In the technology field of process intelligence, knowledge- and data-based MSO techniques (modeling, simulation, optimization) are developed and applied for optimized module and process design. The goal here is to identify suitable process windows in terms of efficiency, productivity and product quality. For this purpose, software modules are being developed for the virtual representation of the process operation, to enable autonomous processing.
Knowledge- and data-based MSO techniques will be further developed in this technology field to ensure autonomous process operation with optimum energy efficiency, productivity and product quality over wide operating windows. In addition, design tools are being developed for the innovative process design of individual components. The goal of this technology field is to use existing process data to empower knowledge-based process simulations with AI tools to enable reliable virtualization of process operations. This hybrid model will be coupled with process automation (i.e. control and regulation; see the corresponding technology field) to enable autonomous driving with real-time optimization. Here again, at the interface between the hybrid simulation and the process control system, AI tools are used in the form of adaptively trained surrogate models. The central goals in this technology field are thus
- (a) to increase the availability of simulation components to represent the design and operation of individual modules in the demonstrator processes (e.g., porous reactors, reactor fluid dynamics) with interfaces to process data for rapid calibration, and
- (b) to develop process intelligence as described above, demonstrating feasibility and utility in miniplant format.