Abstract
Complex processes meet and need Industry 4.0 capabilities. Shorter product cycles, flexible production needs, and direct assessment of product quality attributes and raw material attributes call for an increased need of new process analytical technologies (PAT) concepts. While individual PAT tools may be available since decades, we need holistic concepts to fulfill above industrial needs. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (Part 1) and smart sensors (Part 2). Part 2 of this feature article series describes the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality. The smart sensor consists of (i) chemical and process information in the physical twin by smart field devices, by measuring multiple components, and is fully connected in the IIoT 4.0 environment. In addition, (ii) it includes process intelligence in the digital twin, as to being able to generate knowledge from multi-sensor and multi-dimensional data. The cyber-physical system (CPS) combines both elements mentioned above and allows the smart sensor to be self-calibrating and self-optimizing. It maintains its operation autonomously. Furthermore, it allows—as central PAT enabler—a flexible but also target-oriented predictive control strategy and efficient process development and can compensate variations of the process and raw material attributes. Future cyber-physical production systems—like smart sensors—consist of the fusion of two main pillars, the physical and the digital twins. We discuss the individual elements of both pillars, such as connectivity, and chemical analytics on the one hand as well as hybrid models and knowledge workflows on the other. Finally, we discuss its integration needs in a CPS in order to allow its versatile deployment in efficient process development and advanced optimum predictive process control.
Highlights
The ability of the process industry and its suppliers to sustainably deliver high-quality products at competitive prices and quickly adapt to evolving customer needs will be crucial to the future competitiveness of the process industry and its suppliers
We are convinced that the benefits of establishing and operating smart analytical labs, as mentioned in Part 1 [3] will become easier accessible via use of Process analytical technology (PAT) along the whole asset life cycle together with connectivity to smart sensors
The smart PAT sensor needs a combination of a chemical analytical device and process intelligence (Fig. 1)
Summary
The ability of the process industry and its suppliers to sustainably deliver high-quality products at competitive prices and quickly adapt to evolving customer needs will be crucial to the future competitiveness of the process industry and its suppliers. Integrated in a PAT environment it allows for a flexible and target-oriented predictive control strategy and can compensate variations of the process and raw material attributes In this contribution, we will shortly summarize requirements for smart sensors, revisiting the needs and requirements of smart field devices and linking the gathered physical and chemical information to process intelligence and control solutions inside of digital twin environments. Data science solutions for model switching will choose the best model being part of the digital twin for the current, control, optimization, or estimation task This can be supplemented with sematic sensors, in which expert knowledge is embedded—speeding up an implementation project of model-based real-time optimization (RTO) in the process industry [23]. Apart from specific process control objectives of a certain unit operation, this will revolutionize the overall production control strategy along the life cycle [28], enabling smooth scale-up and technology transfer
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