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.     

Boron doping induced electronic reconfiguration of Fe-Nx sites in N-doped carbon matrix for efficient oxygen reduction reaction in both alkaline and acidic media

Developing non-precious metal-based catalysts as the substitution of precious catalysts (Pt/C) in oxygen reduction reaction (ORR) is crucial for energy devices. Herein, a template and organic solvent-free method was adopted to synthesize Fe, B, and N doped nanoflake-like carbon materials (Fe/B/N–C) by pyrolysis of monoclinic ZIF-8 coated with iron precursors and boric acid. Benefiting from introducing B into Fe–N–C, the regulated electron cloud density of Fe-N_x sites enhance the charge transfer and promotes the ORR process. The as-synthesized Fe/B/N–C electrocatalyst shows excellent ORR activity of a half-wave potential (0.90 V vs 0.87 V of Pt/C), together with superior long-term stability (95.5% current density retention after 27 h) in alkaline media and is even comparable to the commercial Pt/C catalyst (with a half-wave potential of 0.74 V vs 0.82 V of Pt/C) in an acidic electrolyte. A Zn-air battery assembled with Fe/B/N–C as ORR catalyst delivers a higher open-circuit potential (1.47 V), specific capacity (759.9 mA h g^?1_Zn at 10 mA cm^?2), peak power density (62 mW cm^?2), as well as excellent durability (5 mA cm^?2 for more than 160 h) compared to those with commercial Pt/C. This work provides an effective strategy to construct B doped Fe–N–C materials as nonprecious ORR catalyst. Theoretical calculations indicate that introduction of B could induce Fe-N_x species electronic configuration and is favorable for activation of OH1 intermediates to promote ORR process.     

Highly stable nanocarbon supported Pt catalyst for fuel cell via a molten salt graphitization strategy

Proton exchange membrane fuel cells (PEMFCs) durability has been severely hindered by carbon support poor stability in the cathodic Pt-based catalyst. Herein, a high-surface-area nitrogen-doped graphitic nanocarbon (N-G-CA) with mesopores is developed as Pt support to address PEMFCs durability challenge. Resorcinol-formaldehyde aerogel pyrolyzed carbon aerogel is selected as N-G-CA raw material. Nitrogen atoms are introduced into carbon aerogel via NH₃ heat treatment. Then, nitrogen-doped carbon aerogel is transferred into N-G-CA via heating together with transition-metal salts (one of FeCl₃, FeCl₂, CoCl₂, or MnCl₂, etc.) at 1200 °C. As ORR catalyst, Pt/N-G-CA half-wave potential only lost 10 mV, after 30, 000 cycles accelerated aging test in the rotating-desk-electrode. Only 12 mV voltage loss at 1.5 A/cm2 is observed, after 5, 000 cycles for membrane electrode. Pt/N-G-CA exhibits superior durability and activity than commercial Pt/C. High durability of Pt/N-G-CA is due to N-G-CA high graphitization extent, as well as the interactions between doping nitrogen and Pt. N-G-CA is promising as stable support for durable Pt-based catalysts in PEMFCs, thanks to enhanced carbon corrosion resistance, uniformly dispersed Pt, and strong support-metals interaction.     

A novel fusion paradigm for multi-channel image denoising

Multi-channel and single-channel image denoising are on two important development fronts. Integrating multi-channel and single-channel image denoisers for further improvement is a valuable research direction. A natural assumption is that using more useful information is helpful to the output results. In this paper, a novel multi-channel and single-channel fusion paradigm (MSF) is proposed. The proposed MSF works by fusing the estimates of a multi-channel image denoiser and a single-channel image denoiser. The performance of recent multi-channel image denoising methods involved in the proposed MSF can be further improved at low additional time-consuming cost. Specifically, the validity principle of the proposed MSF is that the fused single-channel image denoiser can produce auxiliary estimate for the involved multi-channel image denoiser in a designed underdetermined transform domain. Based on the underdetermined transformation, we create a corresponding orthogonal transformation for fusion and better restore the multi-channel images. The quantitative and visual comparison results demonstrate that the proposed MSF can be effectively applied to several state-of-the-art multi-channel image denoising methods.     

Numerical study on laminar burning velocity of ammonia flame with methanol addition

The combustion characteristics of ammonia/methanol mixtures were investigated numerically in this study. Methanol has a dramatic promotive effect on the laminar burning velocity (LBV) of ammonia. Three mechanisms from literature and another four self-developed mechanisms constructed in this study were evaluated using the measured laminar burning velocities of ammonia/methanol mixtures from Wang et al. (Combust.Flame. 2021). Generally, none of the selected mechanisms can precisely predict the measured laminar burning velocities at all conditions. Aiming to develop a simplified and reliable mechanism for ammonia/methanol mixtures, the constructed mechanism utilized NUI Galway mechanism (Combust.Flame. 2016) as methanol sub-mechanism and the Otomo mechanism (Int. J. Hydrogen. Energy. 2018) as ammonia sub-mechanism was optimized and reduced. The reduced mechanism entitled ‘DNO-NH3’, can accurately reproduce the measured laminar burning velocities of ammonia/methanol mixtures under all conditions. A reaction path analysis of the ammonia/methanol mixtures based on the DNO-NH3 mechanism shows that methanol is not directly involved in ammonia oxidation, instead, the produced methyl radicals from methanol oxidization contribute to the dehydrogenation of ammonia. Besides, NO_x emission analysis demonstrates that 60% methanol addition results in the highest NO_x emissions. The most important reactions dominating the NO_x consumption and production are identified in this study.     

Sulfur doped biocarbon obtained from Sargassum spp. for the oxygen reduction reaction

Sulfured doped carbon electrocatalysts is synthesized from the waste biomass Sargassum spp. Two doping procedures are examined to determine which is better for Oxygen Reduction Reaction (ORR); one by doping biocarbon obtained from the pyrolysis of the biomass and the second through a process of in situ doping in autoclave. The electrocatalyst are obtained from pyrolysis of the sample at 700 °C, which is finally characterized as a metal free electrocatalyst for the ORR. The electrocatalyst are characterized by BET surface area analysis, Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS) and the electrochemical characterization is determined in 0.1 M KOH. The sample SSKPT-1 exhibits a promising electrocatalytic activity with an onset potential of 0.896 V vs RHE and a current density of 5 mA cm^?2 (at 0.2 V vs. RHE) which could be partly attributed to its high BET surface area of 2755 m² g^?1.     

Physicochemical and technological properties of beef burger as influenced by the addition of pea fibre

This study aimed to evaluate the physicochemical characteristics and sensory attributes of beef burgers with the addition of pea fibre as a partial substitute of meat or fat. Three formulations were prepared: control (CON) – similar to the commercial formulation; fibre/less meat (FLM)—5% meat reduction and addition of 1% pea fibre; fibre/less fat (FLF)—7% fat reduction and addition of 1% pea fibre. Non-significant differences were obtained for pH, colour parameters (L* and b*), texture profile, cooking loss and size reduction among formulations. Moreover, sensory analysis with consumers of beef burgers did not indicate differences among the formulations for all the analysed attributes. Therefore, pea fibre is a promising partial replacer for meat and fat in beef burgers due to the preservation of technological parameters and sensory acceptance.     

用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。