Automated Capacity Evaluation For Sanitary Sewers Using API For ICM Model, Data-Lake, Web-GIS, and Microsoft Power Platform

Background Houston's wastewater system is one of the largest and complex in the nation with 39 treatment plants, 381 lift stations, and over 5,900 miles of pipes. On April 1, 2021, a consent decree (CD) between the City of Houston, the United States Environmental Protection Agency (EPA) and the State of Texas was signed to improve Houston's wastewater system. The City of Houston, as part of its Consent Decree, is tasked with developing an Advanced Infrastructure Analytics Platform (AIAP) which incorporates concepts of one-source data, data dynamism, self-service data, and interactivity to facilitate all functions of a wastewater planning group in a synchronous way. A part of AIAP is the Living Infrastructure Plan (LIP). The Automated Capacity Evaluation (ACE) tool fits into LIP as the main capacity evaluation, and capacity alternative recommendation component using hydraulic models as the bases of recommendations. Problem ACE will assist the City of Houston to make capacity-based decisions on a more continuous, living basis using up-to-date data, and yields quick at-your-fingertips results for decision-makers. The alternatives ACE provides are not exhaustive, nor are they prescriptive in the sense that they provide 'a solution'. ACE simply puts alternatives processed through its limited framework at the fingertips of decision makers in a consistent, efficient, and quick manner. Opportunities The City of Houston has devoted considerable resources to develop Innovyze ICM models for their 39 Service Areas. These databases contain considerable data, including networks and simulation data. Innovyze provides a backdoor to ICM called ICM Exchange which allows a script to interact with the wealth of data in ICM automatically. Combining ICM Exchange with the existing ICM databases allows for complex, automated scripting. The City of Houston had developed logic for capacity evaluation. ACE combined the logic of capacity evaluation, the databases of ICM, and the scripting ability of ICM Exchange. Approach The alternative generating part of ACE revolves around four strategies: Inflow and Infiltration Reduction (IIR), conveyance capacity improvements, offline storage, and inline storage. ACE is heavily dependent on the current network for potential solutions. For instance, a new tunnel alleviating overflows which follows a path unrelated to the current network (perhaps related to existing road alignments) will not be a solution provided by ACE. Instead, ACE determines capacity constraints within the existing network layout, and adjusts parameters within the existing network such as conduit width, head-discharge curves, and pump stage on/off. ACE also creates new assets such as offline storage nodes, and inline storage lines -- these new assets are determined by hydraulic constraints and current network layout, not using data such as road alignments or land ownership, although these may eventually roll into ACE. The four strategies of ACE can be interlinked, optimized, analyzed alone, and compared. Strategies can be viewed in complex series, where output from one strategic process influences the input of another strategic process, or the strategies are performed in parallel where within each process decisions are made between strategies. Some strategies require significant iterations, and for iterative processes each iterative step yields a result. From these results an optimal iterative step can be chosen. The only finalized strategy of ACE today is conveyance. All other strategies remain in development. The framework for sequencing strategies and the majority data processing involved for all strategies is also complete. The conveyance strategy is a complex, iterative decision tree. The outcome of the conveyance improvement strategy isn't one solution, but an array of solutions ranging from no improvements to improvements that meet certain trigger and design criteria. The conveyance improvement strategy moves towards these success criteria in a step-by-step fashion by upsizing certain pipes -- not all pipes -- by 1 standard diameter each iteration, and by upsizing certain pumps - not all pumps - when needed so that they are not acting as a downstream capacity constraint. The outcome provides two vital, and impactful pieces of information for the given scenario: an analysis of which pumps need upsizing and by how much after upstream capacity constraints are relieved, and which pipes need upsizing. Of course, these outcomes are specific to the success criteria used, and the scenario fed to ACE. A specific example of the Eddington Lift Station Service area will be used to show an example of results achieved by ACE conveyance improvements. Results A specific example of the Eddington Lift Station Service area will be used to show an example of results achieved by ACE conveyance improvements. Eddington is a small area in the Sims Bayou Service area which includes a single pump, and roughly 1100 acres encompassing approximately 60,000 LF of conduits ranging in size from 4' to 36' diameter, and over 250 nodes. Conduits are improved over seven iterative steps, each step yielding larger diameter pipe. The final alternative, which is not necessarily the cost-value optimized solution but is the hydraulically optimal solution based on the trigger and design criteria, requires upsizing of 36,000LF of conduit, or roughly 60% of the total system. This 60% number can be broken down further into two groups -- pipes which were upsized due directly to the success criteria, and conduits upsized to ensure upstream conduit width is less than or equal to downstream conduit width. Indirect upsizing accounts for roughly 25% of the total conduits upsized. Conclusion and Lessons Learned: Among the lessons learned in ACE revolve around the complexity and importance of having one source of data in order to have a real-on-the-ground satisfactory solution that has community context embedded in decision making many datasets need to be utilized. The City of Houston's Advanced Infrastructure Analytics Platform is addressing this need. ACE can provide benefits to utilities of all sizes, and ACE as a process can be applied to any utility regardless of model vendor. This presentation will focus on ACE, methods and processes involved in ACE, benefits of ACE as a decision-making tool, contextualizing ACE within the larger City of Houston AIAP framework, outcomes of the conveyance improvement strategy of ACE, lessons learned, and potential future developments.