An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

Preparation of iron and nitrogen co-doped carbon material Fe/N-CCM-T for oxygen reduction reaction

In this paper, iron and nitrogen co-doped carbon material with nanotube structure (Fe/N-C_CM-T) was synthesized by pyrolyzing a mixture of Fe salt, chitosan and melamine and displayed high electrocatalytic performance for oxygen reduction reaction (ORR). The structure of the Fe/N-C_CM-T was characterized and their ORR performance in alkaline media was investigated by linear sweep voltammetry, cyclic voltammetry and chronoamperometry. Fe/N-C_CM-T displayed better ORR performance than other carbon materials like Fe/N-C_C-800. The Fe/N-C_CM-800 with a large surface area (302.5 m²/g) especially exhibited the best ORR electrocatalytic performance among the prepared carbon materials, which was also proved by its similar Tafel slope (76 mV decade^?1) to Pt/C catalyst (74 mV decade^?1). Fe/N-C_CM-800 showed similar ORR activity as commercial Pt/C catalyst, but superior tolerance to methanol and stability. Such high ORR performance of the Fe/N-C_CM-T can be attributed to its nanotube structure, high specific surface area (SSA), high graphitic-N and pyridinic-N contents.     

Process parameter dependencies of continuous and pulsed laser modes on surface polishing of additive manufactured aluminium AlSi10Mg parts

Laser powder bed fusion is a well-established 3D printing technique for metal alloys, but exhibits a poor surface quality. Laser polishing provides the possibility of a fast contact-free and fully-automatable surface treatment. This paper deals with the experimental investigation of laser polishing of laser powder bed fusion parts made of aluminium AlSi10Mg. Laser polishing is done with a 4 kW solid state disc laser in combination with a multi-axis system and a one dimensional scanner optic. The laser is operated at continuous and pulsed operation mode. The parameter study reveals a high dependency of the achievable roughness on the laser beam intensity, the track and pulse overlap, the energy density and the number of polishing passes and polishing directions. Pulsed laser polishing mode with up to four passes from different directions revealed the lowest surface roughness of 0.14 μm Ra. With respect to the initial average surface roughness of Ra = 8.03 μm a reduction of the surface roughness of greater than 98 % could be achieved. Polishing with continuous laser radiation at one polishing pass resulted in Ra = 0.23 μm at an area rate of 20 cm²/min. Laser polishing using four passes achieved a further improvement up to Ra = 0.14 μm.     

Invited review: Potential antiobesity effect of fermented dairy products

The growing prevalence of obesity affects millions of people around the world and has gained increased attention over the years because it is associated with the development of other chronic degenerative diseases. Different organizations recommend lifestyle changes to treat obesity; nevertheless, other strategies in addition to lifestyle changes have recently been suggested. One of these strategies is the use of probiotics in fermented dairy products; however, a need exists to review the different studies available related to the potential antiobesity effect of these products. Because probiotic fermented dairy products that support weight management are not available in the market, there is a great opportunity for the development of functional dairy products with new lactic acid bacteria that may present this added health benefit. Thus, the purpose of this overview is to highlight the importance of probiotic fermented dairy products as potential antiobesogenic functional foods and present in vitro and in vivo studies required before this kind of product may be introduced to the market. Overall, most studies attributed the antiobesity effect of fermented dairy foods to the probiotic strains present; however, bioactive peptides released during milk fermentation may also be responsible for this effect.     

Perovskite ceramic oxide as an efficient electrocatalyst for nitrogen fixation

The electrochemical conversion of N₂ to NH₃ is an interesting research topic as it provided an alternative and energy-saving method compared with the traditional way of NH₃ production. Although different materials have been proposed for N₂ reduction, the use of defects in oxides was only reported recently and the relevant working mechanism was not fully revealed. In this study, Sr was used as the dopant for LaFeO₃ to create oxygen vacancies, forming the Sr-doped LFO (La_0.5Sr_0.5FeO_3-δ) perovskite oxide. The La_0.5Sr_0.5FeO_3-δ ceramic oxide used as a catalyst achieves an NH₃ yield of 11.51 μgh^?1 mg^?1 and the desirable faradic efficiency (F.E.) of 0.54% at ?0.6 V vs reversible hydrogen electrode (RHE), which surpassed that of LaFeO₃ nanoparticles. The ¹⁵N isotope labeling method was employed to prove the La_0.5Sr_0.5FeO_3-δ catalyst had the function of converting N₂ into NH₃ under the electrolysis condition. The first principle calculations were used to investigate the mechanism at the atomistic level, revealing that the free energy barriers changed significantly with the introduction of oxygen vacancies that accelerated the overall nitrogen reduction reaction (NRR) procedure.     

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.     

Use of heat-moisture treated maize starch to modify the properties of wheat flour and the quality of noodles

Waxy, normal and high-amylose maize starches were subjected to heat-moisture treatment (HMT) and then added to wheat flour (WF) in different ratios (1%, 5% and 10%). The properties of blends and their cooked noodles were studied to investigate the effects of HMT starches. The incorporation of HMT starch in WF led to an increase in swelling power, peak viscosity and breakdown and to a decrease in setback, thus inhibiting retrogradation, hence enhancing resultant noodle softness. Compared to the same addition ratio of native starch to WF, HMT starch led to higher tensile strength and extensibility in resultant noodles. WF with added HMT starch had higher resistant starch than with native starch. This study showed that addition of HMT maize starch has potential to bring nutritional benefits. However, it is necessary to select the proper blending ratio and amylose content of starch to add, in consideration of its effect on noodle quality.     

Clustering adaptive canonical correlations for high-dimensional multi-modal data

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.     

不同降雨入渗条件下预应力锚索加固边坡稳定性分析

为了研究不同降雨条件下边坡安全系数及预应力锚索轴力变化规律,以省道S303沿线某边坡为研究对象,考虑土体饱和-非饱和渗流与非饱和强度理论,基于有限元分析,对不同降雨工况下的孔隙水压分布、锚索轴力及采用强度折减法计算的边坡安全系数变化规律进行了研究。结果表明: 降雨量一定时,降雨入渗范围随雨强增大而减小,暂态饱和区域随雨强增加而增大; 边坡在加固前后的安全系数均随降雨量增加而不断减小,采用预应力锚索加固能有效提高边坡安全系数,并减小降雨对边坡稳定性的影响; 随着降雨量增加,各锚索轴力均增大,当降雨量一定时,雨强越小,对应的锚索轴力提升幅度越大,靠近底部的锚索轴力对降雨最为敏感。     

超生雾化降尘研究及应用

为解决某矿破碎筛分车间粉尘污染问题，采用喷嘴雾化理论设计喷嘴雾化降尘实验，并根据实验结果在该矿破碎筛分车间应用了超声雾化降尘技术。实验及应用结果表明：超声雾化发生器的液体雾化装置，气体辅助雾化装置和雾束形成器的几何结构影响喷雾的雾化参数与流量特性，液体雾化器与气体辅助雾化装置影响液体初次破碎与雾化，雾束形成器影响雾滴的二次破碎与约束液束形态；超声雾化发生器的气液比与气液压力比存在幂函数关系，幂指数为-1.09；雾滴粒径越小，雾滴的降尘效率越高，当雾滴D50为23 μm时，雾滴的降尘效率在94%以上；在破碎筛分车间振动筛给料皮带和筛上皮带受料点布置超声雾化发生器，超声雾化发生器开启后，车间内粉尘浓度降低至586～10.54 mg/m3，降尘效率在71.38%以上。