The Distribution of Active ingredients in Supported Catalysts Prepared by Impregnation

Supported catalysts, one of the commonest forms of heterogeneous catalysts in practical use, consist of small crystallites of a catalytically active component dispersed in a porous support of high surface area. Impregnation of the support with an aqueous solution of a compound containing the appropriate catalytic component is an important and frequently used method of preparing this type of catalyst. A nonaqueous solution should be used if the sup port surface is hydrophobic or if hydrolysis of the support surface is to be avoided. In its simplest form, this impregnation method involves three steps: (1) contacting the support with impregnating solution for a certain period of time, (2) drying the support to remove the imbibed liquid, and (3) activating the catalyst by calcination, reduction, or other appropriate treatment.     

Sampling Search-Engine Results

We consider the problem of efficiently sampling Web search engine query results. In turn, using a small random sample instead of the full set of results leads to efficient approximate algorithms for several applications, such as:

Network visualization by semantic substrates

Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations     

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.     

Efficient and flexible anonymization of transaction data

Transaction data are increasingly used in applications, such as marketing research and biomedical studies. Publishing these data, however, may risk privacy breaches, as they often contain personal information about individuals. Approaches to anonymizing transaction data have been proposed recently, but they may produce excessively distorted and inadequately protected solutions. This is because these approaches do not consider privacy requirements that are common in real-world applications in a realistic and flexible manner, and attempt to safeguard the data only against either identity disclosure or sensitive information inference. In this paper, we propose a new approach that overcomes these limitations. We introduce a rule-based privacy model that allows data publishers to express fine-grained protection requirements for both identity and sensitive information disclosure. Based on this model, we also develop two anonymization algorithms. Our first algorithm works in a top-down fashion, employing an efficient strategy to recursively generalize data with low information loss. Our second algorithm uses sampling and a combination of top-down and bottom-up generalization heuristics, which greatly improves scalability while maintaining low information loss. Extensive experiments show that our algorithms significantly outperform the state-of-the-art in terms of retaining data utility, while achieving good protection and scalability.     

Development and industrial application of soft sensors with on-line Bayesian model updating strategy

This paper deals with the issues associated with the development of data-driven models as well as model update strategy for soft sensor applications. A practical yet effective solution is proposed. Key process variables that are difficult to measure are commonly encountered in practice due to limitations of measurement techniques. Even with appropriate instruments, some measurements are only available through off-line laboratory analysis with typical sampling intervals of several hours. Soft sensors are inferential models that can provide continuous on-line prediction of hidden variables; such models are capable of combining real-time measurements with off-line lab data. Due to the prevalence of plant-model mismatch, it is important to update the model using the latest reference data. In this paper, parameters of data-driven models are estimated using particle filters under the framework of expectation–maximization (EM) algorithms. A Bayesian methodology for model calibration strategy is formulated. The proposed framework for soft sensor development is applied to an industrial process to provide on-line prediction of a quality variable.     

A global study of Kondepudi’s pitchfork

This model reaction, studied so effectively by Kondepudi and his co-workers, is transferred here to a stirred tank. It is shown that, though the rate constants for the two pathways are identical, the stable steady state can still be one in which one or the other pathway is favoured enormously. We are indebted for partial support to the National Science Foundation through heir grant CPE 8112292.