Chromium-Ruthenium Oxides Supported on Gamma-Alumina as an Alternative Catalyst for Partial Combustion of Methane

Catalyst screening of γ-Al2O3-supported, single-metal and bimetallic catalysts revealed several bimetallic catalysts with activities for partial combustion of methane greater than a benchmark Pt/γ-Al2O3 catalyst. A cost analysis of those catalysts identified that the (2 wt%Cr + 3 wt% Ru)/γ-Al2O3 catalyst, denoted as 2Cr3Ru/Al2O3, was about 17.6 times cheaper than the benchmark catalyst and achieved a methane conversion of 10.50% or 1.6 times higher than the benchmark catalyst based on identical catalyst weights. In addition, various catalyst characterization techniques were performed to determine the physicochemical properties of the catalysts, revealing that the particle size of RuO2 became smaller and the binding energy of Ru 3d also shifted toward a lower energy. Moreover, the operating conditions (reactor temperature and O2/CH4 ratio), stability, and reusability of the 2Cr3Ru/Al2O3 catalyst were investigated. The stability test of the catalyst over 24 h was very good, without any signs of coke deposition. The reusability of the catalyst for five cycles (6 h for each cycle) was noticeably excellent

Deep learning-based semantic segmentation for in-process monitoring in laser welding applications

The broad uses of laser welding in various industrial applications such as shipbuilding, automotive production and battery manufacturing, result from its capabilities of high productivity, flexibility and effectiveness 1. However, the complex nature of laser-material interaction requires additional measures in order to reach the high-quality standards of the goods produced. Therefore, continuous process monitoring in laser welding is crucial to achieve reliable mass production and high-quality products at once. Camera-based process monitoring offers great advantages compared to one-dimensional observation techniques. The spatial resolution enables the monitoring of several process characteristics simultaneously, which leads to a more detailed description of the current process state 2. In the last few years, we proposed a coaxially integrated camera system with external illumination. Process images taken by this system typically show the keyhole area, the weld pool, but also areas of solidified weld and areas of the blank sheet3. To automate image evaluation with respect to the recognition of aforementioned areas, we propose a convolutional neural network architecture to perform pixel-wise image classification4. In this paper, we investigate the influence of multiple hyper-parameters required for the network architecture in use, but also the amount of data that is necessary for high segmentation accuracies. In a second step, the outcome of the network is used to detect process deviations in laser welding image data using supervised machine learning. With the help of the Random Forest algorithm, assessment of the extracted process characteristics with respect to prediction accuracy takes place. Based on the information of the segmented image data, further investigations are carried out into the possibility of predicting individual process parameters such as laser power, welding speed and focus size simultaneously