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Currently research in chemical manufacturing moves towards flexible plug-and-play approaches focusing on modular plants, capable of producing small scales ondemand with short down-times between individual campaigns. This approach allows for efficient use of hardware, a faster optimization of the process conditions, and thus, an accelerated introduction of new products to the market. Driven mostly by the search for chemical syntheses under biocompatible conditions, so-called “click” chemistry rapidly became a growing field of research. The resulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via comparably straightforward and robust analysis techniques. Here we report on a fast and reliable calibration-free online high field NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement with a time interval of 20 s per spectrum, and a robust, automated algorithm to interpret the obtained data. All spectra were acquired using a 500 MHz NMR spectrometer (Varian) with a dual band flow probe having a 1/16 inch polymer tubing working as a flow cell. Single scan 1H spectra were recorded with an acquisition time of 5 s, relaxation delay of 15 s. As a proof-of-concept, the thiol-ene coupling between N-boc cysteine methyl ester and allyl alcohol was conducted in non-deuterated solvents while its time-resolved behaviour was characterised with step tracer experiments Through the application of spectral modeling the signal area for each reactant can be deconvoluted in the online spectra and thus converted to the respective concentrations or molar ratios. The signals which were suitable for direct integration were used herein for comparison purposes of both methods.
Driven mostly by the search for chemical syntheses under biocompatible conditions, so called "click" chemistry rapidly became a growing field of research. The resulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via comparably straightforward and robust analysis techniques possessing short set-up times. Here, we report on a fast and reliable calibration-free online NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement of 1H spectra with a time interval of 20 s per spectrum, and a robust, fully automated algorithm to interpret the obtained data. As a proof-of-concept, the thiol-ene coupling between N-boc cysteine methyl ester and allyl alcohol was conducted in a variety of non-deuterated solvents while its time-resolved behaviour was characterized with step tracer experiments. Overlapping signals in online spectra during thiol-ene coupling could be deconvoluted with a spectral model using indirect hard modeling and were subsequently converted to either molar ratios (using a calibrationfree approach) or absolute concentrations (using 1-point calibration). For various solvents the kinetic constant k for pseudo-first order reaction was estimated to be 3.9 h-1 at 25 °C. The obtained results were compared with direct integration of non-overlapping signals and showed good agreement with the implemented mass balance.
Ugly Spectra and Lousy interfaces – Challenges for Compact NMR Spectroscopy in Process Control
(2016)
With the introduction of advanced process analytical technology, the closeness of key process variables to their limits can be directly controlled and the product can be classified or even released in real time.
Compact NMR instruments can make NMR spectroscopy accessible in industrial and harsh environments for process control.
Smart functions of sensors simplify their use and enable plug-and-play, even though they are more complex. This is particularly important for, self-diagnostics, self-calibration and self-configuration/parameterization. Intelligent field devices, digital field networks, Internet Protocol (IP)-enabled connectivity and web services, historians, and advanced data analysis software are providing the basis for the future project “Industrie 4.0” and Industrial Internet of Things (IIoT).
Important smart features include connectivity and communication ability according to a unified protocol (OPC-UA currently most widely discussed), maintenance and operating functions, traceability and compliance, virtual description to support a continuous engineering, and well as interaction capabilities between sensors. This is a prerequisite for the realization of Cyber Physical Systems (CPS) within these future automation concepts for the process industry. Therefore, smart process sensors enable new business models for users, device manufacturers, and service providers.
The departure from current automation to smart sensor has already begun. Further development is based on the actual situation over several steps. Possible perspectives will be via additional communication channels to mobile devices, bidirectional communication, integration of the cloud and virtualization. The integration of virtual runtime environments can provide a more flexible topology for process control environments.
The talk summarizes the currently discussed requirements to process sensors 4.0 and introduces an online NMR sensor as an example, which was developed in the EU project CONSENS.
Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylation of long-chained olefins is studied in a mini-plant, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro¬emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the high sensitivity of this multi-phase system with regard to changes in temperature and composition demands a continuous observation of the reaction to achieve a reliable and economic plant operation. For that purpose, we tested the potential of both online NMR and Raman spectroscopy for process control. The lab-scale experiments were supported by off-line GC-analysis as a reference method.
A fiber optic coupled probe of a process Raman spectrometer was directly integrated into the reactor. 25 mixtures with varying concentrations of olefin (1-dodecene), product (n-tridecanal), water, n-dodecane, and technical surfactant (Marlipal 24/70) were prepared according to a D-optimal design. Online NMR spectroscopy was implemented by using a flow probe equipped with 1/16” PFA tubing serving as a flow cell. This was hyphenated to the reactor within a thermostated bypass to maintain process conditions in the transfer lines.
Partial least squares regression (PLSR) models were established based on the initial spectra after activation of the reaction with syngas for the prediction of unknown concentrations of 1-dodecene and n-tridecanal over the course of the reaction in the lab-scale system. The obtained Raman spectra do not only contain information on the chemical composition but are further affected by the emulsion properties of the mixtures, which depend on the phase state and the type of micelles. Based on the spectral signature of both Raman and NMR spectra, it could be deduced that especially in reaction mixtures with high 1-dodecene content the formation of isomers as a competitive reaction was dominating. Similar trends were also observed during some of the process runs in the mini-plant. The multivariate calibration allowed for the estimation of reactants and products of the hydroformylation reaction in both laboratory setup and mini-plant.
Modular chemical production is a tangible implementation of the digital transformation of the specialty chemicals process industry. In particular, it enables acceleration of process development and thus faster time to market by flexibly interconnecting and orchestrating standardized physical modules and bringing them to life. For this purpose, specific (chemical) sensors of process analytics are needed, preferably without lengthy calibration or spectroscopic model development.
An excellent example of a "direct" analytical method is online nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy meets the requirements of a direct analytical method because of the direct correlation between the signal area in the spectrum ("counting" the nuclear spins) and the analyte amount of substance concentrations. It is also extremely linear over the concentration range.
With the availability of compact benchtop NMR instruments, it is now possible to bring NMR spectroscopy directly into the field, in close proximity to specialized laboratory facilities, pilot plants, and even industrial-scale production facilities. The first systems are in TRL 8 (Qualified System with Proof of Functionality in the Field).
The presentation will discuss the many building blocks of online nuclear magnetic resonance spectroscopy, from flow cells to automated data analysis.
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 while being based on “chemical” information.
As an example, a fully automated NMR sensor is introduced, using a given pharmaceutical lithiation reaction as an example process within a modular pilot plant. Therefore, a commercially available benchtop NMR spectrometer was adapted 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.
A full integration and intelligent interconnection of such systems and processes progresses only hesitantly. The talk should encourage to re-think digitization of process industry based on smart sensors, actuators, and communication more comprehensively and informs about current technical perspectives such as the “one-network paradigm”, edge computing, or virtual machines. These give smart sensors, actuators, and communication a new perspective.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs.
The talk also generally covers current aspects of high-field and low-field online NMR spectroscopy for reaction monitoring and process control.
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.
The talk presents current research activities towards process control with compact NMR and reflects “cultural differences” between the interdisciplinary parties involved.
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.