石灰石在煤气化过程中的催化和固硫作用

基于CaO在煤气化中的催化和固硫作用,以及石灰石加热分解的性质,石灰石有可能作为煤气化过程中的催化剂和固硫剂。本文根据国内外对石灰石在煤燃烧和煤气化中作用的研究,从石灰石结构、Ca/C,Ca/S,可能的反应机理等几个方面进行了介绍和分析,并指出需进一步研究的内容。     

循环式活性污泥法处理炼油污水

研究为在炼油污水的CAST工艺的主反应区中 ,通过恰当控制pH值和DO(污水中溶解氧含量 )值 ,可以使硝化与反硝化反应同步进行 ,在去除COD ,BOD的同时 ,也可提高其脱氮效果 ,从而得出DO的最佳值为 0 .8mg/L ,pH为 7.3～ 7.7之间。     

竹材丙酮法木质素高效分离技术研究

在一定的温度、压力条件下,利用丙酮提取竹材中的木质素,丙酮通过蒸馏回收后,可以反复利用;而通过蒸馏,得到的高纯度的木质素是重要的化工原料,整个过程形成一个封闭循环系统,基本上无三废排放;研究结果表明:当丙酮浓度为80%,液固比为10∶1,在最高反应温度160℃下保温3h,降解木质素的效果最佳;为解决制浆造纸行业废水对环境的污染问题提出了一条新的途径。     

Electrochemical performance of nanocrystalline Li3V2(PO4)3/carbon composite material synthesized by a novel sol–gel method

A novel sol–gel method based on V₂O₅·nH₂O hydro-gel was developed to synthesize nanocrystalline Li₃V₂(PO₄)₃/carbon composite material. In this route, V₂O₅·nH₂O hydro-gel, NH₄H₂PO₄, Li₂CO₃ and high-surface-area carbon were used as starting materials to prepare precursor, and the Li₃V₂(PO₄)₃/carbon was obtained by sintering precursor at 750 °C for 4 h in flowing argon. The sol–gel synthesis ensures homogeneity of the precursors and improved reactivity. The sample was characterized by XRD, SEM and TEM. X-ray diffraction results show Li₃V₂(PO₄)₃ sample is monoclinic structure with the space group of P2₁/n. The TEM image indicates that the Li₃V₂(PO₄)₃ particles modified by conductive carbon are about 70 nm in diameter. The Li₃V₂(PO₄)₃/carbon system showed that the discharge capacities in the first and 50th cycle are about 155.3 and 143.6 mAh/g, respectively, in the range of 3.0–4.8 V. The sol–gel method is fit for the preparation of Li₃V₂(PO₄)₃/carbon composite material which may offer some favorable properties for commercial application.     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

Oil supply between OPEC and non-OPEC based on game theory

The competing strategies between OPEC (Organization of the Petroleum Exporting Countries) and non-OPEC producers make the oil supply market a complex system, and thus, it is very difficult to model and to make predictions. In this paper, we combine the macro-model based on game theory and micro-model to propose a new approach for forecasting oil supply. We take into account the microscopic behaviour in the clearing market and also use the game relationships to adjust oil supplies in our approach. For the supply model, we analyse and consider the different behaviour of non-OPEC and OPEC producers. According to our analysis, limiting the oil supply, and thus maintaining oil price, is the best strategy for OPEC in the low-price scenario, while the rising supply is the best strategy in the high-price scenario. No matter what the oil price is, the dominant strategy for non-OPEC producers is to increase their oil supply. In the high-price scenario, OPEC will try to deplete non-OPEC’s share in the oil supply market, which is to OPEC’s advantage.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.     

New polyimide–silica organic–inorganic hybrids

A series of polyimide–silica hybrid films with silica contents up to 30 wt % were successfully prepared by the sol‐gel reaction of tetraethoxysiliane in the presence of poly(amic acid) containing pendent hydroxyl groups. The films were yellow and transparent when the silica content was less than 11 wt %. The chemical structure of the films was characterized by Fourier transform infrared spectroscopy, and the morphology of the films was investigated by scanning electronic microscopy and atomic force microscopy. Thermogravimetric analysis, differential scanning calorimetry, and stress–strain tests were used to measure the performance of the films. The results indicate that the glass‐transition temperatures and decomposition temperatures of the hybrid films increased with increasing silica content, whereas the tensile strength had a maximum with the variety of silica contents. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 2210–2214, 2003     

Seeded Crystallization of Leucite

The leucite crystallization kinetics from a hydrothermally derived precursor seeded with nano-crystalline leucite was investigated by X-ray diffraction and non-isothermal differential thermal analysis. The nano-crystalline leucite was prepared by high-energy milling of high-purity leucite powder and the leucite precursor was prepared by the hydrothermal method of silica sol, aluminum nitrate, and potassium nitrate. After the seeds were introduced, the crystallization temperature of the precursor was lowered by 100°C and the transition phase kalsilite did not appear during the crystallization process. When the seeded precursor was heat treated at 700°C, a small amount of cubic leucite was stabilized to room temperature. The seeded precursor showed an exothermic peak between 800° and 920°C under different heating rates. The activation energy for the growth of leucite from the seeded precursor was 256(SD9) kJ/mol.     

Decolorisation and mineralisation of CI Reactive Black 8 by the Fenton and ultrasound/Fenton methods

The effects of pH, ferrous ion and hydrogen peroxide dosage on the decolorisation and mineralisation of CI Reactive Black 8 by the Fenton process with/without ultrasonic irradiation were investigated. It was verified that the presence of ultrasonic irradiation did not enhance the decolorisation of CI Reactive Black 8 significantly by Fenton's reagents, but it enhanced the chemical oxygen demand removal efficiency. The enhancement was more pronounced for lower (<0.89 mm) or higher (>1.78 mm) ferrous ion dosage. The optimal pH for chemical oxygen demand removal was 3.0. Chemical oxygen demand removal efficiency increased with the increasing H₂O₂ dosage and reached the highest level at 5.88 mm, but further increase in H₂O₂ dosage would not increase removal efficiency significantly.