In process industries, e.g., the production of aluminum or plastics, efficiency-improving process optimizations focus on the high consumption of raw materials and energy. Production processes are highly complex in many cases, though, and the consequences of changes as well as potential side effects are difficult to anticipate. Thus, until now thorough analyses are a real challenge.
Also in many cases the amount of data that need to be taken into account in the optimization process is too large for flexible adaptations or to try out new concepts that might lead to greater savings, e.g., by reducing waste or energy consumption. Another aim of process optimization is to improve quality in general while keeping energy consumption constant.
Any sort of restructuring of plants or production processes holds the risk of being a costly yet unprofitable investment whose outcome is known only long after the change is implemented.
One way to avoid this risk is being investigated in the MONSOON project that works on a "Model-based coNtrol framework for Site-wide OptimizatiON of data-intensive processes". The project aims to develop a system that will reliably simulate the consequences of process modifications and the restructuring of production plants. This will allow to forecast the potential benefits of process optimization.
To prepare the simulations, first a model of the current production process is specified. On this basis real sensor data and relevant additional information is attached to the process step in the model. The data thus captured are then made available for analysis and evaluation in a "Cross Sectorial Data Lab" specifically built for that purpose.
The model can then be used to describe changes in process parameters or the flow of the production process. The Data Lab will show the consequences to be expected from these changes. The algorithms that will be developed in the project for this type of analysis permit to use this basic approach in different industries and to adapt it to different needs for optimization.
In conducting the requirements analysis, FIT will also contribute its expertise in the Internet of Things and Industry 4.0 to the project. As the project's technical manager, FIT is in charge of defining the software architecture as well as developing and setting up the "Cross Sectorial Data Lab".
The MONSOON project is funded by the European Union as part of their Spire-02-2016 Horizon 2020 call.