Insights into the intrinsic interaction between series of C1 molecules and surface of NiO oxygen carriers involved in chemical looping processes

Understanding and modulating the interaction between various reactive molecules and oxygen carriers are the key issue to achieve process intensification of chemical looping technology. C1 chemical molecules play an important role in many reactions involved with chemical looping processes. However, up to now, there is still a lack of systematic and in-depth understanding of the adsorption mechanism of C1 molecules on the surface of oxygen carriers (OCs). In this work, the intrinsic interaction between a series of C1 molecules composed of CH₄, CO, CO₂, CH₃OH, HCHO and HCOOH and surface of NiO OCs in the chemical looping process have been studied using density functional theory calculations. Various adsorption configurations of C1 molecules and also different adsorption sites of NiO have been considered. The structural features of stable configuration of C1 molecules on the surface of NiO OCs have been obtained. Further, the interacted sites, types and strengths of C1 molecules on the surface of NiO have been directly pictured by the independent gradient model methods. Also, the nature of the interaction between C1 molecule and NiO surface has been investigated with the aid of energy decomposition analysis from a quantitative view.     

煤焦油加工技术的探讨

煤焦油曾为近代有机化工作出了历史性贡献。我国煤焦油资源丰富,但尚未得到有效和合理利用。根据高温煤焦油的组成特点,对煤焦油的初馏、沥青加工利用和煤焦油产品的深加工技术进行了探讨,提出一些建议。此外还讨论了煤焦油加工企业的发展模式。     

Honey bees mating optimization algorithm for process planning problem

Process planning is a very important function in the modern manufacturing system. It impacts the efficiency of the manufacturing system greatly. The process planning problem has been proved to be a NP-hard problem. The traditional algorithms cannot solve this problem very well. Therefore, due to the intractability and importance of process planning problem, it is very necessary to develop efficiency algorithms which can obtain a good process plan with minimal global machining cost in reasonable time. In this paper, a new method based on honey bees mating optimization (HBMO) algorithm is proposed to optimize the process planning problem. With respect to the characteristics of process planning problem, the solution encoding, crossover operator, local search strategies have been developed. To evaluate the performance of the proposed algorithm, three experiments have been carried out, and the comparisons among HBMO and some other existing algorithms are also presented. The results demonstrate that the HBMO algorithm has achieved satisfactory improvement.     

Effect of cobalt oxide on surface structure of alumina supported molybdena catalysts studied by in situ Raman spectroscopy

A set of Co promoted 10% Mo/Al₂O₃ samples have been characterized by means of Raman spectroscopy under ambient as well as in situ dehydrated conditions. Under ambient conditions, the degree of the polymerization of surface molybdenum oxide species decreases with increasing Co loading. Under dehydrated conditions, the polymeric molybdenum oxide species is absent with the addition of only 0.2% Co. At low Co loadings (2%), before the formation of CoMoO₄ compound, the spectral features are very similar under ambient conditions. Dehydration causes the upward shift of the Mo=O symmetric stretching mode. A broad band around 920–930 cm^–1 was thus observed. This band has been suggested to be associated with the Co-Mo interaction species. In contrast to crystalline CoMoO₄, this species shows a reversibility on H₂ reduction-O₂ reoxidation treatments. From the results obtained, it is proposed that cobalt oxide interacts with the most polymerized molybdenum oxide species to form Co-Mo interaction species and/or crystalline CoMoO₄; therefore, the amount of the surface molybdenum oxide species decreases with a change in the molecular structure as a function of the Co concentration.     

New software developments for quality mesh generation and optimization from biomedical imaging data

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit.     

Catalytic activities and properties of mesoporous sulfated Al2O3–ZrO2

Mesoporous sulfated Al₂O₃–ZrO₂ (MSAZ) catalysts with large surface areas and pore volumes after calcination at high temperature (650 °C) and with higher Al₂O₃ content than 20wt% were successfully prepared from a template of block copolymer (P84). The MSAZ catalysts were characterized by X-ray diffraction (XRD), N₂ adsorption, transmission electron microscopy (TEM), ²⁷Al magic-angle spinning nuclear magnetic resonance (MAS NMR), thermogravimetric analysis (TG–DTG), temperature-programmed desorption of ammonia (NH₃-TPD) and infrared spectra (IR) of adsorbed pyridine. It is shown that the resulting mesostructured sulfated Al₂O₃–ZrO₂ samples have a well-developed textural mesoporosity. The number of acid sites present on MSAZ catalysts is higher than that on conventional sulfated zirconia, and the former catalysts are more active than the latter one for various acid-catalyzed reactions.     

Approximate inference for dynamic Bayesian networks: sliding window approach

Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN to make inferences in uncertain conditions. Unfortunately, exact inference or even approximation in a DBN has been proved to be NP-hard and is generally computationally prohibitive. In this paper, we investigate a sliding window framework for approximate inference in DBNs to reduce the computational burden. By introducing a sliding window that moves forward as time progresses, inference at any time is restricted to a quite narrow region of the network. The main contributions to the sliding window framework include an exploration of its foundations, explication of how it operates, and the proposal of two strategies for adaptive window size selection. To make this framework available as an inference engine, the interface algorithm widely used in exact inference is then integrated with the framework for approximate inference in DBNs. After analyzing its computational complexity, further empirical work is presented to demonstrate the validity of the proposed algorithms.     

Co-Mo-K催化剂上COS、HCN转化率的热力学分析

利用热力学基础数据和相关软件对Co-Mo-K催化剂上COS、HCN加氢和水解反应进行了热力学计算。在热力学上,COS、HCN加氢和水解反应在耐硫变换条件下均为自发过程。计算结果表明在Co-Mo-K耐硫变换条件下COS、HCN主要发生的是水解反应,该结果与工业经验数据基本一致,可以作为工业实际借鉴。