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Introduction
NMR spectroscopy is one of the most important analytical methods in organic chemistry. While most analyses are carried out qualitatively with the aim of substance identification and structure elucidation, quantitative NMR spectroscopy (qNMR) is increasingly gaining importance in research and industry. qNMR provides the most universally applicable form of direct purity determination without need for reference materials of impurities or the calculation of response factors but only exhibiting suitable NMR properties.
Methods
One of the most attractive features of quantitative NMR spectroscopy is that the NMR peak areas can be used directly for concentration quantification without further calibration. Another advantage of NMR spectroscopy is that the method has a high linearity between absolute signal area and sample concentration, which makes it an absolute analytical comparison method that is independent of the matrix. This enables automated robust data evaluation strategies that can be used for online applications of qNMR spectroscopy.
Jancke et al. proposed NMR spectroscopy as a relative primary analytical method because it can be fully described by mathematical equations from which a complete uncertainty budget can be derived, allowing it to be used at the highest metrological level. Weber et al. discussed in detail important aspects of the procedure that enable the realisation of low measurement uncertainties in qNMR measurements. Since certification of CRM requires expanded mea¬sure¬ment uncertainties of less than 0.5 % (relative), the work of Weber et al. demonstrated for the first time that qNMR can fulfil this criterion.
Results
To date, further comparative studies have been carried out in metrology and industry, demonstrating the performance of quantitative NMR spectroscopy and further reducing measurement uncertainties. The development of validation concepts and the commercial availability of suitable certified reference materials facilitate the application, especially in the usually highly regulated industrial environment. Users can thus accelerate the development of analytical methods. The talk will cover a wide range of topics from current metrological activities to new challenges for qNMR spectroscopy and also deals with aspects such as validation and accreditation.
Innovative aspects
• qNMR provides the most universally applicable form of direct purity determination
• Expanded measurement uncertainties lower than 0.15 % (relative) possible
• Benchtop NMR instruments increasingly used for qNMR spectroscopy
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further Process Analytical Technology (PAT) applications. Using the available datasets, a second data analysis approach based on artificial neural networks (ANN) was evaluated. Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
At the end of the article, ideas for solutions are discussed in order to speed up the implementation of new special products from the point of view of process analytics and to network the existing process chains more closely.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further Process Analytical Technology (PAT) applications. Using the available datasets, a second data analysis approach based on artificial neural networks (ANN) was evaluated. Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
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.
The competitiveness of the process industry is based on ensuring the required product quality while making optimum use of equipment, raw materials and energy. Chemical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. The potential of digital technologies belongs to these.
One way is knowledge-based production, taking into account all essential equipment, process and regulatory data of plants and laboratories. Today, the potential of this data is often not yet consistently used for a comprehensive understanding of production. Another approach uses flexible and modular chemical plants, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products. Digital transformation is enabling completely new production concepts that are being used increasingly. Intensified continuous production plants also allow for difficult to produce compounds.
This contribution aims to encourage a more holistic approach to the digitalization and use of machine-assisted methods in (bio) process engineering by introduction of integrated and networked systems and processes, which have the potential to speed up the high-quality production of specialty chemicals and pharmaceuticals.
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.
Chemical companies must find new paths to successfully survive in a changing environment. The potential of digital technologies belongs to these. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short down-times between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds.
Therefore, fully automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. The advances of a fully automated NMR sensor were exploited, using a given pharmaceutical lithiation reaction as an example process within a modular pilot plant. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as , e.g., explosion safety, field communication, and robust evaluation of sensor data. It was thereof used for direct loop advanced process control and real-time optimization of the process. NMR appeared as preeminent online analytical tool and allowed using a modular data analysis tool, which even served as reliable reference method for further PAT applications.
In future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Chemical companies must find new paths to successfully survive in a changing environment. The potential of digital technologies belongs to these. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short down-times between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds.
Therefore, fully automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. The advances of a fully automated NMR sensor were exploited, using a given pharmaceutical lithiation reaction as an example process within a modular pilot plant. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as , e.g., explosion safety, field communication, and robust evaluation of sensor data. It was thereof used for direct loop advanced process control and real-time optimization of the process. NMR appeared as preeminent online analytical tool and allowed using a modular data analysis tool, which even served as reliable reference method for further PAT applications.
In future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Accelerating chemical process development and manufacturing along with quick adaption to changing customer needs means consequent transformation of former batch to continuous (modular) manufacturing processes. These are justified by an improved process control through smaller volumes, better heat transfer, and faster dynamics of the examined reaction systems.
As an example, for such modular process units we present the design and validation of an integrated nuclear magnetic resonance (NMR) micro mixer tailor‐made for a desired chemical reaction based on computational modelling. The micro mixer represents an integrated modular production unit as an example for the most important class of continuous reactors. The quantitative online NMR sensor represents a smart process analytical field device providing rapid and non‐invasive chemical composition information without need for calibration. We describe the custom design through computational fluid dynamics (CFD) for the demands of the NMR sensor as well as for the given reaction conditions. The system was validated with an esterification reaction as an example for a chemical reaction process.
Systems utilizing such an online NMR analyser benefits through short development and set‐up times based on “modular” spectral models. Such models can simply be built upon pure component NMR spectra within minutes to a few hours (i.e., assignment of the NMR signals to the components) instead of tedious DoE calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). The approach was validated for typical industrial reactions, such as hydrogenations or lithiations.
Analysis of dynamic systems
(2019)
Monitoring specific information (i.e., physico-chemical properties, chemical reactions, etc.) is the key to chemical process control when looking at dynamic systems, and quantitative online NMR spectroscopy is the method of choice for the investigation and understanding of dynamic multi-component systems. NMR provides rapid and non-invasive information, and due to the inherent linearity between sample concentration and signal intensity, peak areas can be directly used for quantification of multiple components in a mixture (without the need for any further calibration). This is one of the most attractive features of quantitative NMR spectroscopy. With the launch of devices covering magnetic field strengths from 40 to 90 MHz, so called compact or benchtop NMR systems, this analytical method is now reaching a sufficient degree of compactness and operability for an application outside of very specialized laboratories.
Whilst there are also many other tools available to examine various analytical parameters from dynamic processes, such as mass spectrometry, (near) infrared or Raman spectroscopy, each of these tools can only really be used independently. How can we examine and compare all data describing a particular chemical reaction? How can we visualize information rich, specific, or direct methods together with less specific but established analytical methods? And how can we transfer calibration information to the most appropriate process analytical method or method combination? Quantitative NMR spectroscopy (qNMR) has the potential to substitute offline laboratory analysis for calibration purposes by delivering quantitative reference data as an online method.
The workshop briefly presents the current state of the art of the analysis of dynamic systems by online NMR spectroscopy and analytical data fusion, with the remaining time being used for questions and open discussion with the attendees.