Advanced methods for the support of planning, design and management of drinking water supply systems

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POLITECNICO DI MILANO

DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING – ENVIRONMENTAL AREA DOCTORAL PROGRAMME IN ENVIRONMENTAL AND INFRASTRUCTURE ENGINEERING

ADVANCED METHODS FOR THE SUPPORT OF

PLANNING, DESIGN AND MANAGEMENT

OF DRINKING WATER SUPPLY SYSTEMS

Doctoral Dissertation of: Giulia SACCANI

PhD Supervisor: Ing. Manuela ANTONELLI PhD Tutor: Prof. Roberto CANZIANI

PhD Coordinator: Prof. Alberto GUADAGNINI

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A

BSTRACT

Planning, design and management of a drinking water supply (DWS) system in industrialized and densely populated urban areas deals with a high degree of complexity. Its optimization is therefore a hard task, which can take advantage from the application of specific support tools. However the application of the appropriate tools to support DWS managers is not a well-established practice, being support tools mainly reported in literature to develop participatory process for intervention planning or for research applications.

The DWS system of Milan city has been here considered as case study. Appropriate tools have been suggested and evaluated for the support of three main tasks of water treatment planning design and management: the analysis of water quality data, the design of a treatment unit and the interpretation of pilot scale tests.

Firstly, multivariate statistic tools, typically applied only for water quality characterization with a poor interpretation of results, have been applied on a huge water quality database (almost 100 parameters monitored in more than 500 supply wells for 6 years): indications for the optimization of water quality monitoring plan, source water exploitation and treatment train planning have been derived, proving the effectiveness of this approach, also from the point of view of cost minimisation.

Secondly, two different methods of Multi-Objective Optimization (MOO) have been compared for the optimization of the design of a biofilter for heterotrophic denitrification of groundwater: an evolutionary algorithm derived from NSGA-II (Non-dominated Sorting Genetic Algorithm-II) and an interactive method called NIMBUS. The specific MOO problem has been formulated considering environmental, economic and technical conflicting objectives, and it has been solved highlighting strength and weaknesses of both MOO methods. The optimal design solution obtained by the most suitable MOO method has been then positively validated based on experimental data from a pilot scale denitrifying biofilter.

Finally, these data have been interpreted to verify the feasibility of an heterotrophic denitrification process fed on a groundwater contaminated by NO3, pesticides and volatile organic compounds, assessing the

influence of some operating and design parameters. A simplified simulation model has been formulated to overcome complexity usually associated to biofilm models. An Activated Sludge Model has been adapted for the simulation of the attached biomass process and a 2-step denitrification; thus a specific hydraulic model, particulate retention model and biological model were formulated. Kinetic parameters were calibrated and from their validation, indications for model improvement have been suggested.

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Chapter 1

1. State of the art

Water and wastewater treatment systems are complex and dynamic in nature, and treating water to a defined quality level has become a challenge in many contexts. For this reason, water system planning, design and management, can often take advantage of the application of specific support tools. A wide literature does exist on Decision Support Systems (DSS) that are structured approaches, firstly defined in 1978 by Keen and Scott Morton, whose application in the water and wastewater treatment field has been recently reviewed by Hamouda (2009) and Matthews et al. (2012). DSS main strength is the systematic approach; they are applied when the complexity is brought by the joint consideration of environmental, technical, economic, and sociocultural factors. Thus, DSS main purpose is usually to support participative processes addressing decisions with strategic or policy implications, multiple conflicting objectives, and multiple interested stakeholders (Corner and Kirkwood, 1991). However, despite their potential in system analysis, DSS are often too complex and not suitable to support engineers and practitioners in more detailed and specific tasks where the complexity is brought by the context or the process to be analysed. In these cases, the selection of an appropriate support tool becomes an issue and in literature no reviews have been found on such a wide topic. In particular, considering planning, design and management of a Drinking Water Supply (DWS) system, three main engineering tasks can be identified: (i) the acquisition of information about source water quality, plant productivity, treated water quality and operational goals; (ii) the selection and design of treatment train processes and ancillary equipment; (iii) the conduction and interpretation of pilot studies where needed (Crittenden et al., 2012). The following sections are dedicated to each one of the above mentioned engineering tasks and are aimed at presenting tools and methods reported in literature, as well as aspects of the state of the art that require improvements.

1.1 Data mining techniques for context data analysis

In DWS systems usually large watersheds are interested: comprehensive water quality investigations involve collection of water samples over time at various locations, and the analysis of multiple chemical and biological constituents. The size and complexity of the resulting dataset make overall evaluations difficult, and require the application of appropriate data mining tools (Olsen et al., 2012).

To this purpose, various techniques are reported in literature: (i) data analysis through descriptive statistics and hypothesis testing, (ii) predictive model application, and (iii) interpretative data-driven techniques. The first approach is reported for the characterisation of water quality (Schot and Pieber, 2012), the identification of processes affecting water quality (Kats et al., 2011), and the optimization of monitoring programs (Moreau-Fournier and Daughney, 2012). However, all above mentioned studies involved very simple contexts. As an examples, the characterisation of water quality performed by Schot and Pieber (2012), considered a database of only 15 parameters monitored at 35 sampling points for 1 year. A larger database has been considered in the study by Kats et al. (2011) where only one simple hypothesis had to be tested, or in the study by Moreau-Fournier and Daughney (2012) where only the variation of sampling

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frequency has been considered as option of monitoring plan optimization. In more complex contexts, more powerful tools were required.

For what concern predictive models, various techniques have been increasingly applied since the early 1990s, among which the most commonly used are: logistic regression, weights of evidence, fuzzy logic, artificial/probabilistic neural network, discriminant analysis and likelihood ratio function (Sorichetta et al., 2012). In groundwater management and DWS system planning, the application of predictive models is reported for groundwater vulnerability assessment (Sorichetta et al., 2012) and for the estimation of a contaminant concentration in an area with insufficient groundwater data, starting from contaminant levels in other areas with similar hydrogeological setting and land use but more available data (Stigter et al., 2008). However, the main weakness of predictive models is their reliability: studies reported in literature showed that the results are sensitive to vulnerability threshold definition (Masetti et al., 2009), require the identification and correction of sampling bias (Sorichetta et al., 2012), and most of all, require a validation phase as the performance is not directly related to the number of input predictor factors (Sorichetta et al., 2011).

An approach to overcome drawbacks of predictive models and limitations associated to simple descriptive and inference statistic, can be the application of data-driven interpretative tools. In particular, multivariate statistic methods are diffusely applied in watershed studies (Olsen et al., 2012). They use the correlation among water quality constituents to reduce the number of variables, by extracting new components (or factors) linearly independent. Extracted components are then able to describe water quality patterns that result from hydrologic, geochemical and contamination phenomena (Olsen et al., 2012). Techniques such as Principal Components Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) are reported to be unbiased methods to study patterns and extract meaningful information from groundwater quality data (Gourdol et al., 2013; Papaioannou et al., 2010). A lot of studies in literature report their effectiveness in upgrading water quality knowledge (Gourdol et al., 2013; Selle et al., 2013; Narany et al., 2014; Yu et al., 2014) and a survey of 49 literature articles presented by Olsen et al. (2012), revealed that most of the studies were able to successfully relate the results of PCA to specific environmental factors, processes, and/or contamination sources. However, criticism have been raised on the way the results of these analyses have been interpreted. Olsen et al. (2012) stated that the meaningfulness of PCA results depends upon many factors: the design of the sampling program, the quality of data, treatment of data prior to PCA, and the interpretation of PCA results. They revealed that most of reviewed papers did not adequately address all of these issues. More specifically, Selle et al. (2013) indicated that main weakness is that the interpretation of results is limited to the graphical evaluation of the first 2 extracted components. This approach is not generalizable (Selle et al., 2013) and neglects the information associated to the other extracted components, despite their not negligible contribution to the explained variance (Yidana, 2010; Papaioannou et al., 2010; Olsen et al., 2012; Page et al., 2012; Guigues et al., 2013; Yu et al., 2014). Despite the interpretative potential of multivariate statistics, the application of above mentioned tools has been limited to water characterisation or to the identification of processes relating water quality to environmental factors and contamination sources (Yidana, 2010; Mendizabal et al., 2011; Olsen et al., 2012; Selle et al., 2013; Yu et al., 2014). Few studies explore the application of multivariate statistics for monitoring plan optimization (Papaioannou et al., 2010; Page et al., 2012; Guigues et al., 2013). No reference has been found for multivariate statistics as a useful tool for DWS engineers in planning water resource exploitation and DWS treatment infrastructure. However, this would represent an interesting field of application: improvements in the interpretation of PCA/FA results should be proposed to evaluate the applicability of multivariate statistics as a more useful tool for DWS system planning, design and management. A comparison among mathematical tools applied in above mentioned studies is presented in Table 1.1.

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Table 1.1. Data mining techniques applied as support tools in water system problems.

Reference Approach Purpose Applied technique Notes and Recomendations

Schot & Pieber, 2012

Descriptive tools

Water quality

characterization Correlation, Inference tests

- Spatial and temporal variability considered simultaneously through 3D mapping. Kats et al., 2011 Descriptive tools Identification of processes governing water quality Descriptive statistics, Nonparametric inference tests

- Only one water quality aspect and one process considered. Moreau-Fournier and Daughney, 2012 Descriptive tools Monitoring plan optimization Non-parametric Inference tests

- Only reduction of sampling frequency is considered for the optimization of an existing monitoring plan. Masetti et al., 2007 Predictive model Vulnerability zones

identification Weights of Evidence - Masetti et al., 2009 Predictive model Vulnerability zones identification

Cumulative probability plots,

Likelihood Ratio Model - Sensitive to applied threshold value. Sorichetta et

al., 2011

Predictive model

Vulnerability zones

identification Weights of Evidence

- Performance not directly related to the number of input predictor factors. - Need for results validation. Sorichetta et

al., 2012

Predictive model

Vulnerability zones

identification Weights of Evidence

- Need for identification and correction of sampling bias between training points and evidential theme. Sorichetta et al., 2013 Predictive model Vulnerability zones identification

Weights of Evidence, Logistic Regression - Starn et al., 2014 Predictive model Groundwater quality

prediction Inverse modelling

- Need to improve model estimation by using multiple tracers. Stigter et al., 2008 Predictive model Identification of processes governing water quality

Factorial Regression Analysis - Predictive model accuracy depends on the number of explanatory variables. Ammar et

al., 2011

Predictive model

Monitoring plan

optimization Bayesian Analysis

- Need to explore the use of temporal data. - Need to allow gradual addition of new data. Medizabal et al., 2011 Interpretative tools Groundwater quality characterization, Hydrochemical System

Analysis (HCSA) - Geo-referentiation of results. Yidana, 2010 Interpretative

tools

Identification of processes governing

water quality

Q-mode hierarchical Cluster Analyses (CA), R-mode Factor

Analysis (FA)

- Simple bi-plot analysis of regression scores. Olsen et al., 2012 Interpretative tools Identification of processes governing water quality Principal Components Analysis (PCA)

- Simple bi-plot analysis of regression scores. - PCA results were robust (not sensitive) to

data treatment prior to statistical analyses. Selle et al., 2013 Interpretative tools Identification of processes governing water quality

Principal Component Analysis (PCA), End Member Mixing

Analysis

- Need for a detailed analysis of scores investigating their spatial–temporal patterns - Correlation of regression scores with

supplementary data. Narany et al., 2014 Interpretative tools Identification of processes governing water quality Principal Components Analysis (PCA), Analysis of

Variance (ANOVA)

- Geo-referentiation and kriging interpolation of regression scores. Yu et al., 2014 Interpretative tools Identification of processes governing water quality

Cluster Analysis (CA), Principal Components

Analysis (PCA)

- Simple bi-plot analysis of regression scores. Papaioannou et al., 2010 Interpretative tools Monitoring plan optimization

Cluster Analysis (CA), Discriminant Analysis (DA),

Factor Analysis (FA)

- Simple bi-plot analysis of regression scores. Page et al., 2012 Interpretative tools Monitoring plan optimization Principal Components

Analysis (PCA) - Simple bi-plot analysis of regression scores. Guigues et al., 2013 Interpretative tools Monitoring plan optimization

Principal Component Analysis (PCA), Hierarchical Cluster

Analysis (CA), Analysis of Variance (ANOVA)

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1.2 Methods for the selection and design of water treatment solutions

The selection and design of treatment train processes involves both technical and economic evaluations. Technical analysis usually considers performance efficiency and effectiveness, while economic analysis focuses on real costs, as well as social, legal, and environmental aspects (Hamouda, 2009).

Technical and economic aspects are typically in conflict, and the final solution represent a trade-off often found with the help of specific mathematical methods. Two main approaches can be distinguished: multi-criteria analysis (MA) and multi-objective optimization (MOO).

MA has been diffusely applied in the water treatment field, as reported by Hajkowicz and Collins (2007) who reviewed up to 113 papers from 34 countries, recognizing 61 different MA methodologies applied to 8 types of water treatment problems. However, MA can deal only with semi-structured problems where relationships among variables cannot be mathematically modelled (Siskos and Spyridakos, 1999). This characteristic limits the applicability of MA to problems where the set of technical alternatives to be considered is discrete, predetermined and finite (Miettinen, 1999) and it is possible to judge and rank them.

On the contrary, MOO methods apply to all problems where technological alternatives are not explicitly known in advance and an infinite number of them exists (Miettinen 1999). In fact, MOO deals with more structured mathematical models under multiple conflicting objective functions. It considers a continuous infinite set of technical alternatives and allows the identification of compromise solutions, so-called Pareto optimal solutions, where none of the objective values can be improved without impairing at least one of the others (Miettinen, 1999). A multi-objective optimization problem can be formulated as (Miettinen, 1999): , … , , … , (eq.1) subject to = , … , , … , ∈ , (eq.2) where = ∈ ℝ | ≤ ≤ , = 1, … , !" = 0, $ = 1, … , % %& ≤ 0, ℎ = 1, … % . (eq.3)

The real-valued objective functions : →ℝ have to be simultaneously optimized in the feasible region ⊂ ℝ defined by the lower ( ) and upper ( ) bounds for the design variables as well as equality (!_") and inequality (%_&) constraints.

Formalizing the concept, and referring to Figure 1.1, P1, P2 and P3 are all technical alternatives of a MOO problem defined by two conflicting objectives (z1 and z2) to be minimized.

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The solution P1 is said to be dominated by P2 as P2 is better than P1 with respect to at least one objective (z1) and not worse than P1 with respect to all other objectives (only z2 in the considered example). P3 represents a Pareto optimal solution of the MOO problem defined by equations eq.1-eq.3, as it is not dominated by any other solution.

A wide spectrum of MOO methods does exist and the choice of the most appropriate MOO method to be applied depends on the characteristics of the problem to be solved (Miettinen, 1999). A distinction among different methods can be done mainly on three macro characteristics: uncertainty, linearity and the level of involvement of a human decision maker (DM, i.e. the water treatment designer).

As for uncertainty, when it is due to the lack of information about prevailing states and not to the definition of objective function values, stochastic programming is applied (Rivas et al., 2008); on the contrary, when objectives, constraints and consequences of possible actions are not precisely known, mathematical programming in fuzzy environment applies (Miettinen, 1999). Finally, when the outcome of any feasible solution is assumed to be known, because generated by a defined simulation model, the problem is deterministic.

In deterministic MOO, the linearity of the relationships describing the technical alternative binds the choice of the MOO method as not all methods are able to face non-linear relationships (Tang et al., 1997). Considering the water sanitation field, biological treatments are the most diffused processes described by non-linear relationships. Main types of non-linear MOO methods have been reviewed by Miettinen (1999). Finally, a further distinction exists considering the optimization process and the level of involvement of the DM. If all Pareto optimal points are solutions of a MOO problem, when real problems are concerned, the aim of MOO is finding the Pareto optimal solution that best satisfies the needs of the DM. To find this, usually called the most preferred solution (Miettinen 1999; Hamouda, 2009; Hakanen et al., 2011), additional information is required about DM preferences, that is to say about how to rank Pareto optimal solutions. With respect to the DM intervention in the optimization process, four classes have been identified and reported by Miettinen (1999):

- No preference methods. No articulation of preference information is used, meaning that the

opinions of the DM are not taken into consideration. The MOO problem is solved using some relatively simple methods and the solution obtained is presented to the DM who may either accept or reject the solution. These methods have a very low probability to really find the solution best satisfying the DM and their application is thus limited to situations where the DM does not have any special expectations of the solution and is satisfied with any optimal solution.

- A posteriori methods. The complete Pareto optimal front (or a sub-set) is generated and presented

to the DM, who selects the most preferred one among the alternatives. Three main drawbacks can be identified. Firstly the generation process can be computationally expensive and difficult. Secondly, the selection from a large set of alternatives can be a hard task for the DM. Finally, reporting the alternatives to the DM in an effective way is often a challenge, especially when a high number of objectives is involved.

- A priori methods. The DM must specify his preferences and opinions before the optimization

process. The main weakness is that the DM does not necessarily know in advance what it is possible to attain in the problem, and how realistic his expectations are.

- Interactive methods. Progressive articulation of preference information is used: only a sub-set of Pareto optimal points have to be generated and evaluated, and the DM is asked to specify and correct his preferences and selections as the optimization process continues and he gets a better knowledge of the problem and its potentialities. After a reasonable (and finite) number of

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iterations, every interactive method should yield a solution that the DM can be satisfied with and convinced that no considerably better solution exists. Many of the weak points of the methods in the other three classes are overcome: the most satisfactory solution should be found and the DM can be assumed to have more confidence in the final solution since he is involved throughout the optimization process. However this is possible only assuming that the DM has enough time and capabilities for co-operation.

Various examples can be found in literature, with a wide application of “a posteriori methods” such as evolutionary optimization techniques (genetic algorithm, GA). A comprehensive review of the state of the art for GA and their application in water resources planning and management has been carried out by Nicklow et al. (2010). They have shown that evolutionary computation can be a flexible and powerful tool when appropriately used; however, the required computational time resulted to be the main weakness considering the simulation and optimization time, together with the computational time required for the evaluation of the most preferred solutions among all the Pareto optimal solutions obtained. This is confirmed in the study by Penn et al. (2013), who applied an evolutionary method for the sustainable design of greywater reuse systems: 23 trials were needed, lasting 1.7 days each. Similar considerations were reported also by Sweetapple et al. (2014), about the application of the well-established evolutionary algorithm NSGA-II by Deb et al (2002) for the minimization of greenhouse gas emissions from a wastewater treatment.

As an alternative, Ayesa et al. (1998) applied the minimisation of a global cost criterion for the optimization of design and operation of the activated sludge reactor in a wastewater treatment plant (WWTP). Guidelines for an optimized WWTP design and operation were found; however, they reported that the validity of the results is highly conditioned by the penalty function a-priori defined for the global cost definition, in addition to wastewater characterisation and the validity of the model calibration.

Finally, an interactive method, called NIMBUS, has been applied by Hakanen et al. (2011, 2013) for the optimization of the design of a biological activated sludge process in a WWTP. They have demonstrated the applicability of the first interactive decision support tool for this kind of problem, combining a simulator and an interactive multiobjective optimization system. The interactive MOO required an acceptable computational time, as it generated only those Pareto optimal solutions that were of DM’s interest. Furthermore it avoided converting all evaluation criteria to money, highlighted by Hakanen et al. (2011) as a procedure that can lead to unnecessary simplifications. The main weakness outlined was the relevant amount of time where the involvement of the DM was required, even if Hakanen et al. (2011) stressed that this provided the DM with valuable information and new insight into the problem; this learning aspect was reported as a novelty compared to previously used methods in WWTP design (Hakanen et al., 2011).

As a conclusion, considering a MOO problem, its intrinsic characteristics of uncertainty and linearity can guide the choice of the correct family of MOO methods. However, among deterministic non-linear methods it is not clear how to identify the most appropriate one, especially considering the level of DM’s involvement in the optimization process. Neither the criteria adopted for the choice of the MOO method, nor a comparison of efficacy of different mathematical methods, are usually reported in literature, and further studies should be done to address this issue.

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1.3 Methods for pilot scale data interpretation

Where experiences lack, pilot scale tests are the most helpful tools for design and testing processes, both in terms of reactor configuration and operating conditions (Joksimovic et al., 2006; Crittenden et al., 2012). In particular, pilot plant studies are essential to study process applicability if new or unusual conditions are encountered, or to design reactors when process sizing is based on reaction kinetics or process loading criteria but kinetic expressions are not available (Crittenden et al., 2012). Simulation models are then the main tool for the evaluation of data obtained from pilot-scale plants, useful to predict the performance of planned full-scale plants (Morgenroth et al., 2000).

Among available water treatment technologies, biological processes are the typical example of treatments requiring pilot scale testing, as they are characterized by a high sensitivity to feeding and operating condition variations, that can invalidate process effectiveness (Kapoor and Viraraghavan, 1997). This motivate the wide literature focusing on the modelling of biological processes for water treatment (Noguera et al., 1999; Gernaey et al., 2004; Fenu et al., 2010; Ni et al., 2010; Boltz et al., 2010).

In particular, among biological processes, the correct formulation of simulation models is crucial for the interpretation of attached biomass systems. In fact, mathematical modeling of biofilm reactors is more complex than suspended growth reactors, mainly because of a greater complexity in describing the fate of water constituents (i.e. particulate versus soluble substrate), the impact of bulk-liquid hydrodynamics, and biofilm diffusional resistances (Boltz et al. 2010).

However, in activated sludge processes mathematical models are well-established and used both for research and for solving practical engineering problems, while for biofilm systems the situation is quite different (Morgenroth et al., 2000): Practitioners take little advantage from simulation models, diffusely applied only for research purposes. Biofilm reactor design has often been based on empirical criteria, providing a good basis for design, when applied to the conditions for which empirical data was collected, but that should not be extended to other conditions (Boltz et al. 2010).

At the same time, biofilm models have become more and more complex over recent years, taking into account an increasing amount of details at the micro-scale and asking for the definition of an increasing number of parameters, often difficult to evaluate (Morgenroth et al., 2000). The definition of biofilm and boundary layer thicknesses, as well as attachment and detachment rates, are factors of uncertainty (Boltz et al., 2010) and the accurate determination of the fixed-film kinetic parameter values is reported to be difficult too, as the diffusion resistance within the biofilm may mask true reaction kinetics (Huang et al., 1998). This increased complexity has further restrained the application of these models for real problems on attached biomass reactors.

The application of simulation models as practitioners support tools is even more limited when focusing on drinking water treatments. Often in this context, the simulation model simply considers removal kinetics on limiting substrate (De Mendoca et al., 1992; Lazarova et al., 1992; Lazarova et al., 1994; Vrtovšek and Roš, 2006), and there is the lack of models simulating also biomass growth and decay.

However, this approach has two main weaknesses. Firstly, the evaluation of biomass concentration in biological reactors for drinking water applications is crucial: it is associated to potential biomass release in treated effluent during plant operation, and with the residual organic carbon concentration it represents the main driver of the risk of biological regrowth in distribution networks. Secondly, when the considered removal kinetic is referred to only one limiting substrate, other compounds of interest can be neglected. On the contrary, the release of harmful by-products, as nitrite in case of incomplete denitrification, is a relevant aspect to be considered in drinking water application.

Considering in particular the denitrification process, few references are available for drinking water applications. Lazarova et al. (1992, 1994) considered a fluidized bed for groundwater denitrification and

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studied the existence of two pathways related to two different kinds of denitrifying bacteria: real denitrifiers (i.e. Pseudomonas Stutzeri), able to reduce nitrate to nitrite and simultaneously to rapidly reduce nitrite to nitrogen gas, and nitrate reducing bacteria (i.e Pseudomonas Aeruginosa and Pseudomonas Denitrificans), characterized by the repression of nitrite reductase synthesis in presence of certain concentrations of oxygen or nitrate, and by the consequent accumulation of nitrite until the almost complete depletion of nitrate. However, the studies by Lazarova et al. (1992, 1994) modelled the simple removal kinetic of the two nitrogen species and did not present a model considering the biomass growth and decay rates.

In the wastewater literature, some authors highlight the need to model nitrite accumulation and release through a 2-step denitrification kinetic (Marazioti et al., 2003; Ni et al., 2010) and examples can be found for different bacterial populations (Kornaros and Lyberatos, 1998; Magrì and Flotats, 2008; Ni and Yu, 2008; Marsili Libelli et al., 2001). In particular, simulation models proposed by Magrì and Flotats (2008) and Kornaros and Lyberatos (1998) describe respectively a denitrifying biomass with a nitrite reduction rate considerably greater than the nitrate reduction rate, and a nitrite accumulating biomass. Papers presenting kinetic interpretation of 2-step denitrification are presented in Table 1.2, both for drinking water and wastewater applications. Values of kinetic parameters for models simulating also the active biomass in the system are reported in Table 1.3.

Reported considerations highlight that the application of simulation models for the interpretation of attached biomass reactors in drinking water applications should be further investigated. In particular, the consideration of both substrate and biomass kinetics should be considered, and models able to predict nitrite accumulation and release for denitrification application should be suggested.

Table 1.2. Papers presenting kinetic interpretation of 2-step denitrification.

Reference Water Reactor Biomass Model

Lazarova et al., 1992 Groundwater Fluidized bed reactor Attached biomass simple DN model, _{first order DN} Lazarova et al., 1994 Groundwater Fluidized bed reactors Attached biomass simple DN model, _{first order DN}

Almeida et al., 1995 Nitrate added

water Batch CSTR reactor Suspended biomass

simple DN model, Monod type DN Oh and Silverstein, 1999 Nitrate added

water Sequencing Batch Reactor Suspended biomass

simple DN model, zero order kinetics Glass and Silverstein,

1998

Synthetic brine

wastewater Sequencing Batch Reactor Suspended biomass

simple DN model, zero order kinetics Cao et al., 2013 Wastewater Sequencing Batch Reactor Suspended biomass simple DN model, zero order DN Huang et al., 1998 Wastewater Fixed bed reactor Attached biomass simple DN model,

Monod type DN

An et al., 2011 Oily wastewater Batch CSTR reactor Suspended biomass ASM Kornaros and Lyberatos,

1997 Wastewater Activated Sludge reactor Suspended biomass ASM Kornaros and Lyberatos,

1998 Wastewater Activated Sludge reactor Suspended biomass ASM Marazioti et al., 2003 Wastewater Activated Sludge reactor Suspended biomass ASM Magrì and Flotats, 2008 Piggery

Wastewater Sequencing Batch Reactor Suspended biomass ASM

Marsili Libelli et al., 2001 Wastewater Sequencing Batch Reactor Suspended biomass ASM

Ni and Yu, 2008 Wastewater Activated Sludge reactor Suspended biomass ASM Ni et al., 2010 Wastewater Activated Sludge reactor Suspended biomass ASM Ostace et al., 2011 Wastewater Activated Sludge reactor Suspended biomass ASM

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Table 1.3. Kinetic parameters values for denitrification biological models reported in literature. Kinetic parameters @20°C

Stoichiometric parameters @20°C

Growth (Aerobic & Anoxic) Storage Decay Hydrolysis

µmax,S µmax,STO ηNO3 ηNO2 KS Koh KNO3 KNO2 kSTO KSTO bh bh,no kh ηh KX Yh Yno fi

Reference [d-1] [d-1] [-] [-] [g/m3] [g/m3] [g/m3] [g/m3] [d-1] [gXSTO/gXH] [d -1

] [d-1] [d-1] [-] [g/m3] [gCOD/gCOD] [gCOD/gCOD] [-]

Gujer and Henze, 1991 6.0 - 0.8 - 20.0 0.2 0.5 - - - 0.62 - 3.0 0.4 0.03 0.67 - 0.08 Gujer et a.l, 1999 2.0 - 0.6 - - 0.2 0.5 - 5.0 1.0 0.2 0.1 3.0 - 1.0 0.63 0.54 0.2 Magrì & Flotats, 2008 4.16 - 0.23 0.62 4.0[2] _0.01 _0.5[1] _0.12 _- _- _0.17 _- _4.13 _0.75 _0.17 _0.65 _0.533 _0.08

Ni and Yu, 2008* 0.504 h-1 _0.768 0.6[3] _- 99.85 - 0.5[3] _0.5[3] 2.064 1.0[3] 0.432 - 3.0[3] _- _1.0[3] _- _0.4 _0.2 0.24 h-1 _3.36 _1.74 _6.48 _0.06 0.264 h-1 37.68 4.91 1.656 0.552 0.528 h-1 0.696 6.47 1.992 0.1008 Horn et al., 2003 5.0 - - - 10 0.5 - - - 0.5 - -

Marsili Libelli et al., 2001 6.0[2] _- _0.8[2] _- _4.0[2] _0.2[2] _0.5[2] _0.5 _- _- _0.4[2] _- _3.0[2]_0.6[2] _0.1[2] _0.63[2] _- _0.1[2]

Plattes et al., 2007 1.1 - 0.8[1] - 1.35 0.2[1] 0.5[1] - - - 0.62[1] - 3.0[1] 0.4[1] 0.03[1] 0.72 - 0.08[1] Ostace et al., 2011 4.0 - 0.8 0.38 10 0.2[1] _0.5[1] _- _- _- _0.3 _- _3.0[1] _0.8 _0.10 _0.67[1] _- _0.08[1] Ni et al., 2010 1.992 5.04 0.6 0.6 2.0 0.2 0.5 0.5 5.04 1.0 0.1992 0.1008 3.0 - 1.0 0.63 0.54 0.2 An et al., 2011 0.576 (NO3) - - - 37.8 1.54 - - - 5.28·10 -5_(NO 3) - - - - 0.072 (NO2) 7.84·10-5 (NO2)

* Calibration values for 4 case studies Wastewater Treatment Plants [1] ASM1 by Gujer and Henze (1991)

[2] ASM2 by Gujer et al. (1999) [3] Henze (2000)

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1.4 References

Almeida, J.S., Reis, M.A.M., Carrondo, M.J.T., 1995. Competition between nitrate and nitrite reduction in denitrification by Pseudomonas fluorescens. Biotechnology and Bioengineering, 46(5), 476-484. Ammar, K., McKee, M., Kaluarachchi, J., 2011. Bayesian method for groundwater quality monitoring

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Chapter 2

2. Design of the research

Planning, design and management of a drinking water supply (DWS) system, in industrialized and densely populated urban areas, deals with a high degree of complexity. Its optimization is difficult especially in the case of a relevant water demand to be satisfied through multiple DWS units, spread in the urban area and intercepting various punctual and diffuse pollution plumes. In these cases, an efficient DWS system relies on three main aspects: the deep knowledge of source water quality and its variability, the attentive and optimized design of water processes, and the wise management of water sources and treatment processes. To correctly tackle these aspects, specific tools could be helpful; however, the application of the appropriate tools to support DWS managers and engineers is not a well-established practice, being support tools mainly reported in literature for research applications or to develop participatory processes for intervention planning.

The present PhD thesis focuses on the application of appropriate support tools in DWS planning, design and management. Three main engineering tasks have been considered: (i) the acquisition of information about source water quality and its variability, (ii) the design of treatment processes, (iii) the conduction and interpretation of pilot studies. Support tools available to these purposes have been identified together with main weaknesses limiting their applicability (Chapter 1).

The research work considered the DWS system of the city of Milan as a case study, and tackled each one of the above mentioned engineering tasks, proposing and evaluating appropriate support tools and improvements to face identified weaknesses. The research work is presented in three thematic chapters, each one intended for a publication on international peer-reviewed scientific journal. For this reason, each chapter reports the research work through an introduction on the topic and current advances in literature, the definition of objectives and applied methodologies, the presentation and discussion of results and the summary of achievements.

Firstly, the evaluation of source water quality and its variability has been considered in Chapter 3. Techniques of multivariate statistics were applied as appropriate support tools, namely, Principal Component Analysis (PCA) and Factor Analysis (FA). The application of these techniques is well established in the literature only for the characterization of water bodies; no reference has been found for multivariate statistics as an useful tool for DWS engineers in planning water resource exploitation and DWS treatment infrastructure. Furthermore, the interpretation of results is often poor, especially for what concerns the evaluation of the two distinct component of variability (spatial and temporal). Through the work presented in Chapter 3, a paradigm is proposed for the detailed study of FA regression scores, and appropriate techniques are applied for the evaluation of spatial and temporal components of variability. The analysis has been performed in a DWS design and management perspective: the improved interpretation of FA results coupled with the geo-referentiation of data through GIS maps has been proposed as a tool for DWS system planning, design and management. Indications for the optimization of water quality monitoring

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plan, source water exploitation and treatment train planning have been derived. In particular, an upcoming groundwater pollution by nitrate has been characterised, asking for a dedicated treatment.

To treat nitrate pollution a biological denitrification process has been chosen as interesting technological alternative mainly because of its low management costs, compared to reverse osmosis separation. Beside economic advantages, typical process weaknesses are a high sensitivity to feeding and operating condition variations, that can invalidate process design effectiveness, as well as possible inhibition mechanisms that organic and inorganic co-pollutant can exert on the denitrifying biomass. For these reasons carrying on a pilot scale test is a good practice for the feasibility study of a biodenitrification unit.

Thus, in the second instance, the present work considers the design of a pilot scale biodenitrification unit. The design depends on multiple objectives, often conflicting, as required water quality and treatment efficiency, economic feasibility, or other construction and operational constraints imposed by consideration of the whole treatment train. The choice of the best design solution involves trade-offs. Various multi-objective optimization (MOO) methods are available for trade-offs assessment and optimal design identification. However, neither the criteria adopted for the choice of the MOO method, nor a comparison of efficacy of different mathematical methods, are usually reported in literature. The work presented in Chapter 4 shows how MOO can effectively support the design of a water treatment process and compares two different MOO methods highlighting specific advantages and drawbacks, looking for the most appropriate tool for the optimization of the biofilter design, simulated through a model derived from literature.

Then, an up-flow submerged biofilter was managed at pilot scale for about 3 months. Monitoring data were used for the validation of optimization outputs. At the same time the pilot scale test has been used to study process applicability and efficacy depending on feeding and operating conditions.

In literature, some laboratory or pilot scale studies are reported on heterotrophic denitrification, mainly evaluating the effects of various parameters on nitrate removal efficiency. However, in DWS applications, the evaluation of nitrate removal efficiency has to be completed by other two aspects: the completeness of denitrification reactions and the risk of bacterial regrowth in distribution network, as nitrous nitrogen, organic carbon and biomass concentrations in the treated water have important sanitary drawbacks and are not tolerated. The use of simulation models can then support treatment process management, optimizing its efficacy. In this case a model structure has to be defined, usually choosing among complex biofilm models and more simple models able to consider only removal kinetics. Chapter 5 presents a study based on the pilot scale biofilter experiments, that evaluates the effects of both operating and design parameters on denitrification efficacy from all mentioned points of view. It also proposes a simple simulation model, with a little number of parameters to be calibrated, but able to predict both removal efficiency and biomass growth. Based on pilot scale data, kinetic parameters were calibrated and from their validation, indications for model improvement have been suggested.

Presented doctoral research has been conducted in collaboration with and funded by Metropolitana Milanese s.p.a., that is responsible for the design, construction and management of the drinking water system supplying Milan city urban area. The PhD project has also involved the collaboration with the Industrial Optimization research group (Department of Mathematical Information Technology) of the University of Jyväskylä (Finland), through a period of 3½-months of international mobility.

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Chapter 3

3. Data mining of groundwater quality profiles as a support tool for

planning and management of drinking water supply systems

3.1 Abstract:

The present study shows the effectiveness of multivariate statistic techniques such as Factor Analysis (FA) and Cluster Analysis (CA), not only as unbiased methods to study patterns in a water quality database, but also as a support tool for DWS system planning and management. It considers groundwater quality data from the monitoring dataset of Milan city Drinking Water Supply (DWS) system composed of 88 water quality parameters monitored in 522 wells for 6 years. Its complexity has been faced applying FA as dimension reduction tool, extracting 16 significant factors (58% of the total variance explained) and recognizing groundwater quality variations of natural origin as well as contamination plumes of anthropic origin. The variability of factor scores has been further analysed: (i) in space, through inference tests and CA, and (ii) in time, through correlation and linear regression. Performed analysis, together with geo-referentiation of statistical results, allowed a deeper interpretation of factor scores that led to useful indication for DWS system planning and management. Looking for an improved economic sustainability of the DWS system, an optimization of the monitoring plan has been proposed, identifying the most significant parameters to be monitored (indicator parameters), and the most representative sampling points for each DWS unit. Furthermore, an appropriate wells management logic has been suggested to satisfy basal demand and peaks minimizing pollutants load to be treated by DWS units. Finally, some upcoming treatment targets have been identified, with indications about treatments to be foreseen and the right time to plan interventions.

The research work described in the present chapter was carried out with the valuable support of: Dr. Arianna Azzellino, Research fellow, Politecnico di Milano, Italy.

The research work has been carried out on groundwater quality data provided by Metropolitana Milanese spa, responsible of the drinking water supply of Milan city.

The present chapter, after a further internal review and an adaptation in terms of formatting style, will be submitted for publication to the journal “Environmental Monitoring and Assessment”.

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3.2 Introduction

Planning and management of Drinking Water Supply (DWS) systems can be a complex task, depending on the complexity of the context. Metropolitan areas are industrialized and densely populated, so that water supply is assured through multiple DWS units that intercept various punctual and diffuse pollution plumes, and have to be managed in order to satisfy a relevant water demand, through highly interconnected water supply systems.

In these conditions, an efficient DWS system requires the wise management of the water sources and treatment processes, whose starting point is the deep knowledge of raw water quality and its variability. To this scope, collected water quality has to be extensively monitored, often involving relevant analytical costs and generating a huge dataset (Selle et al., 2013), which is not always properly built or exploited. The monitoring data analysis can thus be difficult and the adoption of appropriate statistical tools is advisable. In literature different statistical tools are reported for the analysis of water quality data: the simplest approaches apply descriptive statistical analyses combined with some hypothesis testing (Kats et al., 2011; Moreau-Fournier and Daughney, 2012; Schot and Pieber, 2012), while more complex contexts are faced through both interpretative models (Ammar et al., 2011; Masetti et al., 2009; Stigter et al., 2008; Starn et al., 2014) and data driven techniques (Olsen et al., 2012; Perumal et al., 2015). Interpretative models require the a priori definition of relationships among variables, as the identification of dependent and independent variables in the model formulation, and imply the need of a validation phase to assess model reliability, as reported by Sorichetta et al. (2011, 2012). Data driven techniques such as Principal Components Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA), allow to overcome these problems: their strength relies on the ability to study patterns in databases and to derive hidden information directly from the dataset (Papaioannou et al., 2010). For this reason they are reported to be effective unbiased methods to extract meaningful information from water quality data (Gourdol et al., 2013), even if Selle et al. (2013) report a potential weakness in the interpretation of the outputs. In fact, they stress that the analysis of the PCA results (regression scores) has been often limited to their projection on bi-plots of the first two components, while a more detailed analysis of scores is often missing.

Anyway, the efficacy of multivariate statistics for water quality assessment and for the identification of its link to environmental factors and contamination sources is diffusely reported in literature (Yidana, 2010;

Mendizabal et al., 2011; Olsen et al., 2012; Gourdol et al., 2013; Selle et al., 2013; Narany et al., 2014; Yu et al., 2014). As a reference, Olsen et al. (2012) have recently reviewed 49 papers dealing with PCA technique to evaluate watershed water quality, from datasets of 6 to 36 variables. At the same time, some references can be found on the application of these techniques for the optimization of monitoring plans (e.g. Papaioannou et al., 2010; Page et al., 2012; Guigues et al., 2013). However, no reference has been found for multivariate statistics application as useful tool to support DWS managers in planning water resource exploitation and DWS treatment infrastructure.

The present study shows how the application of multivariate statistics on water quality data can be an appropriate tool to support DWS planning and management, especially in complex contexts. As a case study, it considered the DWS system of Milan city, one of the most industrialized and densely populated urban areas in Europe. The analysed dataset consisted of about 100 water quality parameters, much more than what previously considered in literature (Olsen et al., 2012). The higher number of variables required a more attentive analysis of output results, and CA and regression techniques have been applied evaluating the spatial and temporal variability of factor scores. The paper illustrates applied techniques, obtained results and the indications that could be derived on water quality monitoring, raw water exploitation and treatment process upgrade to support DWS planning and management.

Advanced methods for the support of planning, design and management of drinking water supply systems