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Organisationseinheit der BAM
The CCQM-K148.a comparison was coordinated by the BIPM on behalf of the CCQM Organic Analysis Working Group for NMIs and DIs which provide measurement services in organic analysis under the CIPM MRA. It was undertaken as a "Track A" comparison within the OAWG strategic plan. CCQM-K148.a demonstrates capabilities for assigning the mass fraction content of a solid organic compound having moderate molecular complexity, where the compound has a molar mass in the range (75 - 500) g/mol and is non-polar (pKow < −2), when present as the primary organic component in a neat organic solid and where the mass fraction content of the primary component in the material is in excess of 950 mg/g.
Participants were required to report the mass fraction of Bisphenol A present in one supplied unit of the comparison material. Participants using a mass balance method for the assignment were also required to report their assignments of the impurity components present in the material. Methods used by the seventeen participating NMIs or DIs were predominantly based on either stand-alone mass balance (summation of impurities) or qNMR approaches, or the combination of data obtained using both methods. The results obtained using thermal methods based on freezing-point depression methods were also reported by a limited number of participants. There was excellent agreement between assignments obtained using all three approaches to assign the BPA content.
The assignment of the values for the mass fraction content of BPA consistent with the KCRV was achieved by most of the comparison participants with an associated relative standard uncertainty in the assigned value in the range (0.1 - 0.5)%.
Modular plants using intensified continuous processes represent an appealing concept to produce pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes, and it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and includes a compact Nuclear Magnetic Resonance (NMR) spectrometer for online quality monitoring as well as a new model-based control approach. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including ro-bust evaluation of sensor data.
Here, we present alternatives for the quantitative determination of the analytes using modular, physically motivated models. These models can be adapted to new substances solely by the use of their corresponding pure component spectra, which can either be derived from experimental spectra as well as from quantum mechanical models or NMR predictors. Modular means that spec-tral models can simply be exchanged together with alternate reagents and products. Beyond that, we comprehensively calibrated an NIR spectrometer based on online NMR process data for the first time within an industrial plant. The integrated solution was developed for a metal organic reac-tion running on a commercial-scale modular pilot plant and it was tested under industrial conditions.
A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. “Chemputer” which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements.
Im Zuge der Digitalisierung der Prozessindustrie werden zunehmend modellbasiere Echtzeitoptimierungsverfahren eingesetzt, sog. „Advanced Process Control“. Mithilfe der sogenannten Modifier-Adaptation ist eine iterative Betriebspunktoptimierung auch mit ungenauen Modellen möglich, sofern zuverlässige Prozessdaten zur Verfügung stehen. Am Beispiel eines smarten Online-NMR-Sensors, der in einem EU-Projekt von der BAM entwickelt wurde, konnte das Konzept in einer modularen Produktionsanlage zur Herstellung eines pharmazeutischen Wirkstoffs erfolgreich getestet werden.
In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum.
The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.
In the following the contents of the files are explained.
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.
Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
(2019)
Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach
was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.
The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.