One area that CastTrip has been working on for the past two years is increasing the use of machine intelligence to increase processing efficiency. “This is very much influenced by the skill of the operator, who sets the point for automation, so we are using a reinforcement learning-based neural network to increase the accuracy of that setting to create a self-driving casting machine. This will definitely achieve more energy-efficiency – nothing like the previous big-step changes, but it is still measurable.
Reuse, Recycle, Reproduction: Design for circular production
The growth in the use of digital technologies to automate machinery and to monitor and analyze production processes – a set of capabilities commonly known as Industry 4.0 – is driven primarily by the need to increase efficiency and reduce waste. Companies are expanding the productivity capabilities of equipment and machinery in manufacturing processes through the use of monitoring and management techniques that can evaluate performance and actively predict optimal repair and innovation cycles. Such an operational strategy, known as conditional maintenance, can extend the lifespan of manufacturing assets and reduce failure and downtime, all of which not only increase efficiency but also directly improve energy efficiency and optimize material consumption. Makes, which helps in reduction. Carbon footprint of production facility.
The use of such tools could also establish a firm on the first steps of the business journey defined by the “circular economy” principles, in which the firm not only produces goods in a carbon-neutral way, but relies on renewable or recycled inputs. Produce them. The circular is a progressive journey of many steps. Each step requires a long-term business plan for managing materials and energy in the short term and for future “sustainability-designed” manufacturing.
IoT monitoring and measurement sensors, deployed on manufacturing assets and production and assembly lines, represent a critical element in the firm’s efforts to implement the circular. Through condition-based maintenance initiatives, the company is able to reduce its energy costs and increase the life and efficiency of its machinery and other manufacturing assets. “Performance and status data collected by IoT sensors and analyzed by management systems provide the ‘next level’ of real-time, factory-floor insights, allowing for much greater accuracy in maintenance evaluation and status-improvement scheduling,” Pierre Sagrafe said. , Notes the circular. Program leader in Snyder Electric’s energy management business.
Global food producer Nestl is undergoing a digital transformation through its Connected Worker initiative, which focuses on improving performance by increasing the flow of paperless information to facilitate better decision making. Jose Luis Buella Salazar, Nestlના’s Eurozone maintenance manager, oversees efforts to increase process-control capabilities and maintenance operations for the company’s 120 factories in Europe.
“Observing the situation is a long journey,” he says. “We relied on a lengthy ‘Level One’ process: knowledge experts review performance on the shop floor and write reports to establish alarm system settings and maintenance schedules. We are now approaching the ‘4.0’ process, where data sensors are online and our maintenance scheduling processes are predictable, using artificial intelligence to predict failures based on historical data that is collected from hundreds of sensors on an hourly basis. About 80% of Nestl’s global facilities use state-of-the-art condition and process-parameter monitoring, with Buella Salazar estimating a 5% reduction in maintenance costs and an increase in equipment performance from 5% to 7%.
Buella Salazar says that most of these improvements are due to the increasingly dense array of IoT-based sensors (between 150 and 300 in each factory), which collects increasingly reliable data, allowing us to detect even the slightest distortion at an early stage. Giving. More time for us to respond, and to reduce our need for external maintenance solutions. Currently, Buella Salazar explains, the carbon-reduction benefits of status-based maintenance are implicit, but this is changing rapidly.
“We have a major energy-intensive tool initiative to install IoT sensors for all such machines in 500 facilities globally, monitoring water, gas and energy consumption for each, and correlating it with related process performance data.” “It simply came to our notice then. This will help Nestl કરવામાં reduce its production energy consumption by 5% in 2023. In the future, such correlation analysis will help Nestl સં combine insights into material consumption measurement to perform “big data analysis for carbon-optimized product-line configuration at an integrated level,” Buela Salazar adds, adding that About 100 other parameters in a complex food-production facility. “Integrating all this data with IoT and machine learning will allow us to see what we haven’t been able to see so far.”