Every factory uses electricity to run its machinery and equipment. In many industries, the cost of electricity is an important component of the price of the final product and therefore has a significant impact on a factory's competitiveness in the marketplace.
The first step in optimizing production costs in this area is to measure and analyze the electricity consumption of individual production facilities, machines and equipment, broken down by production stage, type of product or material used.
HOT_SENSE project
In January, we launched a project called HOT_SENSE to address this issue. It is being carried out as part of the GreenSME programme in collaboration with MJ Polymers, a manufacturer in the injection moulding industry. The acronym of the project is taken from its full name 'Automated HOT Spots detection and electric ENergy uSagE allocation system for sustainable manufacturing'.
The aim of the project is to develop a solution for mapping power consumption in a factory that is affordable for small and medium-sized manufacturers. To do this, we will use power meters. To avoid interfering with the operation and design of machines, we will measure the magnetic field using Rogowski coils and a power network analyzer equipped with an MQTT client and a Wi-Fi or LTE modem. As a pilot test, we will install at least five sensors on different machines at MJ Polymers.
As part of the MeMOM platform, we will develop a cloud component responsible for collecting, processing and distributing data from the energy consumption sensors. We will ensure horizontal scalability of the component to provide the necessary performance in the scenario of simultaneous and intensive reporting from multiple IoT devices. The component will aggregate and combine data from individual sensors with production status reports to link consumption, processing stage and product.
Finally, we will develop an analytics component that will enable multi-dimensional data analysis on a process-by-process basis in the context of the machine, peripherals, raw material (e.g. pellets), production stage, product (per unit and for the entire order) and any semi-finished products. Within the component, for each of the above dimensions, we will identify and visualize hotspots indicating the highest energy consumption and the largest deviations in energy consumption in relation to comparable products, processes, equipment or a selected time period.
Expected results
The primary outcome of the project will be for MJ Polymers to understand energy consumption patterns and obtain data to enable reliable pricing of customer orders. Analysis of these patterns will enable the manufacturer to make changes to optimise power consumption and ultimately reduce direct operating costs. These changes may include changes to production planning and scheduling, batch sizes, power sharing between peripheral equipment, changing operator instructions for turning machines on and off, or replacing older generation machines with less energy-intensive ones.
Reducing power consumption also reduces the carbon footprint of production operations. In addition, by receiving real-time data on energy consumption, the manufacturer can identify any deviations from the norm and correct them immediately, preventing energy loss and extending the life of machinery. This could include unscheduled maintenance or the need for recalibration.
By designing our solution, the manufacturer will be able to make data-driven decisions about energy procurement, such as installing renewable energy sources or optimizing energy purchase tariffs.
Ultimately, the manufacturer will have accurate data to calculate and report the carbon footprint of its operations and individual products, which will enable it to work with customers who require it, and will also have a positive impact on the company's image. Companies that invest in technologies that reduce energy consumption are seen as socially and environmentally responsible, which can attract customers and employees. Such companies can also build better relationships with local communities and authorities.
What's next?
We invite you to follow our blog, where we will report on the next steps of the HOT_SENSE project and the development of the MeMOM platform aimed at automation and green transformation of production.
The project HOT_SENSE has received funding from the European Union’s Horizon Europe research and innovation programme under the GA 101058613. This publication reflects only MASTA's view. The European Commission is not liable for any use that may be made of the information therein.
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