Enhancement effect of zero-valent iron nanoparticle and iron oxide nanoparticles on dark fermentative hydrogen production from molasses-based distillery wastewater

This study investigates the effect of two different iron compounds (zero-valent iron nanoparticle: nZVI and iron oxide nanoparticles: nIO) and pH on fermentative biohydrogen production from molasses-based distillery wastewater. The nZVI and nIO of optimum particle sizes of 50 nm and 55 nm respectively were synthesized and applied for fermentative hydrogen (H₂) production. The addition of nIO & nZVI at (0.7 g/L, pH: 6) resulted in the highest H₂ yield, H₂ production rate, H₂ content and COD reduction. Moreover, the kinetic parameters of H₂ production potential (P) and H₂ production rate (R_m) increased to 387 mL, and 22.2 mL/h, respectively for nZVI, these values were 363 mL and 21.8 mL/h for nIO. The results obtained indicated the positive effect of nZVI and nIO addition on enhanced fermentative H₂ production. The addition of nZVI & nIO resulted in 71% and 69.4% enhancement in biohydrogen production respectively.     

Composition-tunable PtCu porous nanowires as highly active and durable catalyst for oxygen reduction reaction

To accelerate the commercialization of fuel cells, many efforts have been made to develope highly active and durable Pt-based catalyst for oxygen reduction reaction (ORR). Herein, PtCu porous nanowires (PNWs) with controllable composition are synthesized through an ultrasound-assisted galvanic replacement reaction. The porous structure, surface strain, and electronic property of PtCu PNWs are optimized by tuning composition, which can improve activity for ORR. Electrochemical tests reveal that the mass activity of Pt_0.5Cu_0.5 PNWs (Pt/Cu atomic ratio of 1:1) reaches 0.80 A mg_Pt^?1, which is about 5 times higher than that of the commercial Pt/C catalyst. Notably, the improved activity of the porous nanowire catalyst is also confirmed in the single-cell test. In addition, the large contact area with the carrier and internal interconnection structure of Pt_0.5Cu_0.5 PNWs enables them to exhibit much better durability than the commercial Pt/C catalyst and Pt_0.5Cu_0.5 nanotubes in accelerated durability test.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

In situ synthesis of Al2O3–SiC powders via molten-salt-assisted aluminum/carbothermal reduction method

Al₂O₃–SiC composite powder (ASCP) was successfully synthesized using a novel molten-salt-assisted aluminum/carbothermal reduction (MS-ACTR) method with silica fume, aluminum powder, and carbon black as raw materials; NaCl–KCl was used as the molten salt medium. The effects of the synthesis temperature and salt-reactant ratio on the phase composition and microstructure were investigated. The results showed that the Al₂O₃–SiC content increased with an increase in molten salt temperature, and the salt–reactant ratio in the range of 1.5:1–2.5:1 had an impact on the fabrication of ASCP. The optimum condition for synthesizing ASCP from NaCl–KCl molten salt consisted of maintaining the temperature at 1573 K for 4 h. The chemical reaction thermodynamics and growth mechanism indicate that the molten salt plays an important role in the formation of SiC whiskers by following the vapor-solid growth mode in the MS-ACTR treatment. This study demonstrates that the addition of molten salt as a reaction medium is a promising approach for synthesizing high-melting-point composite powders at low temperatures.     

Engineering Silver-Enriched Copper Core-Shell Electrocatalysts to Enhance the Production of Ethylene and C2+ Chemicals from Carbon Dioxide at Low Cell Potentials

Copper catalysts are widely studied for the electroreduction of carbon dioxide (CO₂) to value-added hydrocarbon products. Controlling the surface composition of copper nanomaterials may provide the electronic and structural properties necessary for carbon-carbon coupling, thus increasing the Faradaic efficiency (FE) towards ethylene and other multi-carbon (C₂₊) products. Synthesis and catalytic study of silver-coated copper nanoparticles (Cu@Ag NPs) for the reduction of CO₂ are presented. Bimetallic CuAg NPs are typically difficult to produce due to the bulk immiscibility between these two metals. Slow injection of the silver precursor, concentrations of organic capping agents, and gas environment proved critical to control the size and metal distribution of the Cu@Ag NPs. The optimized Cu@Ag electrocatalyst exhibited a very low onset cell potential of −2.25 V for ethylene formation, reaching a FE towards C₂₊ products (FE_C2+) of 43% at −2.50 V, which is 1.0 V lower than a reference Cu catalyst to reach a similar FE_C2+. The high ethylene formation at low potentials is attributed to enhanced C C coupling on the Ag enriched shell of the Cu@Ag electrocatalysts. This study offers a new catalyst design towards increasing the efficiency for the electroreduction of CO₂ to value-added chemicals.     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

循环水替代7℃水在氯乙烯精馏装置中的应用研究

详细计算了PVC生产精馏系统换热设备的热负荷、循环水量及运行成本,认为通过调整系统的运行温度和压力,精馏装置使用循环水替代7℃水具有可行性,这将有利于突破能耗高的技术瓶颈,最大程度地发挥节能降耗、减污增效的潜能。     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

Eco-driving: the role of feedback in reducing emissions from everyday driving behaviours

Eco-driving has been proposed as an approach to significantly reduce greenhouse gas emissions arising from personal automobile use. Eco-driving is the adoption of a measured driving style, minimising unnecessary breaking and aggressive acceleration. Eco-driving can be seen as a low cost and immediate approach to emission reduction as it involves the modification of drivers’ behaviours as opposed to the development and implementation of newer, more efficient technology. Despite the proposed benefits of eco-driving, numerous challenges are faced in order to encourage the adoption of these behaviours and maintain them long term. This narrative review presents the concept of eco-driving, with a focus on the long-term maintenance of these behaviours, including training programmes and feedback devices. It is clear within current literature that, despite the economic and environmental benefits of adopting eco-driving, drivers require feedback on their actions in order to promote long-term, behavioural, change.     

奥钢联板坯铸机驱动力报警问题的分析及改进

浇铸过程中，驱动力报警会导致紧急停浇甚至滞坯事故发生。为防止事故发生，通常采用降速、强冷等措施，但降速造成成本浪费，强冷又产生裂纹等质量缺陷，两者都难以满足生产需要。通过对铸机的各项参数对比分析，找到影响驱动力报警的主要原因为辊缝异常，轻压下模型参数、冷却工艺设置不合理等，通过调整二冷工艺、优化模型参数、加强轻压下区域扇形段辊缝维护，可以达到明显降低驱动力、消除报警的目的，从而保证连铸生产稳定。