In polymerization processes, unpreventable fouling eventually occurs and results in potential product contamination and increased energy consumption. Formation of this fouling must be detected reliably in real-time to perform obligatory cleaning procedures in time. Unfortunately, measurement technique for this purpose is commercially not available yet. Thus, in the present work a measurement setup is developed that can reliably determine formation of fouling using ultra-sound echo measurements.

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-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 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.

A planar lab-on-a-chip mass spectrometer was developed to get a low-priced micro mass spectrometer for gas analysis from =1 amu. This micro-electrical-mechanical system (MEMS) has a size of 6.5 mm x 11 mm, is fabricated with Lithografie, Galvanoformung-technology (LiGa), and consists of nickel and sapphire. A Time-of-Flight (ToF) mass separator is used for ion separation. Besides the separation, the MEMS includes a plasma based electron impact ionization chamber, an energy filter and a Faraday cup. The results show a functioning system and first mass spectra of a gas mixture consisting of nitrogen (N 2 ), oxygen (O 2 ) and carbon dioxide (CO 2 ). The first measurements show a mass resolution of up to 16 and a limit of detection down to 500 ppm.

The electrical impedance measurement of a suspension is a valid method to monitor crystallization processes. Since it allows measurement of conductivity and permittivity it enables the characterization of non-conductive suspensions. The results obtained show that the concentration of an organic compound of interest can be determined by evaluating its electrical and thermal properties. As the analytical analysis of independent process parameters is a challenging task, a machine learning approach is investigated to extract essential parameter dependency for automated process control purposes.

Foaming impairs the efficiency in several processes of the chemical industry. An early foam detection is advantageous to solve this problem. In this work we investigate the detection of foam by means of radar distance measurements utilizing a low noise 80 GHz FMCW radar with 25 GHz bandwidth. Two foaming solutions with several majority sizes of the air inclusions are used. First, a water butanol solution creates very fine foam. The foam surface is the only measurable reflection. However, it can be distingushed from a refelction at a liquid surface by means of the amplitude. Secondly, a water tenside solution creates fine to coarse foam. The foam surface as well as the liquid surface underneath exhibit a measurable reflection. The finer the foam is, the stronger the reflection at the foam surface and the weaker the reflection at the liquid surface is. Thus, the detection of foam by means of millimeter wave radar measurements is possible.

This paper reports a design of an exchangeable miniaturized mass spectrometry chip using spring-loaded pins and O-rings for electrical and fluidic connections. This planar microelectromechanical system (MEMS)-chip works with 300 µm high silicon structures between two borosilicate glasses and has a size of 13 mm × 7 mm. Because of its small size a small vacuum pump is sufficient, and it is suitable for mobile measurements and portable applications. Because of exchangeability of MEMS-chips with fluidic and electrical high voltage connections a direct comparison between chips is possible.