One of the areas that Castrip has been working on over the past two years is increasing the use of machine intelligence to increase process efficiency in yield. “This is quite affected by the skill of the operator, who sets the points for automation, so we use gain-based neural networks to increase the precision of that setting to create a self-propelled caster. efficiency gains – nothing like the previous big changes, but they are still measurable.”
Reuse, recycle, remanufacture: design for circular production
The growth in the use of digital technologies to automate machines and monitor and analyze production processes – a set of capabilities commonly referred to as Industry 4.0 – is primarily driven by the need to increase efficiency and reduce waste. Companies are expanding the productive capabilities of tools and machines in manufacturing processes through the use of monitoring and management technologies that can assess performance and proactively predict optimal repair and refurbishment cycles. Such an operational strategy, also known as condition-based maintenance, can extend the life of production assets and reduce downtime and downtime, which not only increases operational efficiency, but also directly improves energy efficiency and optimizes material utilization, which helps to create a carbon footprint of the production facility.
The use of such tools can also take a company on the first steps of a journey towards a company defined by the principles of the “circular economy”, where a company not only produces goods in a carbon neutral way, but relies on refurbished or recycled inputs to manufacture them. Circularity is a progressive journey of many steps. Each step requires a viable long-term business plan for materials and energy management in the short term, and design-for-sustainability manufacturing in the future.
IoT monitoring and measurement sensors deployed on production assets and in production and assembly lines are a critical part of a company’s efforts to implement circularity. Condition-based maintenance initiatives allow a company to reduce its energy consumption and extend the life and efficiency of its machines and other inputs. “Performance and condition data collected by IoT sensors and analyzed by management systems provides a ‘next level’ of real-time insight on the factory floor, enabling much greater precision in maintenance assessments and maintenance schedules,” notes Pierre Sagrafena, Circularity. program leader at Schneider Electric’s energy management company.
Global food producer Nestle is undergoing a digital transformation through its Connected Worker initiative, which focuses on improving operations by increasing the paperless flow of information to facilitate better decision-making. José Luis Buela Salazar, Nestlé’s Eurozone Maintenance Manager, oversees efforts to improve process control and maintenance performance for the company’s 120 plants in Europe.
“Condition monitoring is a long journey,” he says. “We used to rely on a lengthy ‘Level One’ process: on-the-job knowledge experts assess performance and write reports to determine alarm system settings and maintenance schedules. We are now moving into a ‘4.0’ process, where data sensors are online and our maintenance planning processes are predictive, where artificial intelligence is used to predict failures based on historical data collected from hundreds of sensors, often on an hourly basis.” of Nestlé’s global facilities utilize advanced condition and process parameter monitoring, which Buela Salazar says has reduced maintenance costs by 5% and increased equipment performance by 5% to 7%.
Buela Salazar says much of this improvement is due to an ever-dense array of IoT-based sensors (each factory has between 150 and 300), “collecting increasingly reliable data, allowing us to see even small degradations in a early stage, giving us more time to respond and reducing our need for remote maintenance solutions.” Currently, explains Buela Salazar, the carbon reduction benefits of condition-based maintenance are implicit, but this is changing rapidly.
“We have a major energy-intensive equipment initiative to install IoT sensors for all such machines in 500 facilities worldwide to monitor water, gas and energy consumption for each machine and make correlations with the respective process performance data,” he says. This will help Nestle reduce energy consumption in production by 5% by 2023. Going forward, such correlation analysis will help Nestle perform “big data analysis to optimize production line configurations at an integrated level of carbon” by combining insights from material utilization measurements , energy efficiency of machines, rotational schedules for motors and gearboxes and as many as 100 other parameters in a complex food production facility, Buela Salazar added. “By integrating all this data with IoT and machine learning, we can see what we haven’t been able to see until now.”