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Bundling analytical capacities to understand phase formation in recycling of functional materials
(2019)
Transitioning from combustion engine-driven transportation to e-mobility demands a paradigm shift – from a system geared to maximize energy efficiency (i.e. fuel consumption) to a system that may be constrained by the availability of high technology (critical) metals required for electrical energy storage systems and drives. In the wake of these developments efforts in securing new resources of these metals from recycling of end-of-life products are increasing steadily.
Recycling of Li-Ion batteries has recently been evaluated. The results pinpoint to a critical need for understanding slag Formation and its dependence on metal components like Mn under extreme conditions. This will allow researchers to predict optimal Operation setting and to react quickly to changing market demands (which may be Li or Co at one point but may also shift to Ni or rare earth elements (REE)). The long-term goal is to control the formation of specific phases in slags allowing for a Maximum yield of elements of interest and optimal recovery in the separation processes that follows. The combination of data on the physical micro structure and local chemistry of the multi-Phase products during and after processing will help to understand and derive thermodynamic and kinetic data on its formation.
In this paper we are giving an overview on the analytical challenges and approaches to provide robust data on local element concentration and species (especially Mn which is a common component of next generation Li-ion batteries cathodes), spanning the dimensions from the nanometer scale to the bulk material. The complementary interactions of X-rays and electrons make them ideal probes to collect Interface and “in-depth” information. Before- and -after studies as well as in situ structural changes and Phase (trans)formation, changes in elemental and elemental species (e.g. oxidation state) distribution may be tracked by X-ray diffraction (XRD), X-ray fluorescence microscopy and X-ray Absorption spectroscopy. The application of such advanced analytical tools will not only provide essential clues during early lab-based experiments towards the development of new recycling technologies, but may also be deployed for on-line and in-line monitoring of industrial processes.
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.
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.
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.