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In this contribution a Raman spectrometer based control structure for the heating of a desorption column is proposed. For this purpose calibration experiments for the absorption of carbon dioxide using monoethanolamine solutions are carried out and calibration models are developed to measure both carbon dioxide liquid loads and monoethanolamine mass fractions. The calibration experiments are supported by online NMR spectroscopy to accurately measure the appearance of all species in the electrolyte system. Both models are tested during the plant operation of a mini-plant for the oxidative coupling of methane and the proof of concept for the control structure is given. The Raman spectroscopy implemented in the ATEX conform mini-plant shows a reliable and robust performance being even indifferent to impurities hindering the GC analysis.
Recent technical developments of NMR instruments such as in acquisition electronics and probe design allow detection limits of components in liquid mixtures in the lower ppm range (approx.. 5–10 ppm amount of substance). The major advantage of quantitative NMR spectroscopy (qNMR) is that it is a direct ratio method of analysis without the need of calibration. This means that the signal for a specific NMR-active nucleus (e.g., a proton) in an analyte can be compared and quantified by reference to a different nucleus of a separate compound, comparable to a counting of spins in the active volume of the spectrometer.
A special application of qNMR in technical mixtures is the observation in the gas phase, which is rarely applied compared to liquid and solid NMR studies. Because of the low density it results in a reduced sensitivity, which can be improved by applying pressure. Therefore a high-pressure NMR setup was developed based on a commercially available NMR tube made of zirconia. This is currently tested up to 20 MPa, but can be extended up to 100 MPa with regard to pressure rating of its components. This work shows results of gas-phase application on natural-gas like reference gas mixtures produced at BAM, as well as investigations on liquefied petroleum gas mixtures (LPG) with high accuracy provided in constant-pressure piston cylinders.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
The field of gas metrology is dealing with the development and improvement of gas analytical methods, as well as production of highly accurate primary reference standards. These are prepared at national metrological institutes (NMI) and represent the highest national level of traceability. Used for certification of secondary standards provided by specialty gas distributors these are very important for a high number of industrial sectors, which have to proof traceability due to legal or regulatory affairs, e.g., natural gas suppliers. Thus, improvements in the production and certification of these mixtures will have a direct influence on gas industry.
Due to its direct correlation to the number of spins within active sample volume quantitative NMR spectroscopy (qNMR) is a highly promising method with absolute comparison abilities in complex systems. Especially for liquefied petroleum gases (LPG) it has the ability of studying the unmodified sample at same conditions like in the cylinder. In contrast to gas chromatography no preparation steps and no changes like evaporation of the sample are necessary.
In this work we show the most recent results of our investigations on highly accurate LPG mixtures provided in constant-pressure piston cylinders. A dynamic pressure-resistant setup allows for sampling and circulation of the samples to ensure a homogeneous withdrawal from the cylinder. This is presented on examples of a commercial multicomponent LPG mixture obtained from specialty gas distributor, as well as two cylinders from the key comparison CCQM-K119 at highest level of international metrology.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of micro¬plastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermo-analytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.
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).
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.
The use of benchtop-NMR instruments is constantly increasing during the recent years. Advantages of being affordable, portable and easy-to-operate without the need for trained staff make them especially interesting for industrial applications in quality control. However, applications of NMR spectroscopy as an online PAT tool are still very rare but offer a huge potential for process optimization and control. A key task to exploit this potential is hardware field integration of the lab-instruments in a rough environment of a chemical plant. Additionally, developments in automation and data evaluation are mandatory to ensure a robust unattended operation with low maintenance requirements. Here, we show an approach of a fully automated analyzer enclosure considering explosion safety, field communication, as well as environmental conditions in the field.
Temperature sensitivity is still a limitation of benchtop-NMR instruments in flow applications. Recent developments of manufacturers allow for limited operation at static temperature levels, however, a dynamic system for continuous operation is still not available. Using a prototype system offering a larger bore, active temperature shielding studies with thermostated air were performed evaluating the performance.
Automated data evaluation of NMR spectra using a modular indirect hard modeling (IHM) approach showed good results and flexibility. 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, offering new ways to simultaneously evaluating large numbers of different models.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2023)
Companies of chemical industry find themselves more often in a rapidly changing environment, e.g., due to variability of raw material quality or energy costs and efficiency. Process optimization and new process concepts become more and more important. For example, flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds like exothermic reactions with high heat dissipation.
Highly automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis approach, which can even serve as reliable reference method for further calibration-dependent PAT applications (e.g., NIR or Raman spectroscopy).
Based on experiences from earlier field studies an improved NMR analyzer enclosure setup was developed and built, including the option of a secondary method (e.g., optical spectroscopy). Integrated control systems allow for a flexible implementation based on the available automation infrastructure at the chemical plant or pilot plant setup. In the future, fully integrated and intelligently interconnecting “smart” PAT systems and processes have the potential speed up the setup of production equipment for chemicals and pharmaceuticals and therefore help to reduce the time-to-market.
Industrial applications of low-field NMR spectroscopy for process and quality control of silanes
(2020)
The combination of different silanes as starting materials and as a product of hydrolysis by several alcohols or water creates a range of hundreds of technical products for a wide range of applications. In recent years, functional trialkoxysilanes have proven to be multi-purpose organosilanes. Applications range from weather protection of buildings to additives for glass fiber industry, sealants, adhesives, coatings and paints to the modification of polymers.
Commercial benchtop NMR spectrometers have the potential to be used in silane chemistry as an online method for reaction monitoring and quality control. NMR nuclei of interest for silane products are 1H and 29Si. In a joint research cooperation between EVONIK and BAM, the applicability of low-field NMR spectroscopy for the chemical analysis of silanes was evaluated. It was shown how it can extend the application range where existing technologies like NIR, Raman, UV/VIS, etc. cannot be used quantitatively due to a lack of reference data.
In a first case study the process of hydrolysis and condensation was observed using online NMR analysis. For this purpose, the substituents of a trialkoxysilane are first hydrolyzed by adding water and corresponding silanols are formed, which can then bind to materials via SiOH functions and crosslink to form siloxane units. Another case study was dealing with the kinetics of the cleavage of a cyclic silane compound. Online NMR analysis was used both in the laboratory and in the manufacturing plant. For this purpose, a fully automated containment system was used, which enables the use of a commercial NMR spectrometer in ATEX-environments. In the third case study presented, quantitative 1H-NMR spectra were acquired on product mixtures of a trialkoxysilane and other components such as organic stabilizers, organotin compounds, an aromatic amine and organic peroxides. An automatic evaluation method based on Indirect Hard Modeling (IHM) was developed.
Recent technical developments of NMR instruments such as in acquisition electronics and probe design allow detection limits of components in liquid mixtures in the lower ppm range (approx.. 5–10 ppm amount of substance). The major advantage of quantitative NMR spectroscopy (qNMR) is that it is a direct ratio method of analysis without the need of calibration. This means that the signal for a specific NMR-active nucleus (e.g., a proton) in an analyte can be compared and quantified by reference to a different nucleus of a separate compound, comparable to a counting of spins in the active volume of the spectrometer.
Technical mixtures can be investigated online directly next to a process setup by using flow probes. This makes it a promising method for process analytical applications, especially during process development in laboratory and pilot plant scale. With the growing market of Benchtop devices based on permanent magnets nowadays an integration of NMR spectroscopy in an industrial environment becomes reasonable.
A special application of qNMR in technical mixtures is the observation in the gas phase, which is rarely applied compared to liquid and solid NMR studies. Because of the low density it results in a reduced sensitivity, which can be improved by applying pressure. Therefore a high-pressure NMR setup was developed based on a commercially available NMR tube made of zirconia. This is currently tested up to 20 MPa, but can be extended up to 100 MPa with regard to pressure rating of its components. This work shows results of gas-phase application on natural-gas like reference gas mixtures produced at BAM, as well as investigations on liquefied gas mixtures with high accuracy provided in piston cylinders.
Besides that amine gas treatment and hydroformylation in a microemulsion represent two other examples of applications in process analytical technology. These show the potential of combination of online NMR spectroscopy with other spectroscopic methods, especially during model development for data evaluation.
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 environments 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.
Validation report on NMR
(2017)
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. Smart sensors enable concepts like self-diagnostics, self-calibration, and self-configuration/ parameterization whenever our current automation landscape allows it.
Here we summarize the currently discussed general requirements for process sensors 4.0 and introduce a smart online NMR sensor module as example, which was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (www.consensspire.eu).