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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 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.
Phase-sensitive optical time domain reflectometry (φOTDR) is an excellent distributed fiber sensing technique and has been applied in various areas. Its noise is however never been comprehensively studied to the best of our knowledge. The different detection noise sources in such a sensing system are thoroughly investigated. The impacts of thermal noise, shot noise and the beat between signal and the amplified spontaneous emission from a pre-amplifier have been theoretically and experimentally demonstrated. Due to the random nature of the φOTDR signal, the detection noise demonstrates distinct features at different fiber positions in a single measurement. The theoretical analysis and the experimental result explicitly affirm most of the fiber sections, and the difference at some positions may be explained by ambient noise.
In this work, we present our results achieved in several research activities for development of distributed fiber optic radiation sensors using glass and polymer optical fibers. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accompanying change in the refractive index of the fiber core can be used for distributed radiation monitoring.
In this work, we present our results achieved in several research activities for development of fiber optic dosimeters. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accom panying change in the refractive index of the fiber core can be used for distributed radiation monitoring in the kGy and MGy range, respectively. Depending on the fiber type and material the RIA shows varying response to dose rates, environmental temperatures and the wavelength of the laser source used.
Division 8.6's competencies and work focus areas are shortly outlined and investigations related to pipeline and submarine power cable monitoring using different distributed fiber optic sensing techniques are presented with some results. Furthermore, BAM 8.6 expertise in the field of sensor application, embedding and integration is shown.
We present a preliminary investigation on distributed humidity monitoring during the drying process of concrete based on an embedded polymer optical fiber (POF). The water dissipated into the POF changes several properties of the fiber such as refractive index, scattering coefficient and attenuation factor, which eventually alters the Rayleigh backscattered light. The optical time Domain reflectometer (OTDR) technique is performed to acquire the backscattered signal at the wavelengths 650 nm and 500 nm, respectively. Experimental results show that the received signal increases at 650 nm while the fiber attenuation factor clearly increases at 500 nm, as the concrete dries out. In the hygroscopic range, the information retrieved from the signal change at 650 nm agrees well with the measurement result of the electrical humidity sensors also embedded in the concrete sample.
Fiber optic Distributed Acoustic Sensing (DAS) is an emerging method for many different monitoring purposes, enabling a spatially and temporally resolved collection of acoustic and vibration information over many kilometers. DAS, thus being a "dynamic" sensing technique, allows for online condition monitoring and the detection and localization of threats or hazards in real time via characteristic acoustic/vibration states and their changes or via occurring anomalous signals, respectively.
At BAM, we have employed this technology for a number of different applications of monitoring of large infrastructures, e.g., bridges, pipelines, submarine power cables or railway tracks. Currently, we are investigating the use of DAS for further innovative uses, which aim at facilitating the energy transition, enabling "smart" infrastructure and providing the basis for comprehensive hazard monitoring and warning systems, respectively. Our research fields include fiber optic borehole monitoring in the context of hydrogen storage caverns, traffic and road monitoring, using DAS for earthquake monitoring in urban areas for disaster management and long-term monitoring of large-scale subsidence caused by mining activities. Another new topic is structural health monitoring in concrete structures in the framework of the FSP Security.
In this talk, we will first briefly present the basics and capabilities of DAS. Then, we will portray our previous and current works related to this technology and show some attractive results. Finally, we will discuss our upcoming projects on exciting new applications of DAS.