A review of the selective reduction of NOx, with hydrocarbons under lean-burn conditions with non-zeolitic oxide and platinum group metal catalysts

Research on the selective reduction of NOx with hydrocarbons under lean-burn conditions using non-zeolitic oxides and platinum group metal (PGM) catalysts has been critically reviewed. Alumina and silver-promoted alumina catalysts have been described in detail with particular emphasis on an analysis of the various reaction mechanisms that have been put forward in the literature. The influence of the nature of the reducing agent, and the preparation and structure of the catalysts have also been discussed and rationalised for several other oxide systems. It is concluded for non-zeolitic oxides that species that are strongly adsorbed on the surface, such as nitrates/nitrites and acetates, could be key intermediates in the formation of various reduced and oxidised species of nitrogen, the further reaction of which leads eventually to the formation of molecular nitrogen. For the platinum group metal catalysts, the different mechanisms that have been proposed in the literature have been critically assessed. It is concluded that although there is indirect, mainly spectroscopic, evidence for various reaction intermediates on the catalyst surface, it is difficult to confirm that any of these are involved in a critical mechanistic step because of a lack of a direct quantitative correlation between infrared and kinetic measurements. A simple mechanism which involves the dissociation of NO on a reduced metal surface to give N(ads) and O(ads), with subsequent desorption of N-2 and N2O and removal of O(ads) by the reductant can explain many of the results with the platinum group metal catalysts, although an additional contribution from organo-nitro-type species may contribute to the overall NOx reduction activity with these catalysts.

COMPARATIVE-STUDY OF NEW AND ESTABLISHED HETEROGENEOUS OXYGEN CATALYSTS

The kinetics of catalysis of a number of new and established heterogeneous O2 catalysts have been studied using Ce(IV) as the oxidant via both the disappearance of the Ce(IV) ions and concomitant appearance of O2. The most active of the catalysts tested utilised a PGM(IV) oxide, usually Ru or Ir, prepared by the Adams method, which appears to generate microcrystalline powders with high surface areas and optimum activities per unit area.

Advanced Ceramic Substrate with Ordered and Designed Micro-Structure for Applications in Automotive Catalysts

This study describes an innovative monolith structure designed for applications in automotive catalysis using an advanced manufacturing approach developed at Imperial College London. The production process combines extrusion with phase inversion of a ceramic-polymer-solvent mixture in order to design highly ordered substrate micro-structures that offer improvements in performance, including reduced PGM loading, reduced catalyst ageing and reduced backpressure.

This study compares the performance of the novel substrate for CO oxidation against commercially available 400 cpsi and 900 cpsi catalysts using gas concentrations and a flow rate equivalent to those experienced by a full catalyst brick when attached to a vehicle. Due to the novel micro-structure, no washcoat was required for the initial testing and 13 g/ft3 of Pd was deposited directly throughout the substrate structure in the absence of a washcoat.

Initial results for CO oxidation indicate that the advanced micro-structure leads to enhanced conversion efficiency. Despite an 79% reduction in metal loading and the absence of a washcoat, the novel substrate sample performs well, with a light-off temperature (LOT) only 15 °C higher than the commercial 400 cpsi sample.

To test the effects of catalyst ageing on light-off temperature, each sample was aged statically at a temperature of 1000 °C, based on the Bench Ageing Time (BAT) equation. The novel substrate performed impressively when compared to the commercial samples, with a variation in light-off temperature of only 3% after 80 equivalent hours of ageing, compared to 12% and 25% for the 400 cpsi and 900 cpsi monoliths, respectively.

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

Incorporating PGMs into a BDI Architecture

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent’s beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.

Diagnostic value of increased sensitivity by massively parallel sequencing

Routine molecular diagnostics modalities are unable to confidently detect low frequency mutations (<5-15%) that may indicate response to targeted therapies. We confirm the presence of a low frequency NRAS mutation in a rectal cancer patient using massively parallel sequencing when previous Sanger sequencing results proved negative and Q-PCR testing inconclusive. There is increasing evidence that these low frequency mutations may confer resistance to anti-EGFR therapy. In view of negative/inconclusive Sanger sequencing and Q-PCR results for NRAS mutations in a KRAS wt rectal case, the diagnostic biopsy and 4 distinct subpopulations of cells in the resection specimen after conventional chemo/radiotherapy were massively parallel sequenced using the Ion Torrent PGM. DNA was derived from FFPE rectal cancer tissue and amplicons produced using the Cancer Hotspot Panel V2 and sequenced using semiconductor technology. NRAS mutations were observed at varying frequencies in the patient biopsy (12.2%) and all four subpopulations of cells in the resection with an average frequency of 7.3% (lowest 2.6%). The results of the NGS also provided the mutational status of 49 other genes that may have prognostic or predictive value, including KRAS and PIK3CA. NGS technology has been postulated in diagnostics because of its capability to generate results in large panels of clinically meaningful genes in a cost-effective manner. This case illustrates another potential advantage of this technology: its use for detecting low frequency mutations that may influence therapeutic decisions in cancer treatment.