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The fourth industrial revolution (also known as “Industry 4.0”) promises to relieve people from manual and physical work by developing cyber-physical systems and smart automation and thereby achieving a more efficient, reliable and safe performance. This will change people’s work from being operators of the system into becoming strategical decision makers and flexible problem solvers. This shift will require a new division of work between people and technical systems, a new division of responsibilities and new ways of interacting with technology, which in turn will pose new demands on people. People will require different physical and cognitive capabilities, different competencies and new strategies to cope with the upcoming challenges. The focus of Industry 4.0 is therefore on designing systems with human in the centre: systems that will extend people’s capabilities, that will allow collaborative work and take advantage of performances in which either people or technology excel. This presentation will focus on the benefits of consideration of human-machine interaction in the context of industry 4.0.
Current and future requirements to industrial analytical infrastructure—part 2: smart sensors
(2020)
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 is versatile deployment in efficient process development and advanced optimum predictive process control.
The competitiveness of the chemical and pharmaceutical industry is based on ensuring the required product quality while making optimum use of plants, raw materials, and energy. In this context, effective process control using reliable chemical process analytics secures global competitiveness. The setup of those control strategies often originate in process development but need to be transferable along the whole product life cycle. 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). In laboratories and pilot plants, offline chemical analytical methods are frequently used, where inline methods are also used in production. Here, a transferability from process development to the process in operation would be desirable. This can be obtained by establishing PAT methods for production already during process development or scale-up. However, the current PAT (Bakeev 2005, Org Process Res 19:3–62; Simon et al. 2015, Org Process Res Dev 19:3–62) must become more flexible and smarter. This can be achieved by introducing digitalization-based knowledge management, so that knowledge from product development enables and accelerates the integration of PAT. Conversely, knowledge from the production process will also contribute to product and process development. This contribution describes the future role of the laboratory and develops requirements therefrom. In part 2, we examine the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality—also within process development or scale-up facilities (Eifert et al. 2020, Anal Bioanal Chem).
The successful shift to NDE 4.0 will not only require developing and embracing new technologies associated with the fourth industrial revolution or becoming an integral part of the overall Industry 4.0, but also developing and adopting new ways of working. It is undoubtful that people will remain in charge of the inspections. However, it is arguable if the current “procedure-following” “level I-III” paradigm can withstand the changes that come along NDE 4.0. With the increased autonomy and interconnectedness expected with NDT 4.0, the majority of traditional NDE tasks will no longer be needed. Instead, different skills, such as that of programming and adapting systems, as well as problem solving, will become vital for the inspections. Therefore, we suggest that a new paradigm is needed—one in which inspector roles and, thus, also the requirements will have to be reinvented. We expect the inspectors to be relieved from the tedious and error prone aspects of the current system and to take responsibility for increasingly complex automated systems and work in closer collaboration with other experts. Thus, we propose that the traditional inspector roles will be transformed into that of the system developer, caretaker and problem solver, each requiring a specific set of skills and assuming different responsibilities. In this talk, we will present the new roles and discuss the challenges that may arise with them.
The successful shift to NDE 4.0 will not only require developing and embracing new technologies associated with the fourth industrial revolution or becoming an integral part of the overall Industry 4.0, but also developing and adopting new ways of working. It is undoubtful that people will remain in charge of the inspections. However, it is arguable if the current “procedure-following” “level I-III” paradigm can withstand the changes that come along NDE 4.0. With the increased autonomy and interconnectedness expected with NDT 4.0, the majority of traditional NDE tasks will no longer be needed. Instead, different skills, such as that of programming and adapting systems, as well as problem solving, will become vital for the inspections. Therefore, we suggest that a new paradigm is needed—one in which inspector roles and, thus, also the requirements will have to be reinvented. We expect the inspectors to be relieved from the tedious and error prone aspects of the current system and to take responsibility for increasingly complex automated systems and work in closer collaboration with other experts. Thus, we propose that the traditional inspector roles will be transformed into that of the system developer, caretaker and problem solver, each requiring a specific set of skills and assuming different responsibilities. In this talk, we will present the new roles and discuss the challenges that may arise with them.
Nondestructive evaluation (NDE) is entering an era of the fourth industrial revolution and will undergo a major transformation. NDE is a vital part of industry and a successful move to NDE 4.0, it will require not just developing and embracing new technologies, but also developing and adopting new ways of working and becoming an integral part of the overall Industry 4.0. This will pose new challenges for the inspection personnel. To ensure the expected benefits from NDE 4.0, inspectors need stay in charge of the changing inspections. The promised autonomy and interconnectedness of NDE 4.0 will supersede the majority of traditional inspector tasks and will in turn require a different set of skills and raise different demands and challenges for the inspection personnel, thus conflicting the current “procedure-following”-“level I-III” paradigm. The new industry 4.0 technologies can be integrated into the current framework, but exploiting their full potential requires changes in the role of the inspectors. The inspectors will be relieved from the tedious and error-prone aspects of the current system. At the same time, they will need to take responsibility for increasingly complex automated systems and work in closer collaboration with other experts. We propose that the traditional inspector roles will be transformed into that of the system developer, caretaker, and problem solver, each requiring a specific set of skills and assuming different responsibilities. For full NDE 4.0, NDE must abandon its traditional role as a self-contained entity with well-defined boundaries and take its role in the wider system that is the industry 4.0.
The successful shift to NDE 4.0 will not only require developing and embracing new technologies associated with the fourth industrial revolution or becoming an integral part of the overall Industry 4.0, but also developing and adopting new ways of working. It is undoubtful that people will remain in charge of the inspections. However, it is arguable if the current “procedure-following” “level I-III” paradigm can withstand the changes that come along NDE 4.0. With the increased autonomy and interconnectedness expected with NDT 4.0, the majority of traditional NDE tasks will no longer be needed. Instead, different skills, such as that of programming and adapting systems, as well as problem solving, will become vital for the inspections. Therefore, we suggest that a new paradigm is needed—one in which inspector roles and, thus, also the requirements will have to be reinvented. We expect the inspectors to be relieved from the tedious and error prone aspects of the current system and to take responsibility for increasingly complex automated systems and work in closer collaboration with other experts. Thus, we propose that the traditional inspector roles will be transformed into that of the system developer, caretaker and problem solver, each requiring a specific set of skills and assuming different responsibilities. In this talk, we will present the new roles and discuss the challenges that may arise with them.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production
(2021)
Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and thus a quicker time to market by flexibly connecting and orchestrating standardised physical modules and bringing them to life (i.e., parameterising them) with digitally accumulated process knowledge.
We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes.