氢氧化铝微粉生产过程中晶种分解的研究

晶种分解是种分法生产氢氧化铝微粉的工序，介绍了控制铝酸钠精制液的分解温度、分解时间、搅拌强度等条件并添加特制的高活性晶种做晶种生产优质氢氧化铝微粉的生产过程，并通过对几个月的生产跟踪分析研究，得出氢氧化铝微粉的晶种分解主要与晶种的质量和数量、分解温度、分解时间及搅拌强度等因素有关，这对于进一步完善氢氧化铝微粉的生产工艺起到很大的作用。     

氧化锌对高胶凝性水泥熟料矿物形成及强度的影响

采用工业原料配料，对以硅酸三钙（C3S，C=CaO，S=SiO2）、硫铝酸钙（C4A3 S^-，C=CaO，A=Al2O3，S^-=SO4^2-）为主导矿物的含C4A3 S^-矿物硅酸盐水泥熟料的矿物形成及其强度进行了研究。借助X射线衍射和热重一差热分析研究了矿物形成过程；探讨了微量元素ZnO对熟料性能的影响及在熟料体系中的固溶情况。研究结果表明：生料中掺入ZnO可使C4A3 S^-和CaS等矿物通过低温反应形成，共存于同一熟料体系中，并可提高熟料的强度；过高的ZnO含量亦会降低C4A3 S^-矿物的分解温度。     

Multivariable adaptive control: A survey

Adaptive control is a control methodology capable of dealing with uncertain systems to ensure desired control performance. This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable adaptive control, and a thorough presentation of various adaptive control schemes for multi-input–multi-output systems, literature reviews on adaptive control foundations and multivariable adaptive control methods, and related technical problems. It covers some basic concepts and issues such as certainty equivalence, stability, tracking, robustness, and parameter convergence. It discusses some of the most important topics of adaptive control: plant uncertainty parametrization, stable controller adaptation, and design conditions for different adaptive control schemes. The paper also presents a detailed study of well-developed multivariable model reference adaptive control theory and design techniques. It provides an introduction to multivariable adaptive pole placement and adaptive nonlinear control, and it concludes by identifying some open research problems.     

Mathematical models of the thermal decomposition of coal: 3. Density,porosity and contraction behaviour

Mathematical models are proposed to predict the true density of coal, semi-coke and coke and the porosity of a coke oven charge during carbonization. These models are combined in a further model which predicts the hitherto unexplained ‘twin-peaked’ contraction curve observed for semi-coke. It is found that the first contraction peak results from the relatively high rate of mass loss occurring towards the end of the primary devolatilization stage. The second peak occurs because of the increase in true density arising from the elimination of hydrogen from the semi-coke.     

Role of La2O3 in Pd-Supported Catalysts for Methanol Decomposition

Systems of Pd supported on various La₂O₃-modified -Al₂O₃ and CeO₂–Al₂O₃ catalysts were tested for catalytic methanol decomposition and characterized by means of XRD, BET, TPR, H₂-chemisorption and CO–FTIR. The addition of lanthanum significantly improved the selectivity of CO and H₂ for all the catalysts but showed a different influence on the catalytic activity in two systems. Methanol conversion decreased on La₂O₃-modified Pd/-Al₂O₃ catalysts, in line with the reduction of Pd dispersion, while the addition of La₂O₃ improved the dispersion of Pd and reinforced Pd–CeO₂ interaction for La₂O₃-modified Pd/CeO₂–Al₂O₃ catalysts, which resulted in a high production rate of CO and H₂. Thus, a synergistic effect between CeO₂ and La₂O₃ was observed on -Al₂O₃-supported Pd catalyst for methanol decomposition.     

负载型钴铜双金属氮化物催化氨分解研究

Co and Mo bimetallic nitrides supported on Mg（Al）O, MgO and γ-Al2O3 were prepared in temperatureprogrammed reactions with NH3. The surface morphology, chemical composition and catalytic activity for NH3 decomposition on the supported Co and Mo bimetallic nitrides were studied by X-ray diffractometer （XRD）, NH3 temperature-programmed desorption and mass spectrometer （NH3-TPD-MS）, temperature-programmed desorption and mass spectrometer （TPD-MS）, H2 temperature-programmed surface reaction （H2-TPSR） and activity test. The phases of Co3Mo3N and MoN could be formed on Mg（Al）O, MgO and Al2O3 during the nitridation, and they might be more uniformly dispersed on Mg（Al）O and MgO than on γ-Al2O3. Transition metallic nitrides are generally considered as potential catalysts for hydrogen-involving reactions due to the entrance of hydrogen atoms into subsurface and the lattice of metallic nitrides. The diffusion of nitrogen in the bulk and the structure transformation of Co and Mo nitride compounds occur during NH3-TPD, but the supported Co and Mo bimetallic nitrides are not easily reduced at H2 atmosphere. Co3Mo3N/Mg（Al）O catalyst exhibits the highest activity, while Co3Mo3N/Al2O3 exhibits the lowest activity for NH3 decomposition. Furthermore, the catalytic activity of Co and Mo bimetallic nitrides is not only much higher than that of supported single metallic nitride, but also highly dependent upon the surface acidity and BET surface area of support.     

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.     

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.     

Thermal conversion (visbreaking) of heavy Iraqi residue

A heavy Iraqi residue was thermally cracked using a soaker visbreaking process at temperatures between 435 and 480 °C, with a range of residence times of 43–109 s in the coil and 151–379 s in the soaker. The pressure was kept constant at 7 bar. Visbroken products were characterized, evaluated and fractionated into light, middle and heavy cuts. The stability of the visbroken products was compared with that of the original feedstock. Also an activation energy was calculated for the visbreaking process and related to the severity of the process. The results obtained show that the conditions used were not severe and could be applied to obtain a required conversion depending on the uses of the products.     

A Dantzig–Wolfe decomposition algorithm for linear economic model predictive control of dynamically decoupled subsystems

This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input limits, input rate limits, and soft output limits. The objective function of the linear program is related directly to the cost of operating the subsystems, and the cost of violating the soft output constraints. Simulations for large-scale economic power dispatch problems show that the proposed algorithm is significantly faster than both state-of-the-art linear programming solvers, and a structure exploiting implementation of the alternating direction method of multipliers. It is also demonstrated that the control strategy presented in this paper can be tuned using a weighted ℓ₁-regularization term. In the presence of process and measurement noise, such a regularization term is critical for achieving a well-behaved closed-loop performance.