of the pilot plant was performed and compared with the LCCA of two full-scale plant

Innovative Surveillance and Process Control in Water Resource Recovery Facilities

Water Resource Recovery Facilities (WRRF), previously known as Wastewater Treatment Plants (WWTP), are getting increasingly complex, with the incorporation of sludge processing and resource recovery technologies. Along with maintaining a stringent effluent water quality standard, the focus is gradually shifting towards energy-efficient operations and recovery of resources. The new objectives of the WRRF demand an economically optimal operation of processes that are subjected to extreme variations in flowrate and composition at the influent. The application of online monitoring, process control, and automation in WRRF has already shown a steady increase in the past decade. However, the advanced model-based optimal control strategies, implemented in most process industries, are less common in WRRF. The complex nature of biological processes, the unavailability of simplified process models, and a lack of cost-effective surveillance infrastructure have often hindered the implementation of advanced control strategies in WRRF. The ambition of this research is to implement and validate cost-efficient monitoring alternatives and advanced control strategies for WRRF by fully utilizing the powerful Internet of Things (IoT) and data science tools. The first step towards implementing an advanced control strategy is to ensure the availability of surveillance infrastructure for monitoring nutrient compositions in WRRF processes. In Paper A, a soft sensor, based on Extended Kalman Filter, is developed for estimating water-quality parameters in a Sequential Batch MBBR process using reliable and inexpensive online sensors. The model used in the soft sensor combines the mechanistic understanding of the nutrient removal process with a statistical correlation between nutrient composition and easy-to-measure parameters. Paper B demonstrates the universality of the soft sensor through validation tests conducted in a Continuous Multistage MBBR pilot plant. The drift in soft-sensor estimation caused by a mismatch between the mathematical model and process behavior is studied in Paper B. The robustness of the soft sensor is assessed by observing estimated nutrient composition values for a period of three months. A systematic method to calibrate the measurement model and update model parameters using data from periodic lab measurements are discussed in Paper B. The term SCADA has been ubiquitous while mentioning online monitoring and control strategy deployment in WRRFs. The present digital world of affordable communication hardware, compact single board processors, and high computational power presents several options for remote monitoring and deployment of soft sensors. In Paper C, a cost-effective IoT strategy is developed by using an open-source programming language and inexpensive hardware. The functionalities of the IoT infrastructure are demonstrated by using it to deploy a soft sensor script in the ContinuousMultistage MBBR pilot plant. A cost-comparison between the commercially available alternatives presented in Paper A and the open-source IoT strategy in Paper B and Paper C highlights the benefits of the new monitoring infrastructure. Lack of reliable control models have often been the cause for the poor performance of advanced control strategies, such as Model Predictive Controls (MPC) when implemented to complex biological nutrient removal processes. Paper D attempts to overcome the inadequacies of the linear prediction model by combining a recursive model parameter estimator with the linear MPC. The new MPC variant, called the adaptive MPC (AMPC), reduces the dependency of MPC on the accuracy of its prediction model. The performance of the AMPC is compared with that of a linear MPC, nonlinear MPC, and the traditional proportional-integral cascade control through simulator-based evaluations conducted on the Benchmark Simulator platform(BSM2). The advantages of AMPC compared to its counterparts, in terms of reducing the aeration energy, curtailing the number of effluent ammonia violations, and the use of computational resources, are highlighted in Paper D. The complex interdependencies between different processes in a WRRF pose a significant challenge in defining constant reference points for WRRFs operations. A strategy that decides control outputs based on economic parameters rather than maintaining a fixed reference set-point is introduced in Paper E. The model-based control strategy presented in Paper D is further improved by including economic parameters in the MPC’s objective function. The control strategy known as Economic MPC (EMPC) is implemented for optimal dosage of magnesium hydroxide in a struvite recovery unit installed in a WRRF. A comparative study performed on the BSM2 platform demonstrates a significant improvement in overall profitability for the EMPC when compared to a constant or a feed-forward flow proportional control strategy. The resilience of the EMPC strategy to variations in the market price of struvite is also presented in Paper E. A combination of cost-effective monitoring infrastructure and advanced control strategies using advanced IoTs and data science tools have been documented to overcome some of the critical problems encountered in WRRFs. The overall improvement in process efficiency, reduction in operating costs, an increase in resource recovery, and a substantial reduction in the price of online monitoring infrastructure contribute to the overall aim of transitioning WRRFs to a self-sustaining facility capable of generating value-added products.Water Resource Recovery Facilities (WRRF), tidligere kjent som avløpsrenseanlegg (WWTP), blir stadig mer komplekse ettersom flere prosess steg tillegges anleggene i form av slambehandling og ressursgjenvinningsteknologi. Foruten hovedmålet om å imøtekomme strenge avløpsvannskvalitetskrav, har anleggenes fokus gradvis skiftet mot energieffektiv drift og gjenvinning av ressurser. Slike nye mål krever økonomisk optimal drift av prosesser som er utsatt for ekstreme variasjoner i volum og sammensetning av tilløp. Bruk av online overvåking, prosesskontroll og automatisering i WRRF har jevnt økt det siste tiåret. Likevel er avanserte modellbaserte kontrollstrategier for optimalisering ikke vanlig i WRRF, i motsetning til de fleste prosessindustrier. Komplekse forhold i biologiske prosesser, mangel på tilgang til pålitelige prosessmodeller og mangel på kostnadseffektiv overvåkingsinfrastruktur har ofte hindret implementeringen av avanserte kontrollstrategier i WRRF. Ambisjonen med denne avhandlingen er å implementere og validere kostnadseffektive overvåkingsalternativer og avanserte kontrollstrategier somutnytter kraftige Internet of Things (IoT) og datavitenskapelige verktøy i WRRF sammenheng. Det første steget mot implementering av en avansert kontrollstrategi er å sørge for tilgjengelighet av overvåkingsinfrastruktur for måling av næringsstoffer i WRRF-prosesser. Paper A demonstrerer en virtuell sensor basert på et utvidet Kalman filter, utviklet for å estimere vannkvalitetsparametere i en sekvensiell batch MBBR prosess ved hjelp av pålitelige og rimelige online sensorer. Modellen som brukes i den virtuelle sensoren kombinerer en mekanistisk forståelse av prosessen for fjerning av næringsstoffer fra avløpsvann med et statistisk sammenheng mellom næringsstoffsammensetning i avløpsvann og parametere som er enkle å måle. Paper B demonstrerer det universale bruksaspektet til den virtuelle sensoren gjennom valideringstester utført i et kontinuerlig flertrinns MBBR pilotanlegg. Feilene i sensorens estimering forårsaket av uoverensstemmelse mellom den matematiske modellen og prosesseatferden er undersøkt i Paper B. Robustheten til den virtuelle sensoren ble vurdert ved å observere estimerte næringssammensetningsverdier i en periode på tre måneder. En systematisk metode for å kalibrere målemodellen og oppdatere modellparametere ved hjelp av data fra periodiske laboratoriemålinger er diskutert i Paper B. Begrepet SCADA har alltid vært til stede når online overvåking og kontrollstrategi innen WRRF er nevnt. Den nåværende digitale verdenen med god tilgjengelighet av rimelig kommunikasjonsmaskinvare, kompakte enkeltkortprosessorer og høy beregningskraft presenterer flere muligheter for fjernovervåking og implementering av virtuelle sensorer. Paper C viser til utvikling av en kostnadseffektiv IoT-strategi ved hjelp av et programmeringsspråk med åpen kildekode og rimelig maskinvare. Funksjonalitetene i IoT-infrastruktur demonstreres gjennom implementering av et virtuelt sensorprogram i et kontinuerlig flertrinns MBBR pilotanlegg. En kostnadssammenligning mellom de kommersielt tilgjengelige alternativene som presenteres i Paper A og åpen kildekode-IoT-strategi i Paper B og Paper C fremhever fordelene med den nye overvåkingsinfrastrukturen. Mangel på pålitelige kontrollmodeller har ofte vært årsaken til svake resultater i avanserte kontrollstrategier, som for eksempel Model Predictive Control (MPC) når de implementeres i komplekse biologiske prosesser for fjerning av næringsstoffer. Paper D prøver å løse manglene i MPC ved å kombinere en rekursiv modellparameterestimator med lineær MPC. Den nye MPC-varianten, kalt Adaptiv MPC (AMPC), reduserer MPCs avhengighet av nøyaktigheten i prediksjonsmodellen. Ytelsen til AMPC sammenlignes med ytelsen til en lineær MPC, ikke-lineær MPC og tradisjonell proportionalintegral kaskadekontroll gjennom simulatorbaserte evalueringer utført på Benchmark Simulator plattformen (BSM2). Fordelene med AMPC sammenlignet med de andre kontrollstrategiene er fremhevet i Paper D og demonstreres i sammenheng redusering av energibruk ved lufting i luftebasseng, samt redusering i antall brudd på utslippskrav for ammoniakk og bruk av beregningsressurser. De komplekse avhengighetene mellom forskjellige prosesser i en WRRF utgjør en betydelig utfordring når man skal definere konstante referansepunkter for WRRF under drift. En strategi som bestemmer kontrollsignaler basert på økonomiske parametere i stedet for å opprettholde et fast referansesettpunkt introduseres i Paper E. Den modellbaserte kontrollstrategien fra PaperDforbedres ytterligere ved å inkludere økonomiske parametere iMPCs objektiv funksjon. Denne kontrollstrategien kalles Economic MPC (EMPC) og er implementert for optimal dosering av magnesiumhydroksid i en struvit utvinningsenhet installert i en WRRF. En sammenligningsstudie utført på BSM2 plattformen viste en betydelig forbedring i den totale lønnsomheten ved bruk av EMPC sammenlignet med en konstant eller en flow proportional kontrollstrategi. Robustheten til EMPC-strategien for variasjoner i markedsprisen på struvit er også demonstrert i Paper E. En kombinasjon av kostnadseffektiv overvåkingsinfrastruktur og avanserte kontrollstrategier ved hjelp av avansert IoT og datavitenskapelige verktøy er brukt for å løse flere kritiske utfordringer i WRRF. Den samlede forbedringen i prosesseffektivitet, reduksjon i operasjonskostnader, økt ressursgjenvinning og en betydelig reduksjon i pris for online overvåkningsinfrastruktur bidrar til det overordnede målet om å gå over til bærekraftige WRRF som er i stand til å generere verdiskapende produkter.DOSCON A

Développement d un outil intégré pour la Modélisation de Procédés et l Analyse de Cycle de Vie (Ecoconception d usines de procédés et application à la production d eau potable)

Des outils adaptés pour s attaquer aux problématiques environnementales sont nécessaires mais malheureusement absents de l industrie. En effet, l introduction de nouvelles pratiques d écoconception dans l industrie des procédés est entravée par le manque de réalisme et de flexibilité des outils associés. Les objectifs principaux de ce travail de recherche étaient le développement d un outil intégré pour la modélisation de procédés et l analyse de cycle de vie (PM-LCA), ainsi que la formulation d une approche méthodologique affiliée pour l écoconception de procédés. L outil logiciel et l approche méthodologique sont appliqués à la production d eau potable.La revue de la littérature scientifique a permis d appréhender les efforts de recherche nécessaires. Les principales lignes directrices sont établies en conséquence.L outil développé, nommé EVALEAU, consiste en une bibliothèque logicielle de modèles de procédés unitaires permettant le calcul d inventaire de données en fonction de paramètres de procédés. L outil est embarqué dans le logiciel ACV Umberto® en complément de la base de données Ecoinvent. Uneboîte à outils pour l analyse de sensibilité, basée sur la méthode de Morris, est implémentée pour l identification des paramètres de procédés ayant une influence majeure sur les résultats d impacts environnementaux.L outil EVALEAU est testé sur deux études de cas - deux usines de production d eau potable existantes. La fiabilité de l approche est démontrée à travers la comparaison des calculs de qualité de l eau, de consommations d énergie et de matériaux avec les données réelles recueillies sur site. Une procédure d écoconception est expérimentée sur une chaîne de traitement complexe démontrant ainsi la pertinence des résultats de simulations et l utilité de l analyse de sensibilité pour un choix optimal des paramètres opératoires. En conséquence, ce premier outil PM-LCA est censé promouvoir l introduction de pratiques d écoconception dans l industrie de l eauAdapted tools for tackling environmental issues are necessary but they are still missing in industry. Indeed, the introduction of ecodesign practices in the process industry is hindered by the lack of realism and flexibility of related tools.The main objectives of this research work were the development of a fully integrated tool for Process Modelling & Life Cycle Assessment (PM-LCA), and the formulation of an affiliated methodological approach for process ecodesign. The software tool and the methodological approach are meant to be applied to water treatment technologies.The literature review leads to a better comprehension of the required research efforts. The main guidelines for the development of the software tool are stated accordingly.The developed tool, named EVALEAU, consists in a library of unit process models allowing life cycleinventory calculation in function of process parameters. The tool is embedded in Umberto® LCA software and is complementary to Ecoinvent database. A sensitivity analysis toolbox, based on theMorris method, was included for the identification of the process parameters mainly affecting the lifecycle impact assessment results.EVALEAU tool was tested through two case studies - two existing drinking water plants. There liability of the modelling approach was demonstrated through water quality simulation, energy and materials inventory simulation, compared with site real data. An ecodesign procedure was experienced on a complex water treatment chain, demonstrating the relevance of simulation results and the usefulness of sensitivity analysis for an optimal choice of operation parameters.This first developed PM-LCA tool is dedicated to foster the introduction of ecodesign practices in the water industryTOULOUSE-INSA-Bib. electronique (315559905) / SudocSudocFranceF

A methodology for implementing a water balance of ESKOM power stations using the online condition monitoring software EtaPRO

Eskom produces approximately 90% of the electricity used in South Africa of which approximately 90.8% is from fossil fuel power plants. The process of electricity generation requires a significant quantity of raw water; therefore, Eskom is considered a strategic water user in South Africa. Water management is a growing focus area due to the increase in water usage and requires continuous improvement. Water management has been identified as an area lagging behind on the advanced analytics initiatives in Eskom. Excel based tools were used for the development of water balance models and water performance calculations in Eskom. This was attributed to the user-friendly functionality and availability to all users. However, the Excel tool posed challenges in allowing for standardisation and validation of calculations, tracking of model changes, continuous trending and storage of data as well as structured graphical user interfaces for screens and dashboard developments. There was therefore a need to develop a methodology on how to structure a water balance model for coal-fired power plants with standard calculation templates that allowed for customisation by each power plant within Eskom. It was required that the water balance model be implemented on a performance and monitoring tool allowing for comparison of power plant targets to actual online data in real time, enhancing the monitoring capabilities. It should have the ability to generate real time water performance data creating an opportunity for improved water management across the generation fleet. The approach adopted in this dissertation was to learn from existing Eskom Excel water balance tools and develop a standard mathematical model in the form of EtaPRO calculation templates. These templates are be structured such that they function as process components to develop water balances at power plants. The mathematical verification of the Excel calculations were to be conducted using Mathcad. The access to real time data, performance monitoring capabilities and availability at all Eskom Power plants, led to the selection of EtaPRO as the modelling platform. The research conducted led to the development of a methodology for setting up a water balance model for a wet-cooled coal-fired power plant. Calculation templates developed into EtaPRO were validated against the Mathcad mathematical model. The results included a well-documented mathematical model of a water balance in Mathcad and the development of 19 calculation templates that perform the function of standard process components. In addition to calculation templates, multiple Non Volatile (NV) records were created to allow the power plants to capture and track permanent data inputs. NV records also allow for creation of case studies, improving the process monitoring capabilities. A water balance model for a selected power plant was simulated in EtaPRO using the developed calculation templates and user defined formulae. Test screens and dashboards were created to illustrate how the calculation templates and water balance framework would be used to develop a typical water balance model and monitoring system. In conclusion, it is possible to develop process models within the EtaPRO software from well-defined mathematical models to address the performance monitoring concerns on water systems within Eskom

A Survey of Deep Learning Methods for WTP Control and Monitoring

Drinking water is vital for everyday life. We are dependent on water for everything from cooking to sanitation. Without water, it is estimated that the average, healthy human won’t live more than 3–5 days. The water is therefore essential for the productivity of our community. The water treatment process (WTP) may vary slightly at different locations, depending on the technology of the plant and the water it needs to process, but the basic principles are largely the same. As the WTP is complex, traditional laboratory methods and mathematical models have limitations to optimize this type of operations. These pose challenges for water-sanitation services and research community. To overcome this matter, deep learning is used as an alternative to provide various solutions in WTP optimization. Compared to traditional machine learning methods and because of its practicability, deep learning has a strong learning ability to better use data sets for data mining and knowledge extraction. The aim of this survey is to review the existing advanced approaches of deep learning and their applications in WTP especially in coagulation control and monitoring. Besides, we also discuss the limitations and prospects of deep learning

Development of an analytical solution for the parallel second order reaction scheme for chlorine decay modelling

Chlorine is broadly used for water disinfection at the final stage of water treatment because of its high performance to inactivate pathogenic microorganisms, its lower cost compared to other well-known disinfectants and its simple operational needs. However, reaction of chlorine with a wide range of organic and inorganic substances in water causes its decay and formation of chlorinated by-products, which are in some cases carcinogenic and harmful to human health. The major challenge is balancing the risk from these with the cost of operation needed to mitigate the impact. These challenges highlights the importance of having a robust modelling approach for chlorine decay in bulk water as a pre-required step to model the chlorine decay and formation of its by-products in the whole distribution system.In this study, initially, a comprehensive literature review was conducted to investigate and evaluate all existing modelling approaches for chlorine decay prediction especially in bulk water. Among all existing modelling schemes, three models were paid more attention due to their popularity and/or fundamentally valid background. They are first order model, second order model and parallel second order model.During the literature review, comparing the effectiveness of the second order model (SOM) proposed by Clark (1998) with the parallel second order model (PSOM) offered by Kastl et al., (1999), the author found that these two models are both fundamentally sound, although the PSOM had better capability in terms of data fitting, and representing the chlorine decay behaviour is much better than SOM. However, non-existence of analytical solution for PSOM was found to be the major negative point for wide adaptation of PSOM compared to SOM.Trying to understand the basic principles of both models, it was understood that the formulation of SOM was genuine and the researchers who claimed that Clark (1998) made a mistake in deriving the analytical solution were proved wrong. This resulted in having the first publication as a comment in Water Research (Fisher et al., 2010b; Appendix A3).Further study was performed on how SOM was formulated and attempts were made to apply the same methodology to PSOM in order to arrive at an analytical solution. Consequently, making a reasonable assumption, an analytical solution for the parallel second order model was formulated and evaluated against the existing numerical method.As the case study of this research, initially, the previous chlorine decay data from Pilbara Water Treatment Plant was fitted to a first order reaction scheme and it was proved that the data did not comply with it. This was an expected result and the need for other model was validated. For further analysis, fresh water samples were collected from Pilbara Water Treatment Plant to perform chlorine decay tests.Temperature effect on the behaviour of chlorine decay in the bulk water was investigated by integrating Arrhenius equation with PSOM. Three methods of temperature analysis were compared and the best one was recommended for practical application. It was shown that the model was capable enough to properly display the chlorine decay profile when temperature varies.The thesis consists of eight chapters. In chapter 1, a brief description of the research background and the overall objectives of the research are given. Chapter 2 focuses on providing a comprehensive literature review about all involved aspects as well as chlorine decay modelling background. Chapter 3 discusses the methodology and analytical methods for conducting laboratory experiments. Chapter 4 gives a prove that the first order decay model does not show accurate results for chlorine decay prediction and the parallel second order model is much more accurate in predicting chlorine concentration. In Chapter 5, the main part of this research, an analytical solution for the parallel second order model is developed. Chapter 6 evaluates the effectiveness of the parallel second order model against the first and second order model. Within chapter 7, temperature effect on the chlorine decay behaviour and the selected modelling approach is evaluated and chapter 8 gives a brief conclusion and recommendation