Polymerization of cyclic monomers. VII. Synthesis and radical polymerization of 1,3‐bis[(1‐alkoxycarbonyl‐2‐vinylcyclopropane‐1‐yl)carboxy]benzenes

1,3‐Bis[(1‐alkoxycarbonyl‐2‐vinylcyclopropane‐1‐yl)carboxy]benzenes 1 [RO: CH₃O (a), C₂H₅O (b)] were synthesized by the esterification of the corresponding 1‐alkoxycarbonyl‐2‐vinylcyclopropane‐1‐carboxylic acids with resorcinol. The structure of the new vinylcyclopropanes was confirmed by elemental analysis and infrared (IR), ¹H nuclear magnetic resonance (¹H‐NMR), and ¹³C nuclear magnetic resonance (¹³C‐NMR) spectroscopy. The radical polymerization of difunctional 2‐vinyl‐cyclopropanes in bulk with 2,2′‐azoisobutyronitrile (AIBN) results in hard, transparent, crosslinked polymers. During the bulk polymerization of the crystalline bis[(1‐methoxycarbonyl‐2‐vinylcyclopropane‐1‐yl)carboxy]benzene 1a, an expansion in volume of about 1% took place. The radical solution polymerization of 1a resulted in a soluble polymer with pendant 2‐vinylcyclopropane groups. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 72: 1775–1782, 1999     

Rather the Rule than the Exception: Non-Convex Pareto Sets and their Navigation in Distillation Processes

Novel algorithms for adaptive approximation and interactive navigation of Pareto sets are applied to various homogeneous distillation processes with recycle streams within an industrial flowsheet simulator. The adaptive approximation scheme shows that the Pareto set consists of both convex and non-convex regions when the product purities are maximised while minimising the total heat duties. It is illustrated how such Pareto sets can be navigated interactively using a ray tracing technique. Our results suggest that the occurrence of non-convex regions in the Pareto sets is due to recycle streams in the flowsheets and is therefore the rule rather than the exception in multi-objective optimisation of flowsheet simulations in chemical engineering.     

Detecting Crystals in Suspensions: Convolutional Neural Networks vs. Gravity-Based Approach for Size Distribution Detection

The majority of fine chemical and pharmaceutical processes includes some form of crystallization steps. For process optimization and control of further downstream steps, the crystal size distribution of the product is a crucial factor. To identify characteristic particle size classes from a large number of measurements, each individual probe has to be separated from the mother liquor and manually analyzed. In this contribution a deep learning-based method is presented using microscopic images as input for crystal size analysis. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. A high segmentation accuracy of the crystals was achieved with 93.02 %. To evaluate the classification performed by the presented convolutional neural network (CNN), it is tested on two sets of images, containing a previously determined particle fraction. With the classifications of the CNN, a Q₃ distribution is calculated. To validate the developed approach in terms of its accuracy it is compared to two other methods as well.     

AI-Based Supervision for a Stirred Extraction Column Assisted with Population Balance-Based Simulation

Solvent extraction as environmental benign separation technique can be modeled in physical detail by population balance of the droplet size distribution. However, much information on the droplet generation and coalescence is necessary for representative results. In this contribution, we present a comparison of AI-evaluated experimental and simulated data on the behavior of a stirred solvent extraction column with an inner diameter of 32 mm. Lab experiments were performed using the standard test system with n-butyl acetate, acetone, and deionized water. A digital camera is placed in front of the middle section as well as the head of the column. Droplet size evaluation is performed using a retrained neural net (Mask R-CNN). The stirred DN32 extraction column is modeled and simulated using a 1D CFD population balance software. The simulation allows for behavior analysis, trends comparison, and validation of the hydrodynamics and mass transfer performances.     

AI in Process Industries – Current Status and Future Prospects

The chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry – through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies.     

Entwicklung und Erprobung eines Wäschersystems zur CO2‐Abreinigung aus Rauchgasen mithilfe alkalischer Reststoffe

An innovative, technical approach for the reduction of CO₂ emissions is presented that utilizes alkaline wastes to capture CO₂ from flue gases in stable mineral form. Comprehensive pilot‐scale experiments were conducted with the developed flue gas scrubbing system at a power plant site. By optimizing the process parameters gas flux, CO₂ partial pressure, circulation flux and suspension liquid‐to‐solid ratio, a CO₂ binding of 40 – 90 g kg^–1 waste could be reached and up to 25 % of the CO₂ could be captured. The new technique is economically advantageous especially when both alkaline waste and CO₂ are produced on site and when the carbonated products can be used as secondary resources.     

Monomers for Adhesive Polymers, 10‐Synthesis,Radical Photopolymerization and Adhesive Properties of Methacrylates Bearing Phosphonic Acid Groups

Syntheses of eight novel methacrylates bearing phosphonic acid groups were synthesized in three to five steps. The interaction of these monomers with hydroxyapatite was investigated using ¹³C‐NMR spectroscopy. Free radical homopolymerizations were carried out in a mixture ethanol/water (2.5/1, v/v) using 2,2′azo(2‐methylpropionamidine) dihydrochloride as initiator. The copolymerization of these monomers with a mixture HEMA/GDMA (5/3, mol/mol) was investigated by photo‐DSC. Dentin shear bond strength measurements showed that 2‐methacryloyloxy‐3‐(1,1,2,2‐tetrafluoroethoxy)propylphosphonic acid 4b , 2,3‐dimethacryloyloxypropylphosphonic acid 18 and 3‐(methacryloyloxy)‐2,2‐(di[(dihydroxyphosphoryl)methyl])propyl methacrylate 23 are promising candidates for dental adhesives.

     

Remark on the modified kiefeer-weiss problem for exponntial families

During his investigations of the modified kiefer-weiss problem Lorden (1980) considered the (auxiliary) problem of minimizing a weighted sum of the error Probabilites and the expected sample size. He derived a compiete solution for one-parameter exponential famlies and "inner" parameter points. Here we treat the remaining case of external points. It turns out that the optimal stopping rules fail to be finite a.s.. An additional example shows that this may happen also for other types of distributions.