Calabuig, Pablo, Calabuig

Calabuig, Pablo

Pablo is the CEO at GoAigua Inc, the US subsidiary of the water utility Group Global Omnium headquartered in Valencia (Spain). He graduated in Civil and Environmental Engineer at the Polytechnic University of Valencia and UC Berkeley, and after working in Valencia’s utility as an Engineer he did his MBA at IESE Business School. He then joined the consulting firm McKinsey & Company, where he served the Madrid, Mexico City and New York Offices. After six years, he recently rejoined his family business to lead its digital expansion in North America.

Titles from this speaker

Description: Houston Wastewater Utility Perspective: Balanced O&M and CIP Budget with...

Houston Wastewater Utility Perspective: Balanced O&M and CIP Budget with Projected Outlook of Risk and Level of Service is Now a Reality

Abstract

Objectives & status: The City of Houston Public Works (HPW) has a sewer network that has a long-standing history of experiencing Sanitary Sewer Overflows (SSO/SSOs). A majority of the Public SSOs are caused by Fats, Oils, and Greases (FOG) that accumulate in the sewer system, restricting the flow rate of the pipe and causing upstream assets to overflow. It is estimated that roughly 70% of these SSOs would be preventable (Image 1) if the City focuses its O&M and CIP resources towards the key factors (or root-causes) of these dry weather SSOs. In the past years, to combat this issue HPW performed a mostly reactive approach, deploying cleaning and inspection resources when a service request was generated after SSOs were notified.
The objective of this project is threefold:
1. Quantify asset-level risk of SSOs for the entire city with non-traditional data science techniques.
2. Identify root causes of SSOs and their individual effect in each asset's risk level.
3. Ultimately find the balance between O&M and CIP Budget, as well as the right initiatives to minimize the risk of SSOs under the consent decree's requirements and in a proactive manner.
The City of Houston started this project in 2020 with the support of GoAigua, which provided the data analysis and software platform for the generation of insights. After piloting the methodology in a group of 13 lift station sewersheds, the methodology has been scaled to the full city and will be rolled out soon.
Methodology/ approach: The project's unique methodology combined a traditional asset management approach with machine learning techniques for the quantification of risk and root-cause analysis of SSOs and FOGs generation: First, HPW and GoAigua developed a methodology to quantify the asset-level risk and consequence of SSOs. For this methodology HPW tested different machine learning algorithms, and eventually selected a random-forest technique. This allowed them to identify all root causes ultimately affecting the generation of SSOs and quantify their effect on the risk to SSO. Some of the key factors include presence of multifamily units, grease generator establishments (e.g., restaurants), average flow velocity, material of pipes, etc. (See Image 2) Once all factors were assessed at an asset, sewer basin and City level, HPW could evaluate potential risk mitigation strategies to minimize the occurrence of SSOs â€“ both O&M initiatives, and CIP ones.
Finally, all these initiatives were piloted and evaluated individually in three selected sewersheds (see Image 3) with a high level of SSOs, and different root causes affecting them. The evaluation was done along two key dimensions: cost/ investment, and benefit/efficacy, which allowed us to prioritize and select them for a larger deployment at a City level. Key initiatives included: public awareness campaigns for grease generator establishments, increased control over grease traps, increased preventative maintenance/ cleaning in risky assets (higher frequency), investment in IoT sensors and real-time surveillance. Findings and significance: Our presentation showcases how the City of Houston was able to leverage their AIAP platform along with machine learning techniques with near real-time and other data sets, both form the Public Works Department and from other Departments (e.g., Health Department, Flood Control, etc.) This project will provide an environmental and economic benefit to the City, as well as improving the customer experience. This allows HPW to better utilize the limited resources, and have full visibility of the balance between O&M vs. CIP budgets.

SpeakerCalabuig, Pablo

Presentation time

14:05:00

14:25:00

Session time

13:30:00

15:00:00

TopicIntermediate Level, Collection Systems, Intelligent Water, Wet Weather

Author(s)

Calabuig, Pablo

Author(s)Pablo Calabuig Pascual1; Fazle Rabbi2; Pratistha Pradhan3;Matthew Katz4

Author affiliation(s)GoAigua, New York, NY1; Department of Public Works, Houston, TX2; Department of Public Works, Houston, TX3; GoAigua, New York, NY4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158646

Volume / Issue

Content sourceWEFTEC

Word count23

Leveraging an Urban Flood Management Tool for Event Forecasting

Abstract

A small creek that runs through the northwest corridor of the City of Toronto often floods during heavy rainfall events. This is due to both the intense rainfall accumulation and potential backups and sanitary sewer overflows (SSOs) in the sanitary sewer network. To minimize the effects of this flooding events, Toronto Water implemented a digital urban flood management tool for flood event monitoring, quantification, and forecasting. This tool acts as an early warning system that enables Toronto Water to predict and quantify flood events, which in turn, allows them to proactively manage the sewer system to reduce flooding and overflow events during these rainfall events. The solution leverages real-time and historical data from sensors throughout the sewer network near the creek, integrates this data with a hydraulic model of the sewer network and combines it with meteorological weather service data and hydrological data of the creek to, then, perform advanced analytics to quantify and forecast potential flooding events during wet weather events. Toronto Water uses the results of this tool to provide its staff members with alerts for potential flooding and sewer overflow events, enabling them to proactively manage their system. In addition to the early warning system alerts, this tool integrates a real-time weather forecast map and provides real-time and forecasted data that is viewed in real-time visualization maps enabling users to monitor and identify flooding and overflow events. This decision support system helps Toronto Water ensure optimization of sewer system capacity during wet weather events. Methodology, Approach, & Results Toronto water uses XVPGA's urban flood management solution for urban flood event monitoring and forecasting. This digital solution obtains and process information from different data sources from Toronto Water and other City of Toronto's agencies, it also includes Toronto Water's GIS of the sewershed network, data from the sanitary sewer flow meters and level sensors installed by Toronto Water, weather data from the Meteorological Service of Canada (MSC) and Toronto Water rain gauge stations, a hydraulic model of the sanitary sewer network, and hydrological data of the nearby creek. Leveraging this input data, the urban flood management tool performs analytics to provide hydro-meteorological forecasting and overlays this data on a real-time visualization and forecasting map. In addition, this tool also provides Toronto Water users with the ability to perform 'what-if' scenarios to analyze potential flooding and overflow events based on estimated (user input) rain accumulation and intensity levels. The main analytical components included in this tool are:

*Hydro-meteorological Forecasting Maps for the estimation of fluvial flow rates based on real-time hydrological models and visualization maps. In addition, rainfall maps for aerial interpolation using geostatistical methods are created. Toronto Water uses these maps for visualization and quantification purposes to complement the analytical data outputs of the Real-time Early Warning System and Early Warning Prediction System.

*Real Time Early Warning System provides flood event warnings based primarily on real-time sensor data, from both the sewer level monitors, rain gauges, and MSC weather data. This is a short-term alerting system that provides alerts of expected time to flood events and potential overflows in the near future. This early warning system enables Toronto Water to proactively manage the sewer system in real-time, dispatching field crews to the most critical points in the system to help manage the system in a way that will reduce flooding levels and eliminate potential overflows.

*Early Warning Prediction System for the prediction of urban flooding and overflow events over the next 24 hours, at any point or area within the sewershed, based on analytics of rainfall forecasts and hydrological modeling data. Similar to the real-time early warning system, but forecasting events over a longer future period, Toronto Water leverages this tool to proactively management the sewer system in preparation for potential flooding events. As this digital tool enables Toronto Water to predict urban flood events and potential overflows within the sanitary sewer network, they are leveraging the tool to optimize decision making and implement early activation protocols and improve decision-making processes for system management by developing coordination plans. In addition, to these analytical components, Toronto can leverage the 'what-if' scenario functionality of the tool to estimate the magnitude of flooding events based on different rainfall scenarios. This functionality enables them to simulate scenarios based on varying (heavier) rainfall events that are predicted to ensure there is sufficient system capacity and their system management plan is optimal in each case.

Toronto Water and Xylem Vue collaborated to develop a digital tool for managing floods in Toronto's northwest corridor. This tool predicts and quantifies flood events, allowing proactive sewer management to reduce flooding. Using real-time and historical data from flow meters, rain gauges, weather services, and a hydraulic model, it provides early warnings and integrates forecasts and maps to optimize system capacity and manage flood risks.

SpeakerSamuels, Michele

Presentation time

09:00:00

09:30:00

Session time

08:30:00

10:00:00

SessionTools for Urban Flood Management and Forecasting

Session number312

Session locationRoom 343

TopicCSO & SSO Planning and Mitigation, Intermediate Level, Resilience, Safety, and Disaster Planning, Stormwater and Green Infrastructure, Watershed Management, Water Quality, and Groundwater

Author(s)