Resource efficiency for the energy transition How demand-oriented control of peripheral systems in production helps ensure energy efficiency

Source: Press release

Germany — Production and sustainability — what was previously believed to be a conflict is being addressed with the project for demand-oriented control of peripheral systems in production (Bestperi) and the integration of data-driven methods in production technology.

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In the project, two electroplating lines of B+T serve as a pilot for a consistent data chain for the industry-oriented identification of resource efficiency potentials.
In the project, two electroplating lines of B+T serve as a pilot for a consistent data chain for the industry-oriented identification of resource efficiency potentials.
(Source: B+T Oberflächentechnik)

The demand for resources in production technology can often not be avoided, but rather their efficient use along the value chain can be improved. However, in addition to the often non-existent, but necessary, transparent data situation, what is missing is above all the concretely implemented added value from data for personnel, value creation and the environment. Large and, to date, mostly undiscovered potentials for increasing resource efficiency exist above all in the analysis of entire process chains. However, when leaving the area of optimising individual processes, the complexity increases as well as the amount and variety of data to be handled. This is because causal effects between the process steps and their impact on the resulting quality and the total resource consumption have to be taken into account simultaneously for a large number of process steps. At this point, the use of digital technologies and machine learning will enable future production technology to significantly increase the resource efficiency of the overall system. The current challenge for manufacturing companies is therefore, on the one hand, the systematic digitalisation of their existing machines, including the supply periphery for data collection, and, on the other hand, the integration of digital services to generate a data benefit along the process chain, both from an ecological and economic perspective.

“The aim of Bestperi is to enable manufacturing companies to reduce their CO2 emissions in a timely manner through the use of data-driven methods by using necessary resources for production more efficiently. The key to this is to supply the processes according to demand. For this, we need transparency along the entire manufacturing chain and must develop digital software services to control the plants,” says Frank Benner, Managing Director of B+T Oberflächentechnik.

Participating in the joint project are: An industrial user, data generators and IT expert from the resource-intensive field of electroplating, B+T Oberflächentechnik (B+T), a research institution with many years of experience in the field of IoT data — the Machine Tool Laboratory WZL of RWTH Aachen University — and an operational-technical expert for data acquisition and development of control systems, Ditec. In this way, the project covers the active use of data in the user context with a focus on predictive maintenance, process control and resource feedback along the value chain. The joint project Bestperi provides a holistic approach to the principle of circular economy with a demand-oriented supply of operating resources and control of peripheral systems with a high degree of transferability from electroplating technology to production technology.

Project scope for achieving the objective

In the project, two electroplating lines of B+T serve as a pilot for a consistent data chain for the industry-oriented identification of resource efficiency potentials. In the project, machine learning methods are used to combine status data along the entire process chain and, based on this, to forecast ecologically and economically valuable key figures. Finally, the trained models are integrated into production as digital services. The industrial application of the services realises the required combination of availability and analysis of production data. Starting with exploratory data analysis to identify patterns, needs and waste, machine learning models are trained to reliably detect and predict these anomalies and patterns. The actual data benefit for increasing resource efficiency is achieved through the operational-technical feedback of action instructions and optimised control signals for the electrolyte feed, as well as rinsing units, transport trolleys, compressed air supply and chemical supply.

The success of the project is evaluated by the increase in resource efficiency by means of dynamic resource flow balancing, CO2 footprint and Ecoscore.

Specific utilisation

The data gathered in production at B+T will create a data chain with more than 100 stations. The project significantly promotes the company's strategic orientation towards the use of digital technologies and machine learning throughout the entire production chain. With the data-driven analysis and the anchored feedback into the control systems, a first-time benefit of data collection is created and thus a significant contribution to the return on investment. At the same time, this benefit serves the overriding reduction of primary resource consumption of material and energy: final metallisation effort, new coatings as well as waste water and waste are significantly reduced. Furthermore, transparency in production increases plant productivity in terms of availability and robustness.

With the establishment of digital services, throughput times are also shortened and thus the utilisation of the plants is optimised. Furthermore, the waste water capacities and rinsing bath quality can be actively controlled in advance through the plant-differentiated consumption depending on the order situation. Based on this, the entire resource procurement is linked to the production planning. The consistent recirculation of dragged-out chemicals into the electrolytes before mixing with other substances reduces carry-over. According to calculations, 4-6 l of fresh water can be saved per drum alone. Furthermore, indirect costs are saved through the taxation of CO2 emissions. Significant customer benefits from data collection and processing are above all the high potentials on the side of product quality and cost efficiency.

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The possibility of rescheduling energy-intensive production processes to times of high availability of renewable energies results in lower-emission and more cost-effective production. The increased transparency and data situation result in simpler controlling and performance management in the area of sustainability, e.g. for the preparation of evidence for ISO:50001. These and other savings potentials must first be precisely determined and validated.

In addition to proactive action measures, the speed of reaction in ongoing production is considerably increased by the up-to-the-minute availability of statuses and recommendations. Furthermore, the safeguarding of secondary process status variables, the demand-controlled supply of operating resources, the precise localisation of faults in production and the targeted use of predictive maintenance will succeed.

The project will enable Ditec to delve deeply into production and process control, including the resource-intensive supply of operating materials. The expansion of services, including the documentation of emissions and resource consumption per product or per time unit on the basis of actual consumption data, can be based on the modular Progal control systems installed at more than 800 customers. For these customers, in turn, an increase in their own resource efficiency can be expected with the use of the services.

“There is no comparable development on the market to date for controlling resource efficiency for electroplating. The cooperation with a leading research institution in the field of data-driven methods in production technology enables us to gain a broader understanding, so that our own products, such as the Service Manager, can be further developed in the direction of digital technologies in a future-proof manner,” says Dr.-Ing. Siegfried Kahlich, Managing Director of Ditec.

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