Dynamic energy and mass balance model for an industrial alkaline water electrolyzer plant process

This paper proposes a parameter adjustable dynamic mass and energy balance simulation model for an industrial alkaline water electrolyzer plant that enables cost and energy efficiency optimization by means of system dimensioning and control. Thus, the simulation model is based on mathematical models and white box coding, and it uses a practicable number of fixed parameters. Zero-dimensional energy and mass balances of each unit operation of a 3 MW, and 16 bar plant process were solved in MATLAB functions connected via a Simulink environment. Verification of the model was accomplished using an analogous industrial plant of the same power and pressure range having the same operational systems design. The electrochemical, mass flow and thermal behavior of the simulation and the industrial plant were compared to ascertain the accuracy of the model and to enable modification and detailed representation of real case scenarios so that the model is suitable for use in future plant optimization studies. The thermal model dynamically predicted the real case with 98.7 % accuracy. Shunt currents were the main contributor to relative low Faraday efficiency of 86 % at nominal load and steady-state operation and heat loss to ambient from stack was only 2.6 % of the total power loss.     

A review of hydrogen production processes by photocatalytic water splitting – From atomistic catalysis design to optimal reactor engineering

‘Renewable energy is an essential part of our strategy of decarbonization, decentralization, as well as digitalization of energy.’ – Isabelle Kocher.Current climate, health and economic condition of our globe demands the use of renewable energy and the development of novel materials for the efficient generation, storage and transportation of renewable energy. Hydrogen has been recognised as one of the most prominent carriers and green energy source with challenging storage, enabling decarbonization. Photocatalytic H₂ (green hydrogen) production processes are targeting the intensification of separated solar energy harvesting, storage and electrolysis, conventionally yielding O₂/H₂. While catalysis is being investigated extensively, little is done on bridging the gap, related to reactor unit design, optimisation and scaling, be it that of material or of operation. Herein, metals, oxides, perovskites, nitrides, carbides, sulphides, phosphides, 2D structures and heterojunctions are compared in terms of parameters, allowing for efficiency, thermodynamics or kinetics structure–activity relationships, such as the solar-to-hydrogen (STH). Moreover, prominent pilot systems are presented summarily.     

Application of Cr3C2/C composite powders synthesized via molten-salt method in low-carbon MgO–C refractories

High-performance and low-carbon MgO–C refractories are important refractories for smelting ultra-low carbon steel and clean steel. Based on this, Cr₃C₂/C composite powders were synthesized by the molten-salt method, and used as an additive to prepare low-carbon MgO–C refractories under nitrogen atmosphere. The phase, morphology and oxidation kinetics of Cr₃C₂/C composite powders were studied. In addition, the effect of Cr₃C₂/C composite powders on the morphology, mechanical properties, thermal shock resistance, and corrosion resistance of MgO–C refractories was investigated. The results indicated that the Cr₃C₂/C composite powders exhibited superior oxidation resistance than flake graphite. Moreover, the Cr₃C₂/C composite powders were introduced into the MgO–C refractories. Compared with the sample without Cr₃C₂/C composite powders, the introduction of 1 wt% Cr₃C₂/C composite powders significantly improved the thermomechanical properties and corrosion resistance of the material, its CMOR, CCS before and CCS after thermal shock were 9.06 MPa, 50.40 MPa and 32.60 MPa, respectively, and the corrosion index was significantly reduced from 44.6% to 26.5%.     

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.     

工业机器人在自动化生产线分拣站的应用

根据自动化生产线分拣站的工艺流程和自动化控制的要求,提出基于工业机器人、PLC以及传送带的货物码垛自动化解决方案。构建以三菱FX3U型PLC、传送带和工业机器人等组成的全自动分拣码垛系统。分析了PLC的动作过程,提供了PLC的程序流程图;分析了工业机器人码垛的工作过程,提供了工业机器人码垛的程序流程图。工业机器人引入到自动化生产系统后,能够进一步提高工厂自动化水平、提高生产效率,减小劳动强度,降低人工成本。     

Optimizing Ni–Ce/HZSM-5 catalysts for ex-situ conversion of pine wood pyrolytic vapours into light aromatics and phenolic compounds

The main objective of the present work is to investigate the influence of nickel to cerium ratio on hydrogen exchanged Zeolite Socony Mobil-5 (HZSM-5) towards the catalytic upgrading of pine derived oxygenated pyrolysis vapours into aromatic hydrocarbon and phenol in pyrolysis oil via ex-situ fixed bed reactor. The presence of CeO₂ could change electron density of Ni, promote the reduction of Ni species, accelerate the transfer of carbon species, and suppress the production of carbon deposits (17.53%–25.11%) compared with the parent HZSM-5 catalyst (28.95%); it also improved the hydrodeoxygenation ability of all xNiyCe/HZSM-5(nickel and cerium bimetal modified HZSM-5) catalysts, resulting increases in noncondensable gas content (from 31.46% to 52.99%–65.53%). Ni to Ce ratio of 1:1 and 1:2 produced highest aromatic hydrocarbon (32.14%) and phenols (55.51%) relative peak areas. The acid center of HZSM-5 and the metal acid center of the Ni:Ce = 1:1 catalyst obviously fine-tuned the formation of coke; and promoted hydrocarbon production. Moreover, high Ni content promoted alkylation of benzene at C6–C9 and increased C10⁺ PAHs relative peak area; high Ce content promoted the formation of olefin and Increasing the cleavage of C–O bonds and promoted hydrogenation or dehydrogenation, reduced polycyclic aromatic hydrocarbons and coke yield, and increased phenols and alkylphenols selectivity.     

Identifying and Verifying Vulnerabilities through PLC Network Protocol and Memory Structure Analysis

Cyberattacks on the Industrial Control System (ICS) have recently been increasing, made more intelligent by advancing technologies. As such, cybersecurity for such systems is attracting attention. As a core element of control devices, the Programmable Logic Controller (PLC) in an ICS carries out on-site control over the ICS. A cyberattack on the PLC will cause damages on the overall ICS, with Stuxnet and Duqu as the most representative cases. Thus, cybersecurity for PLCs is considered essential, and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks. In this study, a vulnerability analysis was conducted on the XGB PLC. Security vulnerabilities were identified by analyzing the network protocols and memory structure of PLCs and were utilized to launch replay attack, memory modulation attack, and FTP/Web service account theft for the verification of the results. Based on the results, the attacks were proven to be able to cause the PLC to malfunction and disable it, and the identified vulnerabilities were defined.     

含铜废水预处理设施脱氮升级改造工程

为了减轻太湖保护区范围内企业处理废水的负担,苏州白荡污水处理厂利用其含铜废水预处理设施的富余处理能力处理某公司产生的含氮废水。针对含氮废水高NO3^--N和低碳氮比等特点,在充分利用原处理设施的基础上,采用"A/O+固定床生物膜工艺+高效脱氮填料+乙酸钠碳源+内回流"组合工艺对含铜废水预处理设施进行脱氮升级改造。改造后实际运行结果表明,设施运行稳定,出水水质达到出水水质达到设计出水要求,TN和NH4^+-N的去除率分别达到85.4%和82.3%,Cu的去除率提高了17.6%。     

Nonlinear model predictive control of an industrial polymerization process

Nonlinear model predictive control (NMPC) is used to maintain and control polymer quality at specified production rates because the polymer quality measures have strong interacting nonlinearities with different temperatures and feed rates. Polymer quality measures that are available from the laboratory infrequently are controlled in closed-loop using a NMPC to set the temperature profile of the reactors. NMPC results in better control of polymer quality measures at different production rates as compared to using the nonlinear process model with reaction kinetics to implement offline targets for reactor temperatures.     

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.