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.     

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。     

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.     

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

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.     

车门铰链系统与车门下沉刚度的相关性

针对某型车门下沉问题,通过台架试验获得车门、铰链和车身等各单因素下沉量和车门绞链系统整体下沉量,对单因素下沉量与系统整体下沉刚度进行线性拟合分析,得到车门铰链系统各单因素与系统下沉刚度的相关度排序.对前、后车门分别选取相关度较高的单因素进行优化,最终改进方案的仿真和试验结果证明该方案可有效地提升车门下沉刚度.采用定量分析法可快速找出影响下沉刚度的敏感因素,并能够快速生成优化方案,为新车型设计提供参考.     

Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.