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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.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are 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 developed 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-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible 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.
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.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. 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 are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
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.
Ergot alkaloids are mycotoxins formed by fungi of the Claviceps genus, which are some of the most common contaminants of food and feed worldwide. These toxins are a structurally heterogeneous group of compounds, sharing an ergoline backbone. Six structures and their corresponding stereoisomers are typically quantified by either HPLC-FLD or HPLC-MS/MS and the values subsequently summed up to determine the total ergot alkaloid content. For the development of a screening method targeting all ergot alkaloids simultaneously, the alkaloids need to be transferred to one homogeneous structure: a lysergic acid derivative. In this study, two promising cleaving methods—acidic esterification and hydrazinolysis—are compared, using dihydroergocristine as a model compound. While the acidic esterification proved to be unsuitable, due to long reaction times and oxidation sensitivity, hydrazinolysis reached a quantitative yield in 40-60 min. Parallel workup of several samples is possible. An increasing effect on the reaction rate by the addition of ammonium iodide was demonstrated. Application of hydrazinolysis to a major ergot alkaloid mix solution showed that all ergopeptines were cleaved, but ergometrine/-inine was barely affected. Still, hydrazinolysis is a suitable tool for the development of a sum parameter screening method for ergot alkaloids in food and feed.
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.
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.
Ratiometric green–red fluorescent nanosensors for fluorometrically monitoring pH in the acidic range were designed from 80 nm-sized polystyrene (PS) and silica (SiO2) nanoparticles (NPs), red emissive reference dyes, and a green emissive naphthalimide pH probe, analytically and spectroscopically characterized, and compared regarding their sensing performance in aqueous dispersion and in cellular uptake studies. Preparation of these optical probes, which are excitable by 405 nm laser or LED light sources, involved the encapsulation of the pH-inert red-fuorescent dye Nile Red (NR) in the core of self-made carboxylated PSNPs by a simple swelling procedure and the fabrication of rhodamine B (RhB)-stained SiO2-NPs from a silane derivative of pH-insensitive RhB. Subsequently, the custom-made naphthalimide pH probe, that utilizes a protonation-controlled photoinduced electron transfer process, was covalently attached to the carboxylic acid groups at the surface of both types of NPs. Fluorescence microscopy studies with the molecular and nanoscale optical probes and A549 lung cancer cells confirmed the cellular uptake of all probes and their penetration into acidic cell compartments, i.e., the lysosomes, indicated by the switching ON of the green naphthalimide fluorescence. This underlines their suitability for intracellular pH sensing, with the SiO2-based nanosensor revealing the best performance regarding uptake speed and stability.
An accurate measurement of the amount fraction of hydrogen in gas mixtures is mandatory for practical applications, requiring methods that are fast, continuous, robust, and cost-effective. This study compares the performance of Raman and benchtop NMR process spectroscopy for determining the hydrogen amount fraction in gas mixtures. A setup was designed to integrate both techniques, enabling measurements of the same sample.
Tests were conducted with gravimetrically prepared gas mixtures of reference quality ranging from 1.20 cmol/mol to 85.83 cmol/mol of hydrogen. The results demonstrate that Raman spectroscopy provides superior performance, with a minimal root mean square error (RMSE) of 0.22 cmol/mol and excellent linearity. In contrast, benchtop NMR spectroscopy faced challenges, such as overlapping peaks and longer measurement times, resulting in a higher RMSE of 0.71 cmol/mol. Raman spectroscopy proves to be particularly well-suited for
practical applications due to its high accuracy and linearity. Meanwhile, benchtop NMR spectroscopy holds potential for future enhancements through ongoing technological advances, such as higher magnetic field strengths. In summary, the results from our study indicate that Raman spectroscopy is already a serviceable method for precise hydrogen quantification, whereas benchtop NMR spectroscopy can be attributed potential for future applications.