When enterprises decide to digitally transform their operations, the key objective often is to decrease costs by increasing yield and throughput, lowering materials wastage, and decreasing equipment downtime. Manufacturing equipment and processes are therefore mainly optimized for throughput and yield.
The need for assessing energy efficiency using Industry 4.0 technologies is becoming urgent, not the least because industry consumes nearly 41.8% of all electricityi that contributes not an insignificant amount to operational costs. That cost-consciousness has existed for some time and U.S. manufacturers have succeeded in decreasing their manufacturing energy intensity by 26% whilst increasing manufacturing gross output by 12% from 1998 to 2018ii.
What is new, however, is a two-pronged challenge to the industry: meeting the ever-rising energy demand while limiting carbon emissions that are not even an integral part of raw material conversion to finished product. In the U.S., the Department of Energy (DoE) is promoting and supporting the importance of energy performance in boosting the cost-competitivenessiii. The EU, meanwhile, has proposed updates to their Energy Efficiency Directive (EED) to comply with the new 2030 targets. The EED drives energy savings in end-use sectors, including buildings, industry, and transport, by requiring energy auditsiv. Enterprises, looking to lower costs and establish their green credentials, are getting their operations certified to ISO 50001, which mandates the use of energy management systems (EMS) and energy audits.
While there is no substitute for efficient equipment and renewable sources of power, smart manufacturing offers several tools that can be used to monitor, assess, and manage energy usage to additionally optimize operations in terms of power consumption.
Energy monitoring and sensors
The traditional approach to energy monitoring in manufacturing operations can be summarized as follows: Manufacturers learn about their energy costs from their utility bill when they cannot confidently correlate energy consumption information with operational information and therefore have little control over energy efficiency.
The most direct method of improving a facility’s energy efficiency is by monitoring and understanding how the assets, including process equipment as well as power supplies, are performing. It is made possible with smart use of networked sensors to inform EMS and analytics exactly how energy is used by every powered asset. The main types of sensors that do this work include power meters or, for greater flexibility, voltage and current sensors. Information from the latter is combined as voltage × current × time to get energy.
Other sensors, like temperature, pressure, and gas flow sensors, that collect data on process conditions inform organizations about the energy needed to achieve optimum process parameters. This allows them to develop key performance indicators and make better operational forecasts.
Quality of power
Monitoring energy consumption at specific points in the process flow or at each equipment can reveal information about the quality of the power being supplied. For instance, industrial motors offer an inductive load to the supply that makes current lag voltage and thus lowers the power factor (PF). A low PF means that a much higher volt-ampere (from voltage × current) or apparent power needs to be supplied for the needed amount of real power measured in kilowatts that does the actual work.
If power factor isn’t corrected, the facility can experience voltage fluctuations and harmonic noise whenever equipment status changes. Reactive loads also sink reactive power under no-load conditions and lower energy efficiency. Another issue is that power factor and harmonics change with the operational status of equipment and the interaction of reactive loads with each other.
The solution lies in the detection of the location and operational conditions on the factory floor where low PF emerges. This along with data analysis allows the determination of suitable reactive elements, such as capacitive components for inductive loads, and their addition where and even when they are needed.
Insights from big data analysis
Energy monitoring systems extract greater value from energy consumption data than otherwise possible. Combined with analytical tools and artificial intelligence (AI), typically cloud-based, patterns and trends in power consumption and process parameters are revealed and made actionable. The benefits include:
1. Power optimization: Large-scale power consumers often pay a billing rate based on their maximum demand within a billing period. Short-term energy consumption peaks can result in disproportionately higher costs. Insights from energy management systems allow facilities to optimize power consumption by “peak shaving.” This can be done, for example, by modifying production processes to lower inrush current demanded by simultaneous startup of multiple machines.
2. Facility management: Monitoring data aids remote facility management by informing production planners of real-time status, including detection of uptimes and downtimes.
3. Fault detection: Energy monitoring and analysis of the resulting data can also provide important information about equipment health. For example, anomalous energy consumption patterns indicate unsafe operating conditions or imminent equipment breakdowns. A planned downtime for maintenance is by far better for operational efficiency.
4. Process recalibration: Because high throughput often compromises reliability as well as energy efficiency, data analysis can help optimize throughput to achieve reasonable tradeoffs with the other factors. Process recalibration can increase both yield and productivity while lowering energy consumption.
This list represents dynamic evaluations, adjustments, and benefits because the electrical load from equipment is not fixed. It varies with changes in process parameters; periodic increases in throughput to meet demand, time-to-market deadlines, or competitive pressures; ongoing facility expansion and modernization; new process development; and changes in energy-efficiency standards. Without continuous and evolving analyses, it is difficult to maintain efficiency certifications and targeted energy costs.
Upgrade operations with intelligence
The phrase “smart factory” conjures up images of sensor-laden assets, edge analytics, machine learning, automated control of robotics, and AI-driven trend analysis and simulations. That makes business decision-makers apprehensive of the capital expense need for digital transformation.
However, upgrading existing facilities to gather energy data is relatively easy to achieve. Even the semi-automatic robotics commonly used in manufacturing today can offer up energy data for correlation with process information.
Current and voltage sensor modules are easily affixed to equipment. Signal conditioning and analog/digital converters and 5G networking can quickly connect the operation-technology hardware to EMS tools deployed on existing IT infrastructure.
Although greater energy efficiency is always desired, it has not been the main reason for Industry 4.0 deployments. Yet energy monitoring and analytical power from low-cost, easily implemented solutions are an ideal starting point for the digital transformation of an enterprise. The new intelligence from such deployments can result in significant savings in energy bills and make it easier to audit as well as certify facilities to efficiency standards.
Find out how
Expert advice from Arrow Intelligent Solutions can help bring new control systems and intelligent manufacturing technologies, including energy monitoring and analytics, online at your facility fast and cost-effectively.
Talk to Arrow today about choosing the right sensor modules, connectivity solutions, data aggregation and analytics, and cloud hosting to achieve energy-efficiency compliance.
iIEA. “Key World Energy Statistics 2021.” www.iea.org/reports/key-world-energy-statistics-2021/final-consumption
iiU.S. EIA. (Dec. 2021). “2018 Manufacturing Energy Consumption Survey.” www.eia.gov/consumption/manufacturing/pdf/MECS%202018%20Results%20Flipbook.pdf
iiiU.S. DoE, Advanced Manufacturing Office. www.energy.gov/eere/amo/advanced-manufacturing-office
ivEuropean Commission. “Energy efficiency directive.” energy.ec.europa.eu/topics/energy-efficiency/energy-efficiency-targets-directive-and-rules/energy-efficiency-directive_en