It is sometimes necessary that a process plant operates over a wide range while satisfying desired performance and stipulated constraints. This paper proposes a method based on computing a mean (nominal) plant, uncert...
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Selective oxidation of alkanes to produce highvalue chemicals is an essential strategy and means to realize efficient utilization of *** this work,a strategy of lanthanum manganese mixed metal oxides(LMMO)regulated vi...
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Selective oxidation of alkanes to produce highvalue chemicals is an essential strategy and means to realize efficient utilization of *** this work,a strategy of lanthanum manganese mixed metal oxides(LMMO)regulated via a facile ionic liquid(IL)-assisted hydrothermal method was proposed to construct the multifunctional catalysts,which exhibited excellent catalytic performance in the selective aerobic oxidation of *** 8.9%cyclohexane conversion with 90%KA oi(cyclohexanol and cyclohexanone)selectivity was achieved over the optimal LMMO catalyst under mild *** effects of anion type,carbon chain length and concentration of ILs on the structure and properties of catalysts were investigated through various characterizations,indicating the structure-directing and template effect of ILs on the multifunctional *** formation of self-assembled spherical nanoparticles followed the"dissolution-nucleation-proliferation"mechanism with the introduction of 1-butyl-3-methylimidazolium hydrogen sulfate,ascribing the synergistic effect between the microenvironment of ILs and the hydrothermal ***,the high reactive oxygen concentration redox capacity,and suitable basic sites of LMMO catalysts mediated by ILs enhance the activation of C-H bonds and molecular oxygen,simultaneously influencing the adsorption and desorption of the substrate.A comprehensive understanding of the high KA oil selectivity and radical reaction mechanism was elucidated based on in situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS)and radical trapping *** recycling and regeneration experiments further illuminated that the removal of adsorbed cyclohexanone acting on the LMMO catalyst was the key to achieve high KA oil selectivity.
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.
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.
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.
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.
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