Optimize Energy Efficiency and Sustainability

Summary

Energy conservation is being pushed to the top of the priority list, driven by world events, rising energy prices and supply chain issues. Pursuing energy conservation is compatible with achieving climate improvement and sustainability goals to which automation and control professionals can make even more meaningful contributions.

Manufacturing is a significant energy user worldwide, making conservation efforts a priority. For example, the U.S. Energy Information Administration (EIA) reported in 2020 that the industrial sector accounted for 33 percent of total U.S. energy consumption. Energy is generally a hidden cost that is part of factory overhead and not tracked as part of the bill of materials for production. Out of sight, out of mind. In addition to lowering consumption, the return-on-investment (ROI) analysis for energy saving expenditures should factor into increasing company competitiveness.

Awareness and engineering

Automation professionals can achieve more efficient energy use starting with awareness, followed by engineering to save energy. The same engineering logic used to control the production process applies to energy management. Using energy input and output and real-time measurements, the efficiency of production processes can be calculated and optimized.

An important first step is measuring and tracking where, when, and how much energy is being consumed. Before making investments in energy and power monitoring devices, automation professionals can use existing data in supervisory control and data acquisition, human-machine interfaces, and historians. They contain a wealth of information to develop meaningful profiles of energy consumption and deviation without adding more hardware. For example, by tracking motor run times and using the nameplate power consumption information, power consumption can be calculated to track approximated energy use without adding more hardware. Add to this alarm limits to detect unusual run times, and unusual start/stop cycles can be used to infer problems that are wasting energy.

Using sound application engineering to improve efficiency can reduce energy. For example, multiple boilers serving a process plant typically each have different nonlinear efficiency characteristics–based load and output levels. Accurately measuring demand and using the efficiency curves can help select part loading of the boilers to achieve the most efficient energy use while serving the needs of the process. This can be done easily in existing systems and does not take artificial intelligence or highly elaborate algorithms to accomplish.

Practical solutions

Ongoing monitoring provides the data to create profiles and reports that are a foundation for reporting energy data and identifying deviations and areas for improvement. Analyzing patterns of this data analysis permits plants to make better decisions about controlling energy costs and making investments.

Fundamental monitoring and control methods are the first step, and later, other investments can be made after analyzing the data to develop ROI for more sophisticated energy savings investments. These might include minimizing peak demand by applying an electrical load-management system to monitor and control electrical consumption of selected equipment, automatically turning off air supply lines when product is not being produced, and using compressed air control systems to optimize operations.

When data is not tracked and related to production output, it can appear that the initial energy consumption investments are not paying off. Sustaining an effective energy program requires ongoing data collection and analysis.

Several methods can be useful:

• continue to reinforce energy as a priority in operational decision making
• communicate program successes as they occur
• extend power- and energy-monitoring solutions to support continuous-improvement efforts
• build in alarm advisories for energy key performance indicators.