Proposta de modelo para reconhecimento de padrões de comportamento de vazamentos de água visando a redução de perdas na distribuição

Abstract

Managing leaks in treated water distribution networks poses a significant challenge for sanitation utilities. With the increasing demand for water due to population growth and climate change, the efficient and sustainable operation of distribution networks becomes essential to mitigate losses. This study addresses water resource management and the negative impacts of losses on the management process. A literature review and field research were conducted at a sanitation utility. Based on the principles of ESG (Environmental, Social, Governance), the study highlights the importance of improving distribution efficiency (environmental dimension), reducing losses to ensure regular water supply (social dimension), and ensuring compliance with regulations and standards (governance dimension). Finding tools to optimize loss management is crucial. The most relevant contribution of this study is its potential adoption by all stakeholders involved in the universalization of access to treated water. The results identified challenges such as real-time monitoring, leak detection, data optimization, process automation, and system integration, which can be overcome by applying information technology. Machine Learning is highlighted as a promising solution for leak detection, enabling real-time data analysis, detection in hard-to-reach areas, modeling of water pressure and flow, and analysis of consumption patterns to identify leaks more quickly and accurately.