This work investigates the problem of vehicle routing and assessment of road network vulnerability during extreme flood conditions by considering inundation levels and their effects on road availability and travel speeds. A stormwater model is used to estimate street-level inundation during three major hurricanes that passed through Wilmington, North Carolina-Florence, Matthew, and Dorian-to determine how different storms can affect the road network system. Toward this, an optimization model was developed that uses flood versus speed reduction functions to integrate flood levels to estimate decreasing the number of available arcs and reducing the interconnectivity of different regions over time. A comparison of the road network analysis using flood estimates from the stormwater model and the 100-year flood maps from the Federal Emergency Management Agency of the United States was performed, and overall results indicate the importance of storm-specific flood estimates to assist emergency planners in the definition of critical roads and affected areas. The paper also explores how different regions are affected during various flood severity levels and identifies areas of vulnerability. It introduces severity scenarios based on route feasibility, shedding light on the dynamic nature of flood impact, and concludes by highlighting the influence of flood changes on the importance of specific road segments in network planning and operation.

Existing reservoir management frameworks traditionally consider historical (predam) flow conditions to deliver environmental flows. Such frameworks may not be feasible because current demand and/or climate could be different from predam conditions. Hence, we developed a multireservoir framework that explicitly considers both human water demands and environmental flow requirements to minimize deviations under current hydroclimatic conditions and demand patterns. The multireservoir framework, Generalized Reservoir Analyses using Probabilistic Streamflow (GRAPS), was modified and implemented to solve the problem of minimizing the flow deviations using feasible sequential quadratic programming for three reservoirs in the Chattahoochee River Basin, Southeastern United States, which is known for its imperiled native biodiversity and productive estuarine ecosystem. Our results show that downstream reservoirs in the cascade system are less influenced by upstream reservoirs' regulation because the downstream reservoirs receive a significant amount of natural flows. By comparing the average wavelet power spectrum at different periodicities between natural flows and downstream releases, we found that the current release policy and modified releases resulted in highly altered flows under shorter periodicities (e.g., less than 2 months) but synchronized flow variance between natural flow and downstream releases at longer periodicities (e.g., greater than 3 years). This framework of linking the multireservoir allocation model through the time-frequency analysis using wavelet power spectrum could not only advance sustainable water management policies to meet water for human and environmental needs but can also add additional value in meeting the downstream environmental demand at desired periodicities.

The effectiveness of model-based leak localization methods in water distribution systems (WDSs), including optimization-based and machine learning approaches, significantly depends on the quality and quantity of input data. Pressure data, easily accessible due to nonintrusive sensor installation and maintenance, are commonly used. However, economic constraints limit the number of sensors in WDSs, highlighting the need for strategic sensor placement to enhance data quality. This study introduces a novel, method-independent sensor placement strategy that integrates cluster definitions (leak resolution) with intuitive surrogates for localization performance, addressing the limitations of existing methods reliant on complex, nonintuitive metrics. We propose the Euclidean cluster-based optimal placement of sensors (ECOPS) approach, which employs sensitivity and uniqueness as fundamental signal properties to guide sensor placement. Validation tests within a comprehensive real-world WDS demonstrate that ECOPS outperforms existing surrogate-based approaches and improves the performance of current sensors installed for leak characterization. These findings provide compelling evidence of ECOPS's potential for enhancing pressure sensor placement, thereby improving leak localization in WDS applications.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models-multilayer perceptron (MLP) and convolutional neural network (CNN)-are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP.

Water distribution systems (WDSs) face a significant challenge in the form of pipe leaks. Pipe leaks can cause loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. Locating pipe leaks has been a constant challenge for water utilities and stakeholders due to the underground location of the pipes. Physical methods to detect leaks are expensive, intrusive, and heavily localized. Computational approaches provide an economical alternative to physical methods. Data-driven machine learning-based computational approaches have garnered growing interest in recent years to address the challenge of detecting pipe leaks in WDSs. While several studies have applied machine learning models for leak detection on single pipes and small test networks, their applicability to the real-world WDSs is unclear. Most of these studies simplify the leak characteristics and ignore modeling and measuring device uncertainties, which makes the scalability of their approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases that account for the realistic leak characteristics (multiple, multi-size, and randomly located leaks) and incorporating noise in the input data to account for the model- and measuring device- related uncertainties. A machine learning-based approach that uses simulated pressure as input to predict both location and size of leaks is proposed. Two different machine learning models: Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), are trained and tested for the four study cases, and their performances are compared. The precision and recall results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN generally outperforming MLP.

The primary goal of the project is to leverage recent developments in smart water technologies to detect and reduce water leakages in large water distribution networks with the aid of neural networks. A cost effective, non-invasive solution to detect leakages in transmission pipelines is needed by many water utilities, as it will lead to significant water savings and reduced pipe breakage frequencies, especially in older infrastructure systems. The eventual goal of the project is to test the ANN model on a real network using field measured pressure and pipe breakage data after tuning and developing the model with simulated data. In this project, we propose building a regression model, based on multi-layer perceptron (MLP) algorithm, which is a class of feedforward artificial neural networks (ANNs) to detect the leak locations within a proposed network. The model should be able to learn the structure, i.e., mapping of various leak nodes and sensor nodes in an area, such that it can detect the leak nodes based on the pressure values with significant accuracy.

The authors previously analysed a real-world solid waste management (SWM) system using the solid waste optimization life-cycle framework (SWOLF) to identify optimal SWM strategies that meet modelled objectives (e.g.cost, environmental impacts, landfill diversion). While mathematically optimal strategies can support SWM decision making, they may not be readily implementable because of unmodelled objectives (e.g.practical limitations, social preferences, political and management considerations). A mathematical programming technique extending SWOLF is used to systematically identify, for several scenarios, different 'optimal' SWM strategies that are maximally different from each other in terms of waste flows, while meeting modelled objectives and constraints. The performance with respect to unmodelled issues was analysed to demonstrate the flexibility in potential strategies. Practitioner feedback highlighted implementation challenges due to existing practices; however, insights gained from this exercise led to more plausible and acceptable strategies by incrementally modifying the initial SWM alternatives generated.

Policies and regulations such as Extended Producer Responsibility (EPR) have been implemented to potentially increase the recycling rate of electronic waste (e-waste), but the cost and environmental impacts of associated collection, transportation, material recovery, material re-processing, and disposal could outweigh the benefits of recycling if the e-waste management system is not effectively designed and implemented. This paper presents a quantitative, holistic framework to systematically estimate life-cycle impacts and costs associated with e-waste management. This new framework was tested using data from the state of Washington's EPR program to represent e-waste collection, transportation, processing and disposal. Sensitivity of process-level life-cycle model outputs to parameter and input variability was also conducted. Drop-off using fossil-fuel-powered personal vehicles was found to be a key contributor to cost and carbon dioxide emissions. Decision-makers must account for drop-off and consider the feasibility of alternate e-waste aggregation strategies to ensure life-cycle benefits of e-waste recycling are maximized.

Solid waste management (SWM) is a key function of local government and is critical to protecting human health and the environment. Development of effective SWM strategies should consider comprehensive SWM process choices and policy implications on system-level cost and environmental performance. This analysis evaluated cost and select environmental implications of SWM policies for Wake County, North Carolina using a life-cycle approach. A county-specific data set and scenarios were developed to evaluate alternatives for residential municipal SWM, which included combinations of a mixed waste material recovery facility (MRF), anaerobic digestion, and waste-to-energy combustion in addition to existing SWM infrastructure (composting, landfilling, single stream recycling). Multiple landfill diversion and budget levels were considered for each scenario. At maximum diversion, the greenhouse gas (GHG) mitigation costs ranged from 30 to 900 $/MTCO2e; the lower values were when a mixed waste MRF was used, and the higher values when anaerobic digestion was used. Utilization of the mixed waste MRF was sensitive to the efficiency of material separation and operating cost. Maintaining the current separate collection scheme limited the potential for cost and GHG reductions. Municipalities seeking to cost-effectively increase landfill diversion while reducing GHGs should consider waste-to-energy, mixed waste separation, and changes to collection.

Building performance simulation [BPS] tools are important in all design stages. However, barriers such as time, resources, and expertise inhibit their use in the early design stages. This study aims to develop, as part of decision -support framework, metamodels to assess the thermal discomfort in a naturally ventilated Brazilian low-cost house during early design. The metamodels predict the degree -hours of discomfort by heat and/or by cold as a function of design parameters for three Brazilian cities: Curitiba, Sao Paulo, and Manaus. The key design parameters, related with passive design strategies, are building orientation, shading devices position and dimensions, thermal properties of the walls and roof, window-to-wall ratio, and effective window ventilation area. The method consists of three main stages: (i) baseline model development; (ii) Monte Carlo simulation; (iii) multivariate regression. Overall, the metamodels showed R-2 values higher than 0.95 for all climates, except the ones predicting discomfort by heat for Curitiba (R-2 =0.61) and Sao Paulo (R-2 =0.75). The proposed metamodels can quickly and accurately assess the thermal performance of naturally ventilated low-cost houses. They can be used to guide professionals during the early design stages, and for educational purposes in building design pedagogy. (C) 2019 Elsevier B.V. All rights reserved.

The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic performance of microalgae-based biofuel production. Models that estimate microalgal growth under different conditions can help to optimize PBR design and operation. To be effective, the growth parameters used in these models must be accurately determined. Algal growth experiments are often constrained by the dynamic nature of the culture environment, and control systems are needed to accurately determine the kinetic parameters. The first step in setting up a controlled batch experiment is live data acquisition and monitoring. This protocol outlines a process for the assembly and operation of a bench-scale photosynthetic bioreactor that can be used to conduct microalgal growth experiments. This protocol describes how to size and assemble a flat-plate, bench-scale PBR from acrylic. It also details how to configure a PBR with continuous pH, light, and temperature monitoring using a data acquisition and control unit, analog sensors, and open-source data acquisition software.

Most reservoir operation practices consider downstream environmental flow as a constraint to meet a minimum release. The resulting flow regime may not necessarily provide downstream aquatic conditions to support healthy ecosystems. These effects can be quantified in terms of changes in values of parameters that represent the flow regimes. Numerous studies have focused on determining the ecological response to hydrological alteration caused by reservoir operation. To mitigate hydrological alteration and restore the natural flow regime as much as possible, a reservoir operation framework is proposed to explicitly incorporate ecological flow requirements. A general optimization-based decision model is presented to consider simultaneously the multiple anthropogenic uses of the reservoir and desirable ecological releases represented by parameters that capture the flow regime. Multiple uses of the reservoir, including water supply, hydropower generation, etc., are modeled as a mixed integer programming problem. Hydropower generation, which is represented by a nonlinear function that usually depends on head and water flow, is linearized using a two-dimensional function. Investigations using a reservoir in Virginia, located in the southeastern United States, demonstrate that compared to standard releases based on current operation practice, releases simulated using this framework perform better in mimicking pre-development flows. The tradeoff between anthropogenic use and ecological releases is investigated. The framework is first demonstrated for instances with perfect stream flow information. To examine the flexibility of this framework in reservoir release management, monthly flow forecasts and disaggregated daily flow conditions are incorporated. Retrospective monthly flow forecasts are obtained through regression models that use gridded precipitation forecasts and gridded soil moisture estimates as predictors. A nonparametric method is chosen to disaggregate monthly flow forecasts to daily flow conditions. Compared with daily flow climatology, forecasted monthly and daily flow better preserves flow variability and result in lower changes of flow parameters under the proposed framework. (C) 2015 Elsevier Ltd. All rights reserved.

•Apply a data driven approach to identify low frequency on hydroclimatic data.•Use a conceptual model to study how land surface modulates frequency in streamflow.•Oscillations account higher percentage of variance for larger basins.•Frequencies in streamflow are usually less than that of precipitation.

This paper discusses the impact of thermal zone modeling on the thermal comfort prediction during the early design of naturally ventilated buildings within four Brazilian cities. A representative low-cost Brazilian house with four rooms and an area of approximately 51 m 2 is simulated. Two zoning approaches are analyzed and the results show small differences between their thermal condition predictions.