Page 164 - Towards A Sustainable Future 2024

Presentation of Data Conclusion

For data collection, the ESI team selected a milling In conclusion, the ESI team’s environmental review of
machine used by SilverSteel. The team collected Silversteel business operations, with a focus on predictive
electricity consumption data before and after servicing maintenance for machinery leads to increased energy
of the machine. Based on electrical power consumption, efficiency, and a cut in associated CO2 emissions. By
the projected annual CO2 emissions were calculated to deploying current monitoring activities and predictive
be 398.17 kg before implementation of the eco-solution maintenance software, it is possible to detect issues before
(Table 2). After servicing the machine, the projected they escalate into significant maintenance problems. This
yearly CO2 emissions were reduced to 316.40 kg (Table proactive approach will not only minimise downtime but
3). This indicates an annual CO2 emission savings of over also extend the lifespan of the machine.
81.77 kg per machine, translating to a 20% reduction in
carbon emissions. Considering servicing of one machine Implementing predictive maintenance involves several key
per month, the annual CO2 emission savings would be stages, including identifying critical assets, establishing
approximately 981.24 kg. databases, analysing failure patterns, installing IoT devices,
and scheduling maintenance based on predictive analytics.
Table 2: Carbon emission per machine before solution The environmental review of SilverSteel’s operations
implementation. underscores the significance of predictive maintenance in
reducing the company’s carbon emissions and operational
Resource Annual Annual CO2 Conversion costs.
Usage Emissions Factor

Electricity 971.15 kWh 398.17 kg 0.41 kg CO2/
kWh

Table 3: Carbon emission per machine after solution
implementation.

Resource Annual Annual CO2 Conversion
Usage Emissions Factor

Electricity 771.72 kWh 316.40 kg 0.41 kg CO2/
kWh

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