Susceptor-assisted fast microwave sintering of TiN reinforced SiAlON composites in a single mode cavity

In the present study, 17 wt % TiN reinforced α-β SiAlON composites were sintered at low temperature by susceptor-assisted microwave heating. The effect of TiN addition on dielectrical properties of starting powders, as well as the influence of sintering temperature on phase evolution, microstructure development and mechanical properties of α/β-SiAlON-TiN composites were investigated. The obtained results showed that TiN addition increased the microwave absorbing properties which is reflected in the peak sintering temperature. Thus, the α:β ratio decreased and mechanical properties were improved, especially the fracture toughness of the composites. Furthermore, an estimate of energy consumption during microwave assisted sintering at the laboratory scale is presented. As a result, the highest values for relative density (97.1%), Vickers hardness (13.35 ± 0.47 GPa), and fracture toughness (7.52 ± 0.54 MPa m^1/2) were obtained by microwave sintering for 30 min at 1300 °C.     

A practical development of engineering simulation-assisted educational AR environments

Engineering simulations have opened several gates for today’s chemical engineers. They are powerful tools to provide technical content as physics-based numerical solvers. Augmented reality (AR) and virtual reality (VR), on the other hand, are already underway to digitize environments in many fields. The combination of AR/VR environments and simulations in engineering education has been attracting widespread interest. Literature has demonstrated a massive amount of digital educational environments in several contexts as being complementary to conventional educational methods. Nevertheless, hosting technical content produced by engineering simulations with educational AR/VR is still challenging and requires expertise from multiple disciplines throughout the technical development. Present work provides a facile and agile methodology for low-cost hardware but content-wise rich AR software development. Inspired by the Covid-19 pandemic, a case study is developed to teach chemical-engineering concepts using a liquid-soap synthesis process. Accordingly, we assess and conclude the digital development process to guide inexperienced developers for the digitalization of teaching content. The present contribution serves as an example of the power of integrating AR/VR with traditional engineering simulations for educational purposes. The digital tool developed in this work is shared in the online version.     

致密砂岩油藏能量补充方式评价及优化

针对致密砂岩油藏大规模体积压裂开发后能量补充困难的问题,利用自主设计制作的大型人造三维岩心物理模型和物理模拟实验舱,开展致密砂岩油藏能量补充方式优化研究。实验结果表明:致密砂岩油藏压裂开发过程中,地层能量损耗严重,采取注水或注气的方式可有效进行能量补充;地层中裂缝规模越大,越有利于原油渗流,后续补充能量的传播范围越广,有助于进一步提高原油采收率;从提高驱油效率和扩大波及系数方面优选吞吐渗吸介质,CO₂均优于活性水,CO₂吞吐开发在矿场试验中取得了显著的增油效果,因此,CO₂吞吐作为一种有效的能量补充方式在致密油开发中展现了良好的应用前景。该文分析了致密砂岩储层水平井压裂开发的渗流规律,优选出致密砂岩储层大规模压裂开发后最佳渗吸介质,可为致密砂岩油藏开发设计提供重要的理论依据。     

Self-optimized and renewable Ni–Co alloy@Co–Co2C catalyst for higher alcohols synthesis from syngas

Higher alcohols synthesis (HAS) from syngas (CO/H₂) has attracted widespread attention, while the low selectivity and poor stability of the catalysts mainly stumbled its industrial application. In the work, Ni–Co alloy nanoparticles (NPs) derived from Co_1-xNi_xAl₂O₄ loaded on the SiO₂ with large specific surface area were prepared; and during reaction, the highly dispersed Ni–Co alloys were self-optimized to Ni–Co alloy@Co–Co₂C. Importantly, Ni–Co alloy@Co–Co₂C can be regenerated through oxidation - reduction - self-optimization process. Characteristic results indicated that the structural liberalization during the reaction process inhibited the loss of Ni, regulated and balanced the dual active sites of the catalyst and the Ni–Co alloys were regenerated after the re-oxidation and re-reduction process. The optimized catalyst exhibited excellent catalytic performance, including a high total selectivity to alcohols of 39.3% and an excellent catalytic stability at 250 °C, 3.5 MPa (H₂/CO = 2) and a space velocity of 6000 mL (g_cat h)^?1. In addition, the Ni–Co alloy@Co–Co₂C catalyst after stability test could recover its original catalytic performance after re-oxidation and re-reduction. The renewable characteristics and superior catalytic performance of Ni–Co alloy@Co–Co₂C made the catalyst to be one of the potential industrial catalysts for HAS.     

Effect of utilization of hydrogen-rich reformed biogas on the performance and emission characteristics of common rail diesel engine

The utilization of renewable gaseous fuels in the diesel engine has gained significant interest in recent years due to its clean-burning nature and higher availability. In this study, hydrogen-rich reformed biogas was used as a gaseous fuel in a common rail diesel engine with diesel as pilot fuel. The hydrogen-rich reformed gas was synthesized through dry-oxidative reforming. The experimentations were performed in the load range from 6 to 24 N m with two different flow rates of gaseous fuel (0.5 and 1.5 kg/h) at a constant speed of 1800 RPM. The effects on engine performance parameters (brake thermal efficiency, brake specific energy consumption, and brake specific diesel consumption), combustion parameters (rate of pressure rise and maximum heat release rate) and emission parameters (Unburnt hydrocarbons, nitrogen oxides, carbon monoxide, and carbon dioxide) were assessed. The induction of gaseous fuel led to an increase in brake thermal efficiency by 10.5%, reduction in brake specific energy consumption by 13.6%, and a reduction of 26.4% in brake specific diesel consumption with a flow rate of 0.5 kg/h when compared to diesel-only mode at 24 N m load. The HC, NO_X and CO₂ emissions were reduced by 18.2%, 7.4% and 1.4% with a flow rate of 0.5 kg/h when compared to diesel-only mode at 24 N m load due to lower availability of carbon content in the combustible mixture. The utilization of renewable fuel like hydrogen-rich reformed biogas has great potential for overcoming the issue related to both biogas and hydrogen in diesel engines. Moreover, the higher diesel substitution also demonstrates the potential for cost-saving and fossil fuel conservation.     

A survey on tracking control of unmanned underwater vehicles: Experiments-based approach

This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV, several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。     

Approbation of an innovative method of pretreatment of dark fermentation feedstocks

According to the International Energy Agency, only a small part of the full potential of biomass energy is currently used in the world. The annual amount of agricultural waste in the Russian Federation is estimated at about 152 million tons, and the energy potential of animal waste is 201 PJ/year. Anaerobic digestion is an efficient method of converting organic waste into renewable energy sources. Previously, the positive effect of pretreatment of various organic feedstocks in vortex layer apparatus (VLA) on the characteristics of anaerobic digestion and energy efficiency was shown. Currently, there is a significant interest in the world in obtaining biohydrogen from organic waste using the dark fermentation (DF) process. During pretreatment in the VLA, the iron working bodies are abraded and iron particles are introduced into the feedstock of the DF reactor. This may have a positive effect on the production rate and yield of hydrogen, which has not been previously studied. This work is aimed at evaluating the possibility of using the VLA as a method for pretreatment of a dark fermentation feedstock for the intensification of biohydrogen production. To achieve this goal, an experimental setup was constructed. It consisted of a 45 L DF reactor, a VLA and a process control system to collect data on the DF process parameters every 5 min. At a hydraulic retention time in the DF reactor of 24 h and in the VLA of 30 s, the hydrogen content in the biogas increased from 51.1% to 52.2%. At the same time, the pH increased from 3.85 to 4.8–4.9, and the hydrogen production rate increased by 16% to 1.941 L/(L day). The hydrogen yield was 80.9 ml/g VS. Thus, pretreatment of the feedstock in VLA can be an effective way to intensify the DF process; however, further study of the VLA operating modes is required in order to optimize the concentrations of iron particles introduced into the feedstock for the most efficient continuous production of dark fermentative biohydrogen.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。