Current and future requirements to industrial analytical infrastructure-part 2: smart sensors.

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.

Current and future requirements to industrial analytical infrastructure-part 1: process analytical laboratories.

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).

High-speed, inline measurement of protein activity and inactivation processes by supercontinuum attenuation spectroscopy.

Some proteins such as catalase and glutamate dehydrogenase (GDH) are very sensitive to external factors such as irradiation or heat, which may cause inactivation. Since proteins are used in a wide field of applications, the entire activity has to be ensured during the whole process. By default, activity is measured by invasive and offline activity assays. To avoid the need for a time-consuming offline analysis, we developed an optical high-speed measurement technique, which may form the basis for the non-invasive inline control of enzyme processes e.g. in the textile or food industry. The technique is based on attenuation spectroscopy using a supercontinuum laser source in combination with multivariate data analysis, in particular partial least squares regression (PLSR). For verification of the approach, samples treated by various stresses were analyzed in parallel by activity assays and our new method. Applying this technique, we were able to determine the activity in the turbid catalase samples after heat treatment, addition of guanidine-HCl or irradiation with UV light by applying partial least squares regression. Furthermore, we demonstrate that the combination of broadband attenuation spectroscopy and PLSR enables us to determine also the activity of GDH in clear solutions after heat treatment.

Unsupervised Screening of Vibrational Spectra by Principal Component Analysis for Identifying Molecular Clusters.

Vibrational spectra are commonly used to study molecular interactions in solutions. However, the data analysis is often demanding and requires significant experience in order to obtain meaningful results. This study demonstrates that principal component analysis (PCA) can serve as an unsupervised tool for initial screening of non-ideal mixture systems. Taking the aqueous solutions of dimethyl sulfoxide (DMSO) as an example, PCA reveals-easily and fast-the two prominent stoichiometries at 1:2 and 1:1 molar DMSO:water ratio and significantly outperforms elaborate spectral profile analysis or common algorithms as indirect hard modeling (IHM) or multivariate curve resolution (MCR). The corresponding molecular 1:1 and 1:2 clusters are known to be dominating configurations in the solutions.