Economic Indicators

Currency exchanges rates – updated monthly. Economic indicators of industrial production. Countries included: France, Germany, Italy, the United Kingdom, the United States, Canada, Japan (Total G-7), Eurozone and Total EU-28 (Total OECD). Updated monthly. Economic indicators of car registration in the United States, Canada, France, Germany, Italy, Poland, Spain, Turkey, the United Kingdom, Japan, Republic of Korea and Other OECD (Total OECD-30). Updated monthly. Monthly averages of crude steel production in thousand metric tons for the United States, Canada, Brazil, Mexico, Austria, Belgium, Czech Republic, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, Turkey, the United Kingdom, Russia, Ukraine, South Africa, Iran, China, India, Japan, Republic of Korea, Taiwan and Australia.     

Discovery of path-attribute dependency in manufacturing environments: A process mining approach

The more knowledge industrial practitioners detain of their production processes, the more they are capable of performing process improvements. Nonetheless, there may exist process characteristics and dependencies that are not easily extractable from business models, such as routing dependent attributes. This paper introduces an algorithm-driven framework to establish whether process path decisions influence the attributes in non-direct sequences, e.g., deploying machine A instead of machine B affects the % of rejected parts on the process, 4 stages down the line. This problem is shown to bears similarities with sequential pattern mining problems. The basis of the solution framework relies on process mining and data mining techniques. The approach proposed is applied on a real industrial log, unveiling deficiencies in the system and providing further improvement recommendations.     

A comparative study on the performance of M (Rh,Ru, Ni)-promoted metallurgical waste driven catalysts for H2 production by glycerol steam reforming

A comprehensive study was conducted on the performance of M-promoted (M = 1%Ru, 1%Rh, 5%Ni) upgraded slag oxide metallurgical waste catalysts (M-UGSO) for hydrogen production by glycerol steam reforming (GSR). The results confirmed that the tendency of the incorporated metal to interact with Mg/Fe containing species within UGSO plays a key role in the surface availability of the corresponding metal, structural changes after reduction, and catalyst stability. Aside its best stability, 5% Ni-UGSO showed a performance (glycerol conversion to gaseous products of 100% and H₂ yield of 74%) comparable with 1% Rh-UGSO (100% and 78%, respectively) or even surpassing that of 1% Ru-UGSO (94% and 71%, respectively), as noble metal-based catalysts. Synergistic cooperation was achieved by incorporated metals (M) and Fe/Mg containing species within UGSO, resulting in enhanced glycerol and water activation. The weakest results of Ru-UGSO could be justified by lack of propensity for MgO–RuO₂ interaction on UGSO surface.     

Utilization and impacts of hydrogen in the ironmaking processes: A review from lab-scale basics to industrial practices

Hydrogen (H₂) energy is a promising candidate to replace carbon monoxide (CO) as a reductant for iron oxide reduction in revolutionary ironmaking industrial processes, and numerous studies have been conducted to intensively study the utilization and impact of H₂ in ironmaking processes. Therefore, this review first collects and compares the H₂-assisted reduction mechanism and kinetics. The impacts of H₂ on the reduction accompanying behaviors, such as the disintegration, swelling, sticking, softening, and melting of iron ores, are then summarized. Third, the performance of H₂ predicted by either mass and heat balance models or numerical simulation models in various ironmaking processes is highlighted. Finally, the different applications of hydrogen-rich materials in blast furnace and non-blast furnace ironmaking processes are further compared to illuminate H₂ utilization before our outlook on the use of H₂ in the ironmaking industry.     

Virtual engineering of cyber-physical automation systems: The case of control logic

Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner.     

Comprehensive Analysis of Secure Data Aggregation Scheme for Industrial Wireless Sensor Network

As an Industrial Wireless Sensor Network (IWSN) is usually deployed in a harsh or unattended environment, the privacy security of data aggregation is facing more and more challenges. Currently, the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating, alternately, the aim at enhancing the security of data. The performances of the secure data aggregation protocols are the trade-off of several metrics, which involves the transmission/fusion, the energy efficiency and the security in Wireless Sensor Network (WSN). Unfortunately, there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN. In consideration of IWSN, we firstly review the security requirements and techniques in WSN data aggregation in this paper. Then, we give a holistic overview of the classical secure data aggregation protocols, which are divided into three categories: hop-by-hop encrypted data aggregation, end-to-end encrypted data aggregation and unencrypted secure data aggregation. Along this way, combining with the characteristics of industrial applications, we analyze the pros and cons of the existing security schemes in each category qualitatively, and realize that the security and the energy efficiency are suitable for IWSN. Finally, we make the conclusion about the techniques and approach in these categories, and highlight the future research directions of privacy preserving data aggregation in IWSN.     

CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds.     

Accelerate the alkaline hydrogen evolution reaction of the heterostructural Ni2P@Ni(OH)2/NF by dispersing a trifle of Ru on the surface

Electrocatalytic water splitting for hydrogen production plays a vital role in the development of new energy field, but there is still a lack of low-content precious metal or cost-effective non-noble metal catalysts for the hydrogen evolution reaction (HER). Therefore, how to develop the catalysts with a smaller amount of precious metal to achieve higher performance is still a major challenge. Herein, we have fabricated Ru–Ni₂P@Ni(OH)₂/NF-2 heterostructure by phosphating Ni(OH)₂/NF and then anchoring Ru on the surface through wet chemical strategy. Benefiting from its optimal ΔG_H1 and synergistic effect, this Ru–Ni₂P@Ni(OH)₂/NF-2 catalyst shows superior electrocatalytic HER kinetics in alkaline electrolyte. A small overpotential of 31 mV is needed for this electrocatalyst to obtain the current densities of 10 mA cm^?2 with remarkable durability over 24 h. This work provides a new strategy for the preparation of effective HER electrocatalyst with a low precious metal content.