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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.
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environ¬ments for advanced process monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.
Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. An increasing number of applications are reported. Robust field integration of NMR systems have to face explosion protection or integration into process control systems with short set-up times. Tis paves the way for industrial automation in real process environments.
Automated data preparation and analysis are cornerstones for a breakthrough of NMR techniques for process control. Particularly, robust chemometrics as well as automated signal processing methods have to be (further) developed especially for NMR spectroscopy in process control. This becomes even more important for so called “smart sensors” providing the basis for the future project “Industrie 4.0”, and Industrial Internet of Things (IIoT), along with current requirements to process control, model based control, or soft sensing. Data analysis techniques are available but currently mostly used for off-line data analysis to detect the causes of variations in the product quality.
Process analytical techniques are extremely useful tools for chemical production and manufacture and are of particular interest to the pharmaceutical, food and (petro-) chemical industries.
Today, mainly optical online methods are applied. NMR spectroscopy has a high potential for direct loop process control. Compact NMR instruments based on permanent magnets are robust and relatively inexpensive analysers, which feature advantages like low cost, low maintenance, ease of use, and cryogen-free operation. Instruments for online NMR measurements equipped with a flow-through cell, possessing a good signal-to-noise-ratio, sufficient robustness, and meeting the requirements for integration into industrial plants (i.e., explosion safety and fully automated data analysis) are currently not available off the rack.
A major advantage of NMR spectroscopy is that the method features a high linearity between absolute signal area and sample concentration, which makes it an absolute analytical comparison method which is independent of the matrix. This is an important prerequisite for robust data evaluation strategies within a control concept and reduces the need for extensive maintenance of the evaluation model over the time of operation. Additionally, NMR spectroscopy provides orthogonal, but complimentary physical information to conventional, e.g., optical spectroscopy. It increases the accessible information for technical processes, where aromatic-toaliphatic conversions or isomerizations occur and conventional methods fail due to only minor changes in functional groups.
As a technically relevant example, the catalytic hydrogenation of 2-butyne-1,4-diol and further pharmaceutical reactions were studied using an online NMR sensor based on a commercially available low-field NMR spectrometer within the framework of the EU project CONSENS (Integrated Control and Sensing).
Produzieren Sie schon oder kalibrieren Sie noch? – Online-NMR-Spektrometer als Smarte Feldgeräte
(2018)
Der Vortrag zeigt allgemeine Anforderungen an "smarte Feldgeräte" und deren Entwicklung in den vergangenen Jahren. Am Beispiel eines smarten Online-NMR-Sensors, der in einem EU-Projekt von der BAM entwickelt wurde, wird die Umsetzung der Anforderung aufgezeigt. Schließlich werden weitere Technologieanforderungen und Lösungsansätze vorgestellt.
Plastic pollution is the biggest environmental concern of our time. Breakdown products like micro- and nanoplastics inevitably enter the food chain and pose unprecedented health risks. In this scenario, bio-based and biodegradable plastic alternatives have been given a momentum aiming to bridge a transition towards a more sustainable future. Polyhydroxyalkanoates (PHAs) are one of the few thermoplastic polymers synthesized 100 % via biotechnological routes which fully biodegrade in common natural environments. Poly(hydroxybutyrate-cohydroxyhexanoate) [P(HB-co-HHx)] is a PHA copolymer with great potential for the commodity polymers industry, as its mechanical properties can be tailored through fine-tuning of its molar HHx content. We have recently developed a strategy that enables for reliable tailoring of the monomer content of P(HB-co-HHx).
Nevertheless, there is often a lack of comprehensive investigation of the material properties of PHAs to evaluate whether they actually mimic the functionalities of conventional plastics. We present a detailed study of P(HB-co-HHx) copolymers with low to moderate hydroxyhexanoate content to understand how the HHx monomer content influences the thermal and mechanical properties and to link those to their abiotic degradation. By increasing the HHx fractions in the range of 2 – 14 mol%, we impart an extension of the processing window and application range as the melting temperature (Tm) and glass temperature (Tg) of the copolymers decrease from Tm 165 ◦C to 126 ◦C, Tg 4 ◦C to − 5.9 ◦C, accompanied by reduced crystallinity from 54 % to 20 %.
Elongation at break was increased from 5.7 % up to 703 % at 14 mol% HHx content, confirming that the range examined was sufficiently large to obtain ductile and brittle copolymers, while tensile strength was maintained throughout the studied range. Finally, accelerated abiotic degradation was shown to be slowed down with an increasing HHx fraction decreasing from 70 % to 55 % in 12 h.
Hydroformylation of short-chained olefins has been established as a standard industrial process for the production of C2 to C6 aldehydes. Using aqueous solutions of transition metal complexes these processes are carried out homogeneously catalyzed. A biphasic approach allows for highly efficient catalyst recovery. Regarding renewable feedstocks, the hydroformylation of long-chained alkenes (> C10) in a biphasic system, using highly selective rhodium catalysts has yet not been shown. Therefore, the Collaborative Research Center SFB/TR 63 InPROMPT develops new process concepts, involving innovative tuneable solvent systems to enable rather difficult or so far nonviable synthesis paths. One possible concept is the hydroformylation of long-chained alkenes in microemulsions. For this, a modular mixer-settler concept was proposed, combining high reaction rates and efficient catalyst recycling via the application of technical grade surfactants. The feasibility of such a concept is evaluated in a fully automated, modular mini-plant system within which the characteristics of such a multiphase system pose several obstacles for the operation. Maintaining a stable phase separation for efficient product separation and catalyst recycling is complicated by small and highly dynamic operation windows as well as poor measurability of component concentrations in the liquid phases. In this contribution, a model-based strategy is presented to enable concentration tracking and phase state control within dynamic mini-plant experiments. Raman spectroscopy is used as an advanced process analytical tool, which allows for online in-situ tracking of concentrations. Combined with optical and conductivity analysis optimal plant trajectories can be calculated via the solution of dynamic optimization problem under uncertainty. Applying these, a stable reaction yield of 40 % was achieved, combined with an oil phase purity of 99,8 % (total amount of oily components in the oil phase) and catalyst leaching below 0.1 ppm.
The application of compact NMR instruments to continuously flowing samples at higher temperatures or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is typically not designed for compensation of such effects. Passive insulation is restricted by the small dimensions within the magnet borehole.
Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements using an optical thermo-graphy setup, a model predictive control was set up to minimize any disturbance effect on the permanent magnet from the probe or sample temperature.
This methodology will facilitate the application of variable-temperature shielding and, therefore, potentially extend the application of compact NMR instruments to flowing samples at higher temperature than the permanent magnet.
The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole.
Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature.
This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.
Um die internationale Spitzenposition deutscher Unternehmen der Prozessindustrie auch in Zukunft sicher zu stellen, müssen die Unternehmen ihre Prozesse und Geschäftsabläufe digitalisieren und gemeinsam mit der Forschung innovative neue Methoden, Apparate, Anlagen, Sensoren und Automatisierungstechnik sowie Datenkonzepte entwickeln. All dies erfordert den Aufbau neuer Fähigkeiten, Investition in Talente, interdisziplinäre Kommunikation zwischen verschiedenen Personen und Abteilungen und eine Bereitschaft zur Veränderung.