Ford Motor Company: Dearborn Campus Uses Digital Twin Tool for Energy Plant Management - Smart Energy Decisions

Distributed Energy Resources, Energy Efficiency, Industrial  -  April 4, 2022 - By Better Buildings, U.S. Department of Energy

Ford Motor Company: Dearborn Campus Uses Digital Twin Tool for Energy Plant Management

A digital twin is the virtual representation of a physical object or system across its life cycle. Ford uses digital twin technology to accurately detect energy losses, pinpoint areas where energy can be conserved, and improve the overall performance of production lines. At Ford’s Dearborn Research and Engineering Campus Central Energy Plant (CEP), the digital twin helps operators and engineers manage energy and operational risks by monitoring the CEP’s energy systems, including the combined heat and power (CHP) system, heat recovery chillers, electric centrifugal chillers, thermal energy storage, and other equipment. The digital twin model pairs a virtual representation of the CEP’s mechanical systems with real-time data to enable learning, reasoning, and dynamic recalibrating for improved decision-making. Real-time data is collected via connected internet of things (IoT) devices, a network of sensors that exchange data with the model. Using the digital twin, the CEP team can drive operational excellence by monitoring operations across the major energy-consuming equipment.

As part of a 10-year Campus Transformation Program to modernize the workplace, Ford Motor Company established sustainability goals for the Dearborn project that focused on campus energy infrastructure. The aspirational goals are based on several focus areas:

  • Climate Change – Ford Motor Company supports CO2 reduction goals consistent with the Paris Climate Accord.
  • Energy – The company plans to use 100 percent renewable electricity for new facilities on the Dearborn campus, and for all manufacturing plants globally by 2035.
  • Water – Net Zero water withdrawals for manufacturing processes.

The design and construction of the Dearborn campus central energy plant included a unique master energy plan to ensure that campus building upgrades followed specific sustainability principles. These required the evaluation of technologies like energy storage, heat recovery, high efficiency chilled water systems, advanced enthalpy controls, solar PV and battery storage, and the use of a digital twin to improve the life cycle of the central energy plant, including design, construction, operations and maintenance. For more details on other campus energy plant technologies, read the Better Plants Showcase Project article: Ford Motor Company: Dearborn Research and Engineering Campus Central Energy Plant.

To implement the digital twin, the Ford design team first defined a series of goals and objectives:

  • Provide operations with real-time operations data in the field, at the point of activity.. This capability allows the operations team to make quick, data-driven decisions, both inside and out of the control room.
  • Give the team access to a comprehensive asset database with useful system information, including system and engineering drawings of mechanical and electrical equipment; standard operating procedures (SOPs) to ensure consistent and smooth operations; engineering change proposals to manage configuration changes; job hazard analyses to identify and mitigate risks associated with specific tasks; and operation and maintenance (O&M) manuals.
  • Integrate the digital twin with CEP’s Computerized Maintenance Management System and provide a platform for deploying augmented reality throughout the central energy plant.
  • Future capability to bring in information from advanced pattern recognition models and machine learning to drive early intervention and operational excellence.

After establishing these goals, the design team began building out the digital twin and outlining its operating procedures. The team first defined and categorized system behavior into the categories of Predicted Desirable (PD), Predicted Undesirable (PU), Unpredicted Desirable (UD), and Unpredicted Undesirable (UU). PD represents the optimal behavior of the central energy plant based on expected operations. PU is any potential facility issues that the team knew about before implementation. UD contains any potential facility issues that the team did not know about before implementation, but that could cause better outcomes or optimization. Finally, UU is any unforeseen potentially serious or catastrophic issue.

With the digital twin defined, the team then took the model through its product life cycle. This cycle includes the system creation phase, the production and construction phase, and the operational phase. In the creation phase, the team defined key system requirements and attributes along with the identification of undesirable system attributes and strategies to mitigate them. The team also considered the manufacturability, supportability, disposability, and sustainability of the system. All these parameters became part of a database for the digital twin.

In the subsequent manufacturing and construction phase, various digital threads were used to manufacture plant components offsite. Components were manufactured with high precision using predefined design and information. The 9.5 miles of piping for the central energy plant were prefabricated offsite and transported to the site for assembly. Once assembled, precision 3D scanners from a digital thread model were used to pre-scan areas and point of assembly. In addition, laser measuring devices were used to measure components and guide the assembly of pieces. The assembled units were then laser scanned again to ensure high precision and eliminate any deviations. New installations included piping that was shop fabricated in 40’ sections allowing fields of welds to be minimized; hangers that were installed and painted well in advance of piping installation; and pipes that were shipped to the site with unique identifiers as well as details on weight and center of gravity directly marked on the pipes, which reduced rigging time.

The team then moved into the operations phase of the project. Here, they performed another 3D laser scan of the whole system, which provided the team with a 3D prototype model and a 3D model of the whole plant and systems. Equipment data was then imported into the Buildings Information Management Software for the entire plant. The digital twin creation process was also used in the building electrical, mechanical, and information technology design and implementation, so the team had data on electrical breakers from 13,200 volts down to the 110-volt outlet level.

The Ford Dearborn Research and Engineering Campus is a ~ 750-acre center with 28 facilities including office buildings, design studios, laboratories, and test tracks. The energy footprint for the research and engineering campus before the construction of the central energy plant can be summarized as:

  • Electric: Approximately 50 MW annual peak
  • Natural Gas: Approximately 1.3 billion cubic feet (BCF)
  • Steam: Was centrally generated at a 1930 Albert Kahn designed Powerhouse with 5 boilers and over 4 miles of distribution piping throughout the campus (significant condensate losses with an estimated 40% system efficiency).

The new plant was completed at the end of 2019 and is projected to achieve a 50% reduction in campus office space energy and water use compared to the older system. The digital twin enables tracking of the plant’s performance towards this 50% target and will enable the operations team to make well-informed decisions regarding operational changes and future enhancements.

The digital twin 3D model has been critical to the efficient design, construction, and operation of Ford’s new central energy plant at the Dearborn Research and Engineering campus. The digital twin helps operators quickly identify problems and reduce response time to system faults. It also helps designers and engineers quantify the potential impacts of a fault or system change along with the impact on downtime. In addition to these efficiency benefits, the digital twin provides historical and current performance data, which reduces the time required for information gathering in times of crisis. Time loss in power plant management can be costly if mismanaged so by improving response time, the digital twin improves the overall performance of the plant. 

The digital twin has also been essential in helping to respond to sudden demand spikes from campus facilities. The model can optimize the delivery of steam, hot water, and chilled water throughout the plant distribution system and ensure demand is met. Future plant additions are already planned and the model is being used to create virtual design and construction scenarios. These virtual implementation models incorporate the new additions and will anticipate potential challenges when it comes time for the construction and operation of the new facilities.

The “factory twin” as described above, is one of several digital twins that make up Ford Motor Company’s vision for its Factory of Tomorrow. The “factory twin” is a foundational element for the “manufacturing digital twin” which aggregates all data for assets, as well as digital representations for manufacturing process and products. Drilling further down to the “asset digital twin” provides data such as energy consumption and waste, both live and historical, which enable simulations and analysis to comply with environmental sustainability requirements.

Using what is called a “digital thread,” Ford ties these digital twins together to provide rich operational data about how its facilities are operating. This information helps operate its facilities efficiently and engineer new programs faster.

This column originally appeared as a blog from Better Buildings. 

 

Through DOE's Better Buildings Initiative, more than 950 commercial, public, industrial, and residential organizations share their proven energy efficiency strategies and inspire others to tap into the continued potential for energy efficiency. Collectively these organizations have saved more than 2.2 quadrillion Btus of energy, equivalent to over $13 billion, and more than 130 million metric tons of carbon dioxide. Partners have reduced their water use by more than 10 billion gallons. Together, partners represent more than 30 of the country's Fortune 100 companies, 12 of the top 25 U.S. employers, 12% of the U.S. manufacturing energy footprint, and 13% of total commercial building space, as well as 17 Federal agencies, 8 national laboratories, and more than 80 state and local governments spanning the nation. 

 

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