Future climatic, demographic, technological, urban and socio-economic challenges call for more flexible and sustainable wastewater infrastructure systems. Exploratory modelling can help to investigate the consequences of these developments on the infrastructure. In order to explore large numbers of adaptation strategies, we need to re-balance the degree of realism of sewer network and ability to reflect key performance characteristics against the model's parsimony and computational efficiency. We present a spatially explicit algorithm for creating sanitary sewer networks that realistically represent key characteristics of a real system. Basic topographic, demographic and urban characteristics are abstracted into a squared grid of ‘Blocks’ which are the foundation for the sewer network's topology delineation. We compare three different pipe dimensioning approaches and found a good balance between detail and computational efficiency. With a basic hydraulic performance assessment, we demonstrate that we attain a computationally efficient and high-fidelity wastewater sewer network with adequate hydraulic performance. A spatial resolution of 250 m Block size in combination with a sequential Pipe-by-Pipe (PBP) design algorithm provides a sound trade-off between computational time and fidelity of relevant structural and hydraulic properties for exploratory modelling. We can generate a simplified sewer network (both topology and hydraulic design) in 18 s using PBP, versus 36 min using a highly detailed model or 1 s using a highly abstract model. Moreover, this simplification can cut up to 1/10th to 1/50th the computational time for the hydraulic simulations depending on the routing method implemented. We anticipate our model to be a starting point for sophisticated exploratory modelling into possible infrastructure adaptation measures of topological and loading changes of sewer systems for long-term planning.@en

We explore the dynamics of centralised and decentralised wastewater infrastructure across various scenarios and introduce novel insights into their performance regarding structural vulnerability, hydraulic capacity, and costs. This study determines circumstances under which infrastructure hybridisation outperforms traditional centralised infrastructure paradigms. We combined system analysis to map out the modelling problem with the model-based exploration of the transition space using the novel TURN-Sewers model. System diagramming was used to identify the parameters or combinations of parameters that significantly influence the performance indicators being assessed. This allowed the creation of relevant simulation scenarios to identify circumstances where a decentralised sewer system could outperform a centralised one. TURN-Sewers was applied to model the infrastructure maintenance and generation of new infrastructure over 20 years for a municipality on the Swiss Plateau, considering a population growth rate of 0.03 a−1. Results show that decentralisation in expansion areas with higher densification can outperform the hydraulic performance and structural vulnerability of expanding centralised sanitary wastewater infrastructure. Decentralised systems can also offer economic advantages when capital expenditure costs for small-scale wastewater treatment plants are significantly reduced compared to current costs, particularly at higher discount rates, e.g. reaping effects of economies of scale. The findings of this study emphasise the potential of transition pathways towards decentralisation in urban water infrastructures and the value of models that allow the exploration of this transition space.@en

Struvite crystallisation is a fast and reliable phosphorus removal and recovery process for concentrated waste streams - such as hydrolysed human urine. In order to optimise P-elimination efficiency, it is beneficial to obtain larger particle sizes: they are easier to separate and less prone to wash-out than smaller particles. This paper presents the results of a study on the effect of process parameters on particle size in a single step struvite precipitation. Crystals formed in batch experiments with real hydrolysed urine were shown to have an average size of >90 μm at pH 9 and 20 °C. This is reduced to 45 μm when changing stirrer type. Particle size increases with lower supersaturation. The results showed that under otherwise constant conditions, particle size decreases with lower temperature and has a minimum between pH 9 and 10. Deviating trends are observed at pH <8. Struvite formation in a CSTR (continuously stirred tank reactor) process was shown to be a reliable stable process that does not require any pH control. A method based on conductivity measurement is presented to estimate ionic strength, which is needed for equilibrium calculations.@en

Struvite crystallisation is a fast and reliable phosphorus removal and recovery process for concentrated waste streams - such as hydrolysed human urine. In order to optimise P-elimination efficiency, it is beneficial to obtain larger particle sizes: they are easier to separate and less prone to wash-out than smaller particles. This paper presents the results of a study on the effect of process parameters on particle size in a single step struvite precipitation. Crystals formed in batch experiments with real hydrolysed urine were shown to have an average size of >90 μm at pH 9 and 20 °C. This is reduced to 45 μm when changing stirrer type. Particle size increases with lower supersaturation. The results showed that under otherwise constant conditions, particle size decreases with lower temperature and has a minimum between pH 9 and 10. Deviating trends are observed at pH <8. Struvite formation in a CSTR (continuously stirred tank reactor) process was shown to be a reliable stable process that does not require any pH control. A method based on conductivity measurement is presented to estimate ionic strength, which is needed for equilibrium calculations.@en

Struvite crystallisation is a fast and reliable phosphorus removal and recovery process for concentrated waste streams - such as hydrolysed human urine. In order to optimise P-elimination efficiency, it is beneficial to obtain larger particle sizes: they are easier to separate and less prone to wash-out than smaller particles. This paper presents the results of a study on the effect of process parameters on particle size in a single step struvite precipitation. Crystals formed in batch experiments with real hydrolysed urine were shown to have an average size of >90 μm at pH 9 and 20 °C. This is reduced to 45 μm when changing stirrer type. Particle size increases with lower supersaturation. The results showed that under otherwise constant conditions, particle size decreases with lower temperature and has a minimum between pH 9 and 10. Deviating trends are observed at pH <8. Struvite formation in a CSTR (continuously stirred tank reactor) process was shown to be a reliable stable process that does not require any pH control. A method based on conductivity measurement is presented to estimate ionic strength, which is needed for equilibrium calculations.@en

Struvite crystallisation is a fast and reliable phosphorus removal and recovery process for concentrated waste streams - such as hydrolysed human urine. In order to optimise P-elimination efficiency, it is beneficial to obtain larger particle sizes: they are easier to separate and less prone to wash-out than smaller particles. This paper presents the results of a study on the effect of process parameters on particle size in a single step struvite precipitation. Crystals formed in batch experiments with real hydrolysed urine were shown to have an average size of >90 μm at pH 9 and 20 °C. This is reduced to 45 μm when changing stirrer type. Particle size increases with lower supersaturation. The results showed that under otherwise constant conditions, particle size decreases with lower temperature and has a minimum between pH 9 and 10. Deviating trends are observed at pH <8. Struvite formation in a CSTR (continuously stirred tank reactor) process was shown to be a reliable stable process that does not require any pH control. A method based on conductivity measurement is presented to estimate ionic strength, which is needed for equilibrium calculations.@en

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.@en

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.@en

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.@en

Separating urine from wastewater at the source reduces the costs of extensive wastewater treatment. Recovering the nutrients from urine and reusing them for agricultural purposes adds resource saving to the benefits. Phosphate can be recovered in the form of struvite (magnesium ammonium phosphate). In this paper, the behaviour of pharmaceuticals and heavy metals during the precipitation of struvite in urine is studied. When precipitating struvite in urine spiked with hormones and non-ionic, acidic and basic pharmaceuticals, the hormones and pharmaceuticals remain in solution for more than 98%. For heavy metals, initial experiments were performed to study metal solubility in urine. Solubility is shown to be affected by the chemical conditions of stored and therefore hydrolysed urine. Thermodynamic modelling reveals low or very low equilibrium solute concentrations for cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni) and lead (Pb). Experiments confirmed Cd, Cu and Pb carbonate and hydroxide precipitation upon metal addition in stored urine with a reaction half-life of ca. 7 days. For all metals considered, the maximum specific metal concentrations per gram phosphate or nitrogen showed to be typically several orders of magnitudes lower in urine than in commercially available fertilizers and manure. Heavy metals in struvite precipitated from normal stored urine could not be detected. Phosphate recovery from urine over struvite precipitation is shown to render a product free from most organic micropollutants and containing only a fraction of the already low amounts of heavy metals in urine.@en

Struvite (MgNH4PO4·6H2O) precipitation eliminates phosphate efficiently from urine, a small but highly concentrated stream in the total flux of domestic wastewater. Precipitation experiments with hydrolysed urine evaluated the solubility product of struvite. The stored and fully hydrolysed urine had an ionic strength of between 0.33 and 0.56 M and required the estimation of activity coefficients. From our data, we identified the Davies approximation with the two constants A=0.509 and B=0.3 as agreeing best with our laboratory results. The standard solubility product Ks0=f1[NH4+]f 2[Mg2+]f3[PO43-] ([ ]=concentration of the species; fx=corresponding activity coefficient) of struvite in urine was found to be 10-13.26±0.057 at 25 °C and the enthalpy of struvite formation ΔH was 22.6(±1.1) kJ mol-1. The equilibrium calculations required the following dissolved complexes: [MgCO3]aq, [MgHCO3]+, [MgPO4]-, [NH4HPO4]-, and [NaHPO4]- and to a lesser extent [MgSO4]aq and [NH4SO4]-. Organic complexes do not seem to influence the solubility product substantially. For practical purposes, a conditional solubility product Kscond=[Mgaq]·[NH4 ++NH3]·[Portho]=10-7.57 M3 was derived to calculate struvite solubility in urine at 25 °C, pH=9.0 and ionic strength I=0.4 M directly from measured concentrations.@en

The presented approach aims to overcome the scarce data problem in service life modeling of water networks by combining subjective expert knowledge and local replacement data. A procedure to elicit imprecise quantile estimates of survival functions from experts, considering common cognitive biases, was developed and applied. The individual expert priors of the parameters of the service life distribution are obtained by regression over the stated distribution quantiles and aggregated into a single prior distribution. Furthermore, a likelihood function for the commonly encountered censored and truncated pipe replacement data is formulated. The suitability of the suggested Bayesian approach based on elicitation data from eight experts and real network data is demonstrated. Robust parameter estimates could be derived in data situations where frequentist maximum likelihood estimation is unsatisfactory, and to show how the consideration of imprecision and in-between-variance of experts improves posterior inference.@en

The presented approach aims to overcome the scarce data problem in service life modeling of water networks by combining subjective expert knowledge and local replacement data. A procedure to elicit imprecise quantile estimates of survival functions from experts, considering common cognitive biases, was developed and applied. The individual expert priors of the parameters of the service life distribution are obtained by regression over the stated distribution quantiles and aggregated into a single prior distribution. Furthermore, a likelihood function for the commonly encountered censored and truncated pipe replacement data is formulated. The suitability of the suggested Bayesian approach based on elicitation data from eight experts and real network data is demonstrated. Robust parameter estimates could be derived in data situations where frequentist maximum likelihood estimation is unsatisfactory, and to show how the consideration of imprecision and in-between-variance of experts improves posterior inference.@en

The presented approach aims to overcome the scarce data problem in service life modeling of water networks by combining subjective expert knowledge and local replacement data. A procedure to elicit imprecise quantile estimates of survival functions from experts, considering common cognitive biases, was developed and applied. The individual expert priors of the parameters of the service life distribution are obtained by regression over the stated distribution quantiles and aggregated into a single prior distribution. Furthermore, a likelihood function for the commonly encountered censored and truncated pipe replacement data is formulated. The suitability of the suggested Bayesian approach based on elicitation data from eight experts and real network data is demonstrated. Robust parameter estimates could be derived in data situations where frequentist maximum likelihood estimation is unsatisfactory, and to show how the consideration of imprecision and in-between-variance of experts improves posterior inference.@en

Predictions of the expected number of failures of water distribution network pipes are important to develop an optimal management strategy. A number of probabilistic pipe failure models have been proposed in the literature for this purpose. They have to be calibrated on failure records. However, common data management practices mean that replaced pipes are often absent from available data sets. This leads to a 'survival selection bias', as pipes with frequent failures are more likely to be absent from the data.To address this problem, we propose a formal statistical approach to extend the likelihood function of a pipe failure model by a replacement model. Frequentist maximum likelihood estimation or Bayesian inference can then be applied for parameter estimation. This approach is general and is not limited to a particular failure or replacement model.We implemented this approach with a Weibull-exponential failure model and a simple constant probability replacement model. Based on this distribution assumptions, we illustrated our concept with two examples. First, we used simulated data to show how replacement causes a 'survival selection bias' and how to successfully correct for it. A second example with real data illustrates how a model can be extended to consider covariables.@en

Predictions of the expected number of failures of water distribution network pipes are important to develop an optimal management strategy. A number of probabilistic pipe failure models have been proposed in the literature for this purpose. They have to be calibrated on failure records. However, common data management practices mean that replaced pipes are often absent from available data sets. This leads to a 'survival selection bias', as pipes with frequent failures are more likely to be absent from the data.To address this problem, we propose a formal statistical approach to extend the likelihood function of a pipe failure model by a replacement model. Frequentist maximum likelihood estimation or Bayesian inference can then be applied for parameter estimation. This approach is general and is not limited to a particular failure or replacement model.We implemented this approach with a Weibull-exponential failure model and a simple constant probability replacement model. Based on this distribution assumptions, we illustrated our concept with two examples. First, we used simulated data to show how replacement causes a 'survival selection bias' and how to successfully correct for it. A second example with real data illustrates how a model can be extended to consider covariables.@en

Predictions of the expected number of failures of water distribution network pipes are important to develop an optimal management strategy. A number of probabilistic pipe failure models have been proposed in the literature for this purpose. They have to be calibrated on failure records. However, common data management practices mean that replaced pipes are often absent from available data sets. This leads to a 'survival selection bias', as pipes with frequent failures are more likely to be absent from the data.To address this problem, we propose a formal statistical approach to extend the likelihood function of a pipe failure model by a replacement model. Frequentist maximum likelihood estimation or Bayesian inference can then be applied for parameter estimation. This approach is general and is not limited to a particular failure or replacement model.We implemented this approach with a Weibull-exponential failure model and a simple constant probability replacement model. Based on this distribution assumptions, we illustrated our concept with two examples. First, we used simulated data to show how replacement causes a 'survival selection bias' and how to successfully correct for it. A second example with real data illustrates how a model can be extended to consider covariables.@en

Predictions of the expected number of failures of water distribution network pipes are important to develop an optimal management strategy. A number of probabilistic pipe failure models have been proposed in the literature for this purpose. They have to be calibrated on failure records. However, common data management practices mean that replaced pipes are often absent from available data sets. This leads to a 'survival selection bias', as pipes with frequent failures are more likely to be absent from the data.To address this problem, we propose a formal statistical approach to extend the likelihood function of a pipe failure model by a replacement model. Frequentist maximum likelihood estimation or Bayesian inference can then be applied for parameter estimation. This approach is general and is not limited to a particular failure or replacement model.We implemented this approach with a Weibull-exponential failure model and a simple constant probability replacement model. Based on this distribution assumptions, we illustrated our concept with two examples. First, we used simulated data to show how replacement causes a 'survival selection bias' and how to successfully correct for it. A second example with real data illustrates how a model can be extended to consider covariables.@en