Transformation of the reabsorption characteristics of lignite treated by low and high temperature drying process

Abstract

The reabsorption characteristics of the lignite treated by low and high temperature drying process were addressed in the paper. The information about the moisture form, functional groups, effective water-filled porosities and equilibrium moisture content of the lignite before and after the drying process was investigated using Differential scanning calorimetry (DSC), Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy and a self-made reabsorption device, respectively. The results show that the low drying temperature (140, 190, 230?°C, 10?min, N₂) has little impact on the effective water-filled porosities of the resulted samples, whereas it has a great influence on the main oxygen-containing functional groups, which amount firstly decreases and then increases with the drying temperature increasing. In the case of the lignite samples dried under high-temperature (600, 700, 800?°C, 30?s, N₂), the amount of the effective water-filled porosity of the sample decreases and the amount of oxygen-containing functional groups increases as the temperature increasing. The reabsorption capability of the high temperature dried sample is much lower than that of the sample treated under low drying temperature. The reabsorption characteristics of the low-temperature dried samples are affected by the amount of the oxygen-containing functional groups, while the effective water-filled porosity is main factor for the lignite samples derived from high temperature drying process. Moreover, the work gives a good evidence that the high-temperature drying process is an effective choose for lignite upgrading.     

Validation of Models Predicting Nucleation Rates from Induction Times and Metastable Zone Widths

Three approaches for estimation of nucleation rates from induction time and metastable zone width (MSZW) were validated based on directly measured nucleation rates for paracetamol in ethanol. To quantitatively predict nucleation kinetics using Kubota's methods it is necessary to know the minimum detectable number concentration of nuclei. This was found by determination of light transmission of a series of diluted suspensions of newly nucleated crystals where the size had already been assessed by optical reflection measurement (ORM). The measured nucleation rates strongly depended on both temperature and supersaturation. The Nyvlt method predicted nucleation rates in this system reasonably well; however, it gave slightly low estimates for all temperatures. The methods of Kubota provided nucleation rates that were low by an order of magnitude.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

A disruption recovery plan in a three-stage production-inventory system

This paper proposes a recovery plan for managing disruptions in a three-stage production-inventory system under a mixed production environment. First, a mathematical model is developed to deal with a disruption at any stage while maximizing total profit during the recovery-time window. The model is solved after the occurrence of a disruption event, with changed data used to generate a revised plan. We also propose a new and efficient heuristic for solving the developed mathematical model. Second, multiple disruptions are considered, where a new disruption may or may not affect the recovery plans of earlier disruptions. The heuristic, developed for a single disruption, is extended to deal with a series of disruptions so that it can be implemented for disruption recovery on a real-time basis. We compare the heuristic solutions with those obtained by a standard search algorithm for a set of randomly generated disruption test problems, and that show the consistent performance of our developed heuristic with lower computational times. Finally, some numerical examples and a real-world case study are presented to demonstrate the benefits and usefulness of our proposed approach.     

A short guide to topological terms in the effective theories of condensed matter

This article is meant as a gentle introduction to the topological terms that often play a decisive role in effective theories describing topological quantum effects in condensed matter systems. We first take up several prominent examples, mainly from the area of quantum magnetism and superfluids/superconductors. We then briefly discuss how these ideas are now finding incarnations in the studies of symmetry-protected topological phases, which are in a sense a generalization of the concept of topological insulators to a wider range of materials, including magnets and cold atoms.     

Using provenance to efficiently improve metadata searching performance in storage systems

In cloud storage systems, more than 50% of requests are metadata operations, and thus the file system metadata search performance has become increasingly important to different users. With the rapid growth of storage system scales in volume, traditional full-size index trees cannot offer high-performance metadata search due to hierarchical indexing bottleneck. In order to alleviate the long latency and improve the quality-of-service (QoS) in cloud storage service, we proposed a novel provenance based metadata-search system, called PROMES. The metadata search in PROMES is split into three phases: (i) leveraging correlation-aware metadata index tree to identify several files as seeds, most of which can satisfy the query requests, (ii) using the seeds to find the remaining query results via relationship graph search, (iii) continuing to refine and rerank the whole search results, and sending the final results to users. PROMES has the salient features of high query accuracy and low latency, due to files’ tight and lightweight indexing in relationship graph coming from provenance’s analysis. Compared with state-of-the-art metadata searching schemes, PROMES demonstrates its efficiency and efficacy in terms of query accuracy and response latency.     

Dynamic design of sales territories

We introduce the Multiple Traveling Salesmen and Districting Problem with Multi-periods and Multi-depots. In this problem, the compactness of the subdistricts, the dissimilarity measure of districts and an equity measure of salesmen profit are considered as part of the objective function, and the salesman travel cost on each subdistrict is approximated by the Beardwood–Halton–Hammersley formula. An adaptive large neighbourhood search metaheuristic is developed for the problem. It was tested on modified Solomon and Gehring & Homberger instances. Computational results confirm the effectiveness of the proposed metaheuristic.     

变邻域量子烟花算法求解CVRP

针对带容量约束的车辆路径问题，提出一种融合量子进化算法和变邻域优化策略的变邻域量子烟花算法。该算法采用等分随机键与最大位置法结合的实数编码方式，通过量子旋转门和非门变异提高算法全局搜索能力，同时运用结合2-Opt的变邻域优化策略加强局部搜索能力。选取17个基准算例进行参数实验和对比实验，实验结果表明，相对于对比算法，所提出的算法具有较好的寻优能力和收敛速度。     

航班恢复问题的迭代局部搜索算法

在恶劣天气和机械故障等原因造成航班不能按照原计划执行时，航空公司需要采取相应的措施对航班进行恢复。本文基于经典的资源指派模型，综合考虑了调整时间、换机、联程拉直、取消航班和调机5种恢复策略，提出一种以最小化加权成本为优化目标的航班恢复模型，并设计一种迭代局部搜索算法。首先用构造-修复启发式方法构造可行解，然后从该初始解出发，在飞机路线对的邻域中进行局部搜索。当陷入局部最优后，对解进行扰动，然后从扰动后的解重新出发进行局部搜索。为了提高搜索效率，同时降低陷入局部最优解的概率，局部搜索过程采用模拟退火算法。实例结果表明，本文提出的模型及算法能够在短时间内对受到影响的大规模航班计划进行恢复。