Session: 08-03-01: Energy-Related Multidisciplinary I

Paper Number: 96040

96040 - A Data Driven Analysis on the Energy Performance and Efficiency of Water Treatment Plants 

Water treatment plants are responsible for over 30 terawatt-hours per year of electricity consumption in the United States with an annual cost of nearly $2 billion. According to the U.S. Environmental Protection Agency (EPA), municipalities and utilities can achieve energy savings in the order of 15 to 30 percent with expected payback periods ranging from a few months to a few years. Understanding the energy consumption in water treatment plants as well as the potential energy efficiency measures (EEMs) for these facilities can help the municipalities to prioritize the relevant energy efficiency projects based on their payback period and potential impact on their energy bill. In the present paper, the energy performance data for 70 water treatment plants is obtained from the U.S. Department of Energy Industrial Assessment Center (IAC) database. Energy audits were performed in these 70 sites between 2012 and 2022. The database includes the approximate location, square footage, annual energy use, annual plant production, identified EEMs, and their associated energy/cost savings as well as estimated payback period. An analysis is performed to understand the correlations between annual energy consumption, annual plant production, and footprint of the plants in each region. The energy consumed per unit of production and per unit of plant area are calculated for all of the facilities before and after implementing the identified EEMs and the results are discussed accordingly. An average energy use intensity based on the plant area and production is evaluated and used as a benchmark to understand the relative performance of a given facility with respect to the average energy performance of all water treatment plants (before and after the energy audits).  The analysis is also extended to understand the most promising EEMs for water treatment plants based on their region and annual production. An artificial neural network (ANN) is then developed to facilitate energy forecasting of water treatment plants using basic inputs including plant area, annual production, and climate region. The outputs will include estimated annual energy consumption, estimated potential savings that can be identified through conducting an energy audit, and a score of how the facility energy performance compares against the average energy use intensity of the other water treatment plants. The ANN model will be the core of a basic energy analysis tool that can help the municipalities to easily evaluate the performance of their water treatment plants and estimate the potential savings that may be achieved as the result of performing an energy audit.

Presenting Author: Alex Callinan Florida Institute of Technology

Presenting Author Biography: Alex received his Bachelor's Degree in Mechanical Engineering from the Florida Institute of Technology in 2020. His current research is focused on industrial energy efficiency, developing models and methods for energy analysis in industrial facilities.

Authors:

Alex Callinan Florida Institute of Technology
Hamidreza Najafi Florida Institute of Technology
Aldo Fabregas Florida Institute of Technology
Troy Nguyen Florida Institute of Technology