Jackson is a water engineer who is experienced in using physics-based models to evaluate hydraulics and control strategies. Jackson commonly utilizes data analytics tools to streamline engineering processes. He is a senior user and developer of Jacobs proprietary tool, Replica Operations, which is used to deliver digital twins for treatment facilities. Jackson received his bachelor’s degree in civil engineering from the University of South Alabama and is currently enrolled part time at the Georgia Institute of Technology for a master’s degree in data analytics. Jackson is a Professional Engineer registered in the State of Florida.

Mobile Area Water and Sewer System (MAWSS) owns and operates a collection system network consisting of approximately 85lift stations, three severe weather attenuation facilities, and two treatment facilities. MAWSS contracted Jacobs to develop a comprehensive collection system model to be used for planning purposes and perform targeted evaluations on areas of interest in the system.

The modeling process combines aspects from the fields of data analytics and physics to accurately represent a system. Engineers are typically well versed in physics but in most cases do not have a background in data analytics. By utilizing data analytics in the modeling process a robust model can be developed by automating repetitive tasks to allow for more time to focus on the integrity of the model.

Jacobs utilized various data analytics tools and proprietary tools to streamline the model development and calibration effort. Visualization of flow meter quality and model calibration quality was streamlined by creating scripts to automate data manipulation tasks before viewing the data in a dashboard. Jacob’s dashboard hosting platform was used to share pertinent data with MAWSS through a secure web application. A proprietary method that utilizes custom machine learning and optimization algorithms was used for automating the calibration of the collection system. Most calibration efforts fall short of an optimal or near optimal solution since calibration is typically a manual process. Using this automated method for calibration provides a near optimal solution and frees up time to focus on providing a robust complete model instead of spending time manually changing calibration parameters.