The objective of the SPEAR was to develop an optimization platform that improves industrial production processes with regard to
energy-related aspects. One focus of the project was the energy optimization of production processes, production lines and
industrial buildings. The developed platform can optimize the energy consumption of existing and new plants. The results are
applicable in the context of a virtual commissioning as well as to running production systems. The optimization platform can be
used as a local application or as a cloud-based service and relies on optimization algorithms that provide a forecast of the
expected energy consumption over a defined period of time.
As part of the project, digital twins of individual components were extended by a corresponding energy model, allowing complex
(mechanical) systems to be constructed. A wizard was developed to simplify and speed up the creation process. The models
themselves were neutrally defined and built on the Functional Mock-up Interface (FMI) in combination with the AutomationML (AML)
data format. This combination allows to describe dynamic behavior as well as physical data and meta information of components,
plants and lines.
By accurately calculating energy consumption, industrial customers can save on energy costs. Energy-intensive processes, for
example, can be shifted to time windows where sufficient and more cost-effective energy is available. This plays a significant
role, for example, in the use of renewable energies such as wind power or solar energy. In addition to improving the use of
renewable energy by matching production schedules to availability periods, utilities can more easily design, manage, and balance
the power grid. This reduces overall energy demand. At the same time, industrial customers are supported in reducing their
effective emissions - which is becoming increasingly important due to legal and regulatory aspects.
let's dev was in charge of WP5 "Implementation and Interfaces of the Optimization Platform" and was responsible for the execution
and coordination of all activities regarding the SPEAR Optimization Platform: from the analysis of the requirements to the
realization of the demonstrator.
The user-centered design approach was followed from the beginning and the target group was described with personas. The insights
gained were iteratively integrated into the demonstrator. In addition to the user-centered design, the technical specification
and implementation of the cloud application was also a major part of our work. Thus, different communication protocols and
interfaces were realized to meet the needs of partners and use cases. Apache Kafka was a central technology of the optimization
platform.
A major innovation of SPEAR is the mirroring of the energy consumption of the production plant by simulating extended behavioral
models on low-cost hardware. This hardware is also used for embedded hardware-in-the-loop (HIL) simulations that run in parallel
with the real plant, so that the digital twin of a plant is extended by a digital shadow. This allows highly flexible and
cost-effective approaches to be explored, implemented, and production system planning and execution optimized through accurate
simulation of production processes. This supports efforts such as Industry 4.0 or Smart Industry.
In relation to the focused production facilities, the German SPEAR consortium reflected the entire value chain including machine
and simulation experts, as well as experts for modeling energy consumers and suppliers from renewable energies. Both large and
medium-sized manufacturing companies as well as academic institutions were involved. The balanced competencies enabled the
project's concepts to be implemented and standardization efforts to be advanced.
