Microstructures and properties of nickel-titanium carbide composites fabricated by laser cladding

In this work, Ni/TiC composites were synthesized by the laser cladding technique (LCT). A scanning electron microscope (SEM), X-ray diffractometer (XRD), microhardness meter, electrochemical workstation, and friction and wear tester examined the microstructure, surface morphology, phase structure, microhardness, wear, and corrosion resistances of the Ni/TiC composites. These results indicated the Ni/40TiC composite contained finer equiaxed crystals than the Ni and Ni/20TiC composites. In addition, numerous TiC particles in the Ni/40TiC composite impeded growth of the nickel crystals, which resulted in the fine microstructure of the Ni/40TiC composite. The Ni, Ni/20TiC, and Ni/40TiC composites exhibited face-centered cubic (f c c) lattices. The average microhardness values of the Ni/20TiC and Ni/40TiC composites were approximately 748 HV and 851 HV, respectively. The Ni/40TiC composite had the lowest friction coefficient (0.43) among all three coatings, and only some shallow scratches appeared on the surface of the Ni/40TiC composite. The corrosion potential (E) of Ni/40TiC exceeded the Ni/20TiC composite, and both were larger than the Ni composite, which indicated the Ni/40TiC composite had outstanding corrosion resistance and the Ni composite had poor corrosion resistance. The corrosion current densities (i) of Ni, Ni/20TiC, and Ni/40TiC composites were 5.912, 4.405, and 3.248 μA/cm², respectively.     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

Development of high pressure membraneless alkaline electrolyzer

A technology for cyclic generation of hydrogen and oxygen using electrodes made of variable valency material that does not need the use of separating ion-exchange membranes is presented. The technological solution enables to fabricate electrolyzers for uninterrupted producing high-pressure hydrogen with reduced energy intensity of the production. The total work for compressing 1 m³ of hydrogen and 0.5 m³ of oxygen has been estimated. Results of investigation of influence of discrete supply of DC current to the electrolysis cell, in order to improve the processes of gas evolution and to simplify the power systems of the electrolysis plant, have been considered. There is also considered an electrolysis installation equipped with a thermosorption compressor in which LaNi₅ is used as a hydride-forming compound. The comparative characteristics of the developed electrolyzer and the currently used hydrogen generators are given.     

The production of Indo-Pacific monochrome drawn glass beads in the Sasanian Empire: Insights from Xinjiang,northwest China

Indo-Pacific glass beads are produced by the drawn technique, which originates from South Asia, and their chemical compositions are unique in South and Southeast Asia. However, a small number of Indo-Pacific beads with Sassanian glass compositions are excavated in Asia and Africa after the 3rd c. CE, and their production sites in South/Southeast Asia or in the Sassanian region remain controversial. In this study, 15 drawn glass beads with various colours from Astana necropolis (ca. the 4th-8th c. CE) in Xinjiang, northwest China were investigated by using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), Scanning Electron Microscope, Raman spectroscopy and visible to near-infrared spectroscopy to characterize the production technology and origins. The results show that most Astana glass beads share similar chemical compositions with the glassware from Veh Arda?īr, a famous Sasanian site. Furthermore, Sasanian glass compositions predominate in Indo-Pacific beads in Xinjiang during the 4th-8th c. CE, while popular glass recipes in contemporary South/Southeast Asia are infrequently found; thus, it is deduced that the drawn method should have been mastered by Sasanian craftsmen. Moreover, the cobalt materials in Sasanian glass were imported from further western regions and changed over time. The popular Sasanian glass across central Eurasia reflects the trade monopoly of Sasanian in West and Central Asia, and the land glass bead trade is distinct from the contemporary maritime trade in the Indian and Pacific Oceans.     

Optimization of flow in additively manufactured porous columns with graded permeability

Chemical engineering systems often involve a functional porous medium, such as in catalyzed reactive flows, fluid purifiers, and chromatographic separations. Ideally, the flow rates throughout the porous medium are uniform, and all portions of the medium contribute efficiently to its function. The permeability is a property of a porous medium that depends on pore geometry and relates flow rate to pressure drop. Additive manufacturing techniques raise the possibilities that permeability can be arbitrarily specified in three dimensions, and that a broader range of permeabilities can be achieved than by traditional manufacturing methods. Using numerical optimization methods, we show that designs with spatially varying permeability can achieve greater flow uniformity than designs with uniform permeability. We consider geometries involving hemispherical regions that distribute flow, as in many glass chromatography columns. By several measures, significant improvements in flow uniformity can be obtained by modifying permeability only near the inlet and outlet.     

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.     

Topology optimization of flexure hinges with a prescribed compliance matrix based on the adaptive spring model and stress constraint

This paper focuses on the configuration design of flexure hinges with a prescribed compliance matrix and preset rotational center position. A new method for the topology optimization of flexure hinges is proposed based on the adaptive spring model and stress constraint. The hinge optimization model is formulated by maximizing the bending displacement with a spring while optimizing the compliance matrix to a prescribed value. To avoid numerical instability, an artificial spring is used as an auxiliary calculation, and a new strategy is developed for adaptively adjusting the spring stiffness according to the prescribed compliance matrix. The maximum stress of flexure hinge is limited by using a normalized P-norm of the effective von Mises stress, and a position constraint of rotational center is proposed to predetermine the position of the rotational center. In addition, to reduce the error of the stress measurement, a simple but effective filtering method is presented to obtain a complete black-and-white design. Numerical examples are used to verify the proposed method. Topology results show that the obtained flexure hinges have the prescribed compliance matrix and preset rotational center position while also meeting the stress requirements.     

2.3–21 GHz broadband and high linearity distributed low noise amplifier

This paper focuses on the design of a 2.3–21 GHz Distributed Low Noise Amplifier (LNA) with low noise figure (NF), high gain (S₂₁), and high linearity (IIP3) for broadband applications. This distributed amplifier (DA) includes S/C/X/Ku/K-band, which makes it very suitable for heterodyne receivers. The proposed DA uses a 0.18 μm GaAs pHEMT process (OMMIC ED02AH) in cascade architecture with lines adaptation and equalization of phase velocity techniques, to absorb their parasitic capacitances into the gate and drain transmission lines in order to achieve wide bandwidth and to enhance gain and linearity. The proposed broadband DA achieved an excellent gain in the flatness of 13.5 ± 0.2 dB, a low noise figure of 3.44 ± 1.12 dB, and a small group delay variation of ±19.721 ps over the range of 2.3–21 GHz. The input and output reflection coefficients S₁₁ and S₂₂ are less than −10 dB. The input compression point (P_1dB) and input third-order intercept point (IIP3) are −1.5 dBm and 11.5 dBm, respectively at 13 GHz. The dissipated power is 282 mW and the core layout size is 2.2 × 0.8 mm².