Terrill, Trevor Jon (2018-08). Advancements in the Industrial Internet of Things for Energy Efficiency. Doctoral Dissertation.
The Internet of Things is an emerging field that leverages the connections of everyday objects for the betterment of society. A subfield of this trend, the Industrial Internet of Things (IIoT), has been referred to as an industrial revolution that enhances both productivity and safety in the industrial environment. While still in its early stages, identified improvements have the potential to markedly improve manufacturing productivity. Energy efficiency within manufacturing plants has traditionally received little focus. The Industrial Assessment Center Program demonstrates the potential energy improvements that can be realized in manufacturing plants, but these assessments also highlight some of the traditional barriers to energy efficiency. Some of these barriers include the lack of data to justify actionable improvements, unclear correlations between improvement costs and potential cost savings, and lack of knowledge on how energy improvements provide ancillary benefits to the plant. The IIoT has the potential to increase energy efficiency implementation in manufacturing plants by addressing these challenges. This dissertation discusses the framework in which energy efficiency enhancements within the IIoT environment can be realized. The dissertation initially details the potential benefits of IIoT for energy efficiency and presents a general framework for these improvements. While proposed IIoT frameworks vary, they all include the core elements of improved sensing capabilities, enhanced data analysis, and intelligent actuation. In addition to presenting the framework generally, the dissertation provides detailed case studies on how each of these framework elements lead to improved energy efficiency in manufacturing. The first case study demonstrates improved sensing capabilities in the IIoT framework. A non-intrusive flow meter for use in compressed air and other fluid systems is presented. The second case study discusses Autonomous Robotic Assessments of Energy, which use advanced data analysis to autonomously perform a lighting energy assessment in facilities. The third case study is then presented on intelligent actuation, which uses a novel k-means algorithm to autonomously determine appropriate times to actuate compressors for air systems in manufacturing plants. Each of the presented case studies includes experimental tests demonstrating their capabilities.