In this paper,we propose“The Infinite Separation Principle”.This principle contains two implications:firstly,even exhausting all separation approaches,including chemical techniques,it is impossible to achieve 100%pu...
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In this paper,we propose“The Infinite Separation Principle”.This principle contains two implications:firstly,even exhausting all separation approaches,including chemical techniques,it is impossible to achieve 100%purity for separating a mixture;secondly,separation can continue infinitely without an endpoint.
Wolfgang Marquardt, AVT - processsystemsengineering, Germany, shares his views on the recent developments related to the Journal of processcontrol. The steady flow of accepted manuscripts has resulted in a signific...
Wolfgang Marquardt, AVT - processsystemsengineering, Germany, shares his views on the recent developments related to the Journal of processcontrol. The steady flow of accepted manuscripts has resulted in a significant back-log of papers by the end of 2008. The publisher has increased the page quota by 70% to a total number of 1768 pages for the 10 issues in the 2009 volume. A high quality review process is only possible if the editorial board is assisted by qualified referees who provide the Regional Editors with thorough and timely reviews which not only check the originality but also the relevance of a manuscript. Vinay Kariwala has been selected as the Best Referee for 2009. Frank Doyle, the Reviews Editor of the Journal, has been elected to become an IFAC Fellow in 2009. The High Impact Paper Award of IFAC will be given to acknowledge the author of a paper published on the broad areas of control theory and applications in one of the IFAC journals.
Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA, like PCA, is used to identify and remove correlation...
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Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA, like PCA, is used to identify and remove correlations among problem variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA identifies only linear correlations between variables, NLPCA uncovers both linear and nonlinear correlations, without restriction on the character of the nonlinearities present in the data. NLPCA operates by training a feedforward neural network to perform the identity mapping, where the network inputs are reproduced at the output layer. The network contains an internal "bottleneck" layer (containing fewer nodes than input or output layers), which forces the network to develop a compact representation of the input data, and two additional hidden layers. The NLPCA method is demonstrated using time-dependent, simulated batch reaction data. Results show that NLPCA successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.
Developing electrolyte with high electrochemical stability is the most effective way to improve the energy density of double layer capacitors(DLCs), and ionic liquid is a promising choice. Herein, a novel ionic liquid...
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Developing electrolyte with high electrochemical stability is the most effective way to improve the energy density of double layer capacitors(DLCs), and ionic liquid is a promising choice. Herein, a novel ionic liquid based high potential electrolyte with a stabilizer, succinonitrile, was proposed to improve the high potential stability of the DLC. The electrolyte with 7.5 wt% succinonitrile added has a high ionic conductivity of 41.1 m S cm^(-1) under ambient temperature, and the DLC adopting this electrolyte could be charged to 3.0 V with stable cycle ability even under a discharge current density of 6 A g^(-1). Moreover, the energy density could be increased by 23.4% when the DLC was charged to 3.0 V compared to that charged to 2.7 V.
A double layered, one-pot hydrothermal method was adopted in this work to prepare transition metal ions （Fe3＋, Ni2＋, Cu2＋ and Co2＋） doped TiO〉 The morphology and chemical properties of TiO2 and the status of me...
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A double layered, one-pot hydrothermal method was adopted in this work to prepare transition metal ions （Fe3＋, Ni2＋, Cu2＋ and Co2＋） doped TiO〉 The morphology and chemical properties of TiO2 and the status of metal ions were characterized with XRD, TEM, BET, UV-Vis and XPS analysis. TEM images show that the obtained TiO2 was very uniform with an average particle size of 10.4 nm. XPS, TEM and XRD results show that transitional metals were doped onto TiO2 in the form of ions. Photocatalytic decomposition of oxalic acid under UV illumination and methylene blue degradation under visible light on these materials were conducted, respectively. The results reveal that Cu2＋-TiO2 and C02＋-TiO2 showed a highest activity under UV and visible light illumination, respectively, and they were both more active than commercial P25 TiO2. With this special design of double layers, the hydrolysis of titanium precursor in the system with water can be easily controlled and metal ions are simply doped. This strategy can be further applied to synthesize metal ion doped TiO2 using various metal precursors with controllable amounts, and thus lead to better optimization of highly active photocatalyst.
This article presents a methodology for the continuous detection and definition of process performance improvement opportunities, especially as these pertain to the quality of operations (such as product quality). The...
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This article presents a methodology for the continuous detection and definition of process performance improvement opportunities, especially as these pertain to the quality of operations (such as product quality). The problem is first reduced to an essentially pattern recognition formulation for which an integrated and adaptive methodology combining analogical reasoning and symbolic induction is developed. The resulting classification of past records of data is used to support the construction of a decision support system for the generation/selection of operating suggestions leading to performance improvements. The overall approach complements the usual set of statistical tools, commonly employed to address quality management problems. The basic methodology is also extended to handle fuzzy class definitions and function learning formulations. Case studies, covering both simulated and real industrial situations, illustrate the concepts and their practical utility.
A Wave-Net is an artificial neural network with one hidden layer of nodes, whose basis functions are drawn from a family of orthonormal wavelets. The good localization characteristics of the basis functions, both in t...
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A Wave-Net is an artificial neural network with one hidden layer of nodes, whose basis functions are drawn from a family of orthonormal wavelets. The good localization characteristics of the basis functions, both in the input and frequency domains, allow hierarchical, multiresolution learning of input-output maps from experimental data. Furthermore, Wave-Nets allow explicit estimation for global and local prediction error-bounds, and thus tend themselves to a rigorous and explicit design of the network. This article presents the mathematical framework for the development of Wave-Nets and discusses the various aspects of their practical implementation. Computational complexity arguments prove that the training and adaptation efficiency of Wave-Nets is at least an order of magnitude better than other networks. In addition, it presents two examples on the application of Wave-Nets, (a) the prediction of a chaotic time-series, representing population dynamics, and (b) the classification of experimental data for process fault diagnosis.
It is very difficult to directly spin the lignocellulose without *** liquids(ILs)are promising solvent to dissolve lignocellulose to prepare cellulose ***,the degree of cellulose polymerization(DP)is reduced when lign...
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It is very difficult to directly spin the lignocellulose without *** liquids(ILs)are promising solvent to dissolve lignocellulose to prepare cellulose ***,the degree of cellulose polymerization(DP)is reduced when lignocellulose is dissolved in ILs,and the lignin removal rate is *** elongation at break and tensile strength of the fibers obtained by spinning the lignocellulose dissolved in ILs are *** this paper,preparing cellulose fiber directly from lignocellulose based on dissolving corn stalk via[C4mim]Cl-L-arginine binary system is *** shows that the removal rate of lignin can reach 92.35%and the purity of cellulose can reach 85.32%after corn stalk was dissolved at 150℃C for 11.5 h when the mass fraction of arginine is 2.5%.The elongation at break of fiber reached 10.12%and the tensile strength reached 420 *** is mainly due to the fact that L-arginine not only inhibits the degradation of cellulose but also promotes the *** any pulping or pretreatment,preparing cellulose fibers via direct dissolution and extrusion may provide a simple and effective way to prepare many novel cellulose materials.
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