Fat, Oil, and Grease Sewer Waste Management System: A Modeling Platform for Simulating the Formation of FOG Deposits in Sewer Networks

Abstract

The accumulation of fat, oil, and grease (FOG) deposits in sanitary sewer systems is a global issue responsible for billions of dollars in annual maintenance costs, environmental impact, and public health concerns. Laboratory and data-driven studies have explored the physical and chemical characteristics of FOG deposits, formation kinetics, and factors influencing their accumulation in sewer networks, such as pipe sags, pipe age, and pipe material. However, a scalable modeling platform capable of incorporating FOG kinetics and the key variables driving accumulation is not currently available. This study introduces a new platform for modeling the formation and attachment of FOG deposits in sewer systems, the FOG Sewer Waste Management System (FOG-SWMS). FOG-SWMS was utilized to quantify the impact of pipe sags, pipe age and roughness, and pipe material on the accumulation of FOG deposits in sewer lines. Results indicate that under typical sewer conditions, pipe sags and pipe age may increase the accumulation of FOG deposit mass by as much as approximately 23% and 150%, respectively. Surface pH≥8, as a proxy for cementitious pipe materials, also resulted in more than a 172% increase in FOG deposit mass accumulation. These results confirm data from previous laboratory studies and machine learning algorithms, and also provide a mechanistic explanation of formation processes. Two full-scale sewer network case studies, Study Area 1 (SA1) and Study Area 2 (SA2), were analyzed using FOG-SWMS, to evaluate the predictive capability of the model to identify FOG deposit accumulation zones. FOG-SWMS successfully predicted approximately 85% and 73% of known accumulation zones in SA1 and SA2, respectively. Significant changes in the spatial and temporal distribution of FOG deposits in response to new commercial and residential development also were demonstrated with the model. Fat, oil, and grease deposits are solid masses that form in sanitary sewer lines and create a variety of issues for municipalities. They are primarily the result of discharged food waste residuals into sewers, which reacts with typical wastewater components to form a solid that can block sewer lines and cause overflow of wastewater. These impacts are common, and equate to an incredible amount of time and money spent on sewer repairs and maintenance. This study introduces a new computer-based model that allows researchers to visualize the formation of FOG deposits in sewer line segments. The model is not limited to specialized researchers; it also is intended to be used by engineers and pretreatment coordinators who may be concerned with the accumulation of FOG deposits in sewer line engineering structures. The main goals of this study were (1) to provide the sewer line workforce with a tool for predicting the accumulation of FOG deposits in sewer networks, especially when there are changes in commercial establishments; (2) to serve as a platform for integrating modern sewer inspection data and sensor deployment studies that are becoming more popular; and (3) to incorporate future advancements in machine learning and AI algorithms.