Using machine learning to cut industry emissions
Intelecy machine learning software helps companies to cut energy consumption and maintenance costs and reduce resource waste.
Thirty per cent of the world’s greenhouse gas emissions come from manufacturing industries. Reducing this footprint is essential to meeting the emission reduction targets set out in the Paris Agreement.
Although the exact number varies from sector to sector, most industrial operations achieve only about 50-70 per cent efficiency. This encompasses equipment inefficiency, defective or low-quality end products, stops and slowdowns, and results in a significant waste of energy and material resources.
More and more companies are collecting data in an effort to reduce the efficiency gap, but one of the challenges is handling the sheer amount of information from thousands of data points.
Improving industrial efficiency with machine learning software
Intelecy is a cloud-based software for predictive maintenance and process optimisation designed specifically for Industrial IoT data. It uses machine learning to process data from sensors across the production chain that can impact efficiency, including air flow, humidity, pressure, temperature, and many more.
Intelecy is being used to detect anomalies in production equipment at an early stage, reducing maintenance costs. It can also be used for quality forecasting, enabling enable companies in natural resource-processing industries to avoid producing low-quality or unusable end products.
Moreover, Intelecy helps to identify the manufacturing steps that contribute most to energy consumption and overall greenhouse gas emissions. By reducing energy waste, it is possible to reduce CO₂ emissions by as much as 40 per cent without reducing production.
Additionally, the software helps to avoid unwanted downtime and damage to machinery by detecting anomalies in production before issues and delays arise.
