Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Civil Engineering


Klotz, Leidy

Committee Member

Aziz, Nadim

Committee Member

Piratla, Kalyan

Committee Member

Burati, James


This research introduces a unique data analysis method and develops empirical models to evaluate energy use and energy cost in wastewater collection systems using operational variables. From these models, several Best Management Processes (BMPs) are identified that should benefit utilities and positively impact the operation of existing infrastructure as well as the design of new infrastructure. Further, the conclusions generated herein display high transferability to certain manufacturing processes. Therefore, it is anticipated that these findings will also benefit pumping applications outside of the water sector. Wastewater treatment is often the single largest expense at the local government level. Not surprisingly, significant research effort has been expended on examining the energy used in wastewater treatment. However, the energy used in wastewater collection systems remains underexplored despite significant potential for energy savings. Estimates place potential energy savings as high as 60% within wastewater collection; which, if applied across the United States equates to the energy used by nearly 125,000 American homes. Employing three years of data from Renewable Water Resources (ReWa), the largest wastewater utility in the Upstate of South Carolina, this study aims to develop useful empirical equations that will allow utilities to efficiently evaluate the energy use and energy cost of its wastewater collection system. ReWa's participation was motivated, in part, by their recent adoption of the United States Environmental Protection Agency 'Effective Utility Strategies' within which exists a focus on energy management. The study presented herein identifies two primary variables related to the energy use and cost associated with wastewater collection: Specific Energy (Es) and Specific Cost (Cs). These two variables were found to rely primarily on the volume pumped by the individual pump stations and exhibited similar power functions for the three year period of evaluation. The data was analyzed using statistical analysis and was and was cross-validated using an analysis of variance (ANOVA). It is anticipated that the relationship developed for Es will be of the most value to other utilities since it represents the most generalizable parameter. Historically, the approach to energy management at the wastewater collection level has been simply to input the energy necessary to accomplish the goal of safely and effectively moving the raw wastewater to the treatment plant. This study aims to change this approach through the development of generalizable relationships.