Partial Oxidation of Methane to Syngas over Calcined Ni–Mg/Al Layered Double Hydroxides

The catalytic partial oxidation of CH₄ to syngas was carried out over an Ni–Mg/Al mixed-oxide catalyst prepared from layered double hydroxide-type precursors. The catalysts were characterized by XRD, TPR, UV-DRS, XRF, BET and CHNS analysis. The effects of the catalyst composition and the calcination temperature on the catalytic performance and the extent of catalyst deactivation were investigated. Ni–Mg/Al oxide catalysts converted CH₄ into syngas efficiently with high selectivity. The catalyst performance was strongly related to the Ni particle size and the calcination temperature. The catalysts that were calcined at higher temperature exhibited a better catalytic performance. In conclusion, the NiAl₂O₄ spinel phase had a positive effect on the stability of the catalyst.     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.     

A continuous-time framework for least squares parameter estimation

This paper proposes a continuous-time framework for the least-squares parameter estimation method through evolution equations. Nonlinear systems in the standard state space representation that are linear in the unknown, constant parameters are investigated. Two estimators are studied. The first one consists of a linear evolution equation while the second one consists of an impulsive linear evolution equation. The paper discusses some theoretical aspects related to the proposed estimators: uniqueness of a solution and an attractive equilibrium point which solves for the unknown parameters. A deterministic framework for the estimation under noisy measurements is proposed using a Sobolev space with negative index to model the noise. The noise can be of large magnitude. Concrete signals issued from an electronic device are used to discuss numerical aspects.     

聚氯乙烯塑料中多种邻苯二甲酸酯类增塑剂的同时测定

为了在线监控聚氯乙烯塑料中增塑剂的含量,利用傅立叶变换红外光谱法-ATR技术测定样品的红外光谱,采用偏最小二乘法对所测红外光谱进行分析,建立了一种无损快速分析方法,定量地测定聚氯乙烯塑料中多种邻苯二甲酸酯类增塑剂的含量。该方法测量精密度的变异系数小于6%,测量准确度的变异系数小于3%。该方法不受基体的干扰,简单快速,适合于流水线的质量控制。     

线性多智能体系统一致性问题的部分稳定性方法（英文）

A linear transformation is proposed to deal with the consensus problem of high-order linear multi-agent systems(LMASs).In virtue of the linear transformation, the consensus problem is equivalently translated into a partial stability problem. We discuss three issues of the LMASs under a generalized linear protocol: 1) to find criteria of consensus convergence; 2) to calculate consensus function; 3) to design gain matrices in the linear consensus protocol. Precisely, we provide a necessary and sufficient criterion of consensus convergence in terms of Hurwitz stability of a matrix and give an analytical expression of the consensus function. In addition, we set up a relation between the gain matrices in the protocol and the convergence time and consensus accuracy of the agents, and then design the gain matrices with respect to a pre-specified convergence time and a required consensus accuracy.     

ESTIMATION OF THE PREDICTION ERROR VARIANCE AND AN R2 MEASURE BY AUTOREGRESSIVE MODEL FITTING

Abstract. For predicting the future values of a stationary process { x_t } ( t = 0, pL 1, pL 2,…) on the basis of its past, two key parameters are the variance V ( h ), h ≥ 1, of the h -step prediction error and Z ( h ) ={ R (0) - V ( h )}/ R (0), the corresponding measure, in an R ² sense, of the predictability of the process from its past, where R (0) denotes the process variance. The estimation of V ( h ) and Z ( h ) from a realization of T consecutive observations of { x_t } is considered, without requiring that the process follows a finite parameter model. Three different autoregressive estimators are examined and are shown to be asymptotically equivalent in the sense that as T ∝ they have the same asymptotic normal distribution. The question of bias in estimating these parameters is also examined and a bias correction is proposed. Finite sample behaviour is investigated by a simulation study.     

A fuzzy constraint-based approach to data reconciliation in material flow analysis

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least-squares minimization. Here, we use a fuzzy set-based approach, replacing Gaussian likelihood functions by fuzzy intervals, and a leximin criterion. We show that the setting of fuzzy sets provides a generalized approach to the choice of estimated values, that is more flexible and less dependent on oftentimes debatable probabilistic justifications. It potentially encompasses interval-based formulations and the least squares method, by choosing appropriate membership functions and aggregation operations. This paper also lays bare the fact that data reconciliation under the fuzzy set approach is viewed as an information fusion problem, as opposed to the statistical tradition which solves an estimation problem.     

Partial oxidation of methane to synthesis gas over α-Al2O3-supported bimetallic Pt–Co catalysts

The partial oxidation of methane to synthesis gas was studied at atmospheric pressure and in the temperature range of 550–800°C over -Al₂O₃-supported bimetallic Pt–Co, and monometallic Pt and Co catalysts, respectively. Both methane conversion and CO selectivity over a bimetallic Pt_0.5Co₁ catalyst were higher than those over monometallic Pt_0.5 and Co₁ catalysts. Furthermore, the addition of platinum in Pt–Co bimetallic catalysts effectively improved their resistance to carbon deposition with no coking occurring on Pt_0.5Co₁ during 80 h reaction. The FTIR study of CO adsorption observed only linearly bonded CO on bimetallic Pt–Co catalysts. TPR and XPS showed enhanced formation of a cobalt surface phase (CSP) in bimetallic Pt–Co catalysts. The origins of the good coking resistivity of bimetallic Pt–Co catalysts were discussed.     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.