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Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.
The distributed measurement of relative humidity is a sought-after capability for a wide range of applications in civil engineering and structural health monitoring. We show that polymethyl methacrylate (PMMA) optical fi-bers can be employed as a sensor medium to conduct distributed humidity measurement by analyzing Rayleigh backscattering traces obtained by OTDR. We make use of the effect that water penetrates the fiber core and directly influences the local fiber attenuation and Rayleigh backscatter coefficient. We conducted distributed backscattering analysis for two different pulse wavelengths: 500 nm and 650 nm. The 650 nm results are susceptible to both, attenuation changes and backscatter changes, whereas backscatter results at 500 nm are not affected by humidity-induced attenuation and only exhibit a change of Rayleigh backscattered power as a function of humidity. The combined measurement and analysis of both parameters at these two wavelengths has the advantage that cross-sensitivities on backscatter change and attenuation, such as strain and tempera-ture changes, could be separated from the humidity response of the fiber. We present laboratory results for a humidity range between 30% and 90% for both pulse wavelengths: including step responses, humidity cycles and hysteresis analysis. In addition to the attenuation and backscatter coefficient dependence, we also analyze optical runtime changes as a function of humidity. POFs have the advantage that they can be directly embed-ded into materials such as concrete or soil to measure water content or localize water ingress. Standard step-index PMMA POFs can be used as a distributed relative humidity sensor up to 200 m distance.
We report, to our knowledge, for the first time on humidity-induced Brillouin frequency shifts in perfluorinated graded index polymer optical fibers. A linear relation between Brillouin frequency shift and humidity was observed. Furthermore, the humidity coefficient of the Brillouin frequency shift is demonstrated to be a function of temperature (-107 to -64 kHz/%r.h. or -426 to -49 kHz m3/g in the range of 20 to 60 °C). An analytical description proves temperature and humidity as two mutually independent effects on the Brillouin frequency shift.
One of the unique advantages of polymer optical fibres (POF) is that they can be used to measure very high strain values up to 100 % and beyond exceeding the strain limits of silica fibre-based sensor principles. In this paper the distributed strain measurement capabilities of POF based on backscatter change evaluation are summarized and distributed backscatter measurement technologies are intro-duced. Application examples in the structural health monitoring (SHM) field are presented: a promising approach is the integration into technical textiles for high-strain measurement in earthwork structures and crack detection in buildings. The potential of POF for future applications in SHM such as distributed relative humidity sensing is discussed.
One of the unique advantages of polymer optical fibres (POF) is that they can be used to measure very high strain values up to 100 % and beyond exceeding the strain limits of silica fibre-based sensor principles. In this paper the distributed strain measurement capabilities of POF based on backscatter change evaluation are summarized and distributed backscatter measurement technologies are intro-duced. Application examples in the structural health monitoring (SHM) field are presented: a promising approach is the integration into technical textiles for high-strain measurement in earthwork structures and crack detection in buildings. The potential of POF for future applications in SHM such as distributed relative humidity sensing is discussed.