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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.
As presented at the NAMUR general meeting 2015, the technology roadmap "Process Sensors 4.0" identifies the necessary requirements as well as the communication abilities of such process sensors. We report on the progress of discussions in trialogue between users, software and device manufacturers as well as the research.
An important key is the definition of an appropriate and uniform topology for such smart sensors, which will be driven forward in a new NAMUR AK 3.7 "smart sensors" in mutual exchange with device and software manufacturers and research institutions.
Smart sensors and smart reference materials – an approach to the industrial internet of things
(2016)
The BAM targets within Bonares I4S are adaption of two online sensors being optimised for mobile applications: A LIBS spectrometer (Laser Induced Breakdown Spectroscopy) as well an a XRF spectrometer (X-Ray Fluorescence
Spectroscopy). Beyond , the certification of soil reference materials is scope of I4S at BAM. Therefore, a managable relational database structure is needed, based on a modular Architecture, which is dedicated to spectroscopy. The requirements to such a database are discussed.
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 increasing pollution of terrestrial and aquatic ecosystems with plastic debris, which leads to the accumulation of microscopic plastic particles of still unknown fate, is an upcoming problem of our time. In order to monitor the degree of contamination and to understand the underlying processes of degradation and internalization of plastic debris, analytical methods are urgently needed, which help to identify and quantify microplastics. Currently, expensive collected and purified materials enriched on filters are investigated by (micro) infrared spectroscopy (FTIR). Few studies using micro-Raman spectroscopy have been published as well. In contrast to FTIR, Raman spectroscopy can handle wet samples, but it suffers from interference of fluorescent materials. Both micro-FTIR- and micro-Raman, always include time consuming scanning and mapping procedures followed by the manual inspection and measurement of selected particles.
The Cultivation of “Saccharomyces cerevisiae” for enzyme production was monitored using Near-infrared spectroscopy. An inline NIR optrode was therefore immersed in a 15 L vessel. The calibration was done using a Partial Least Squares (PLS) model with reference measurements of glucose, ammonium, phosphate, ethanol, and optical density. A nonlinear biological process model based on an extended Kalman Filter (EKF) was used to describe the fermentation behavior. It was found that EKF corrects inaccurate PLS predictions.
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.
This key comparison aims to assess the core capabilities of the participants in gas analysis. Such competences include, among others, the capabilities to prepare Primary Standard gas Mixtures (PSMs), perform the necessary purity analysis on the materials used in the gas mixture preparation, the verification of the composition of newly prepared PSMs against existing ones, and the capability of calibrating the composition of a gas mixture. According to the Strategy for Key Comparisons of the Gas Analysis Working Group, this key comparison is classified as an RMO track A key comparison.
The artefacts were binary mixtures of propane in nitrogen at a nominal amount-of-substance fraction level of 1000 μmol/mol. The values and uncertainties from the gravimetric gas mixture preparation were used as key comparison reference values (KCRVs). Each transfer standard had its own KCRV. The results are generally good. All results are within ± 1 % of the KCRV.
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.
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.