Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

In-situ FT-IR spectroscopic studies of fuel cell electro-catalysis: From single-crystal to nanoparticle surfaces

The work presented in this article shows the power of the variable temperature, in-situ FT-IR spectroscopy system developed in Newcastle with respect to the investigation of fuel cell electro-catalysis. On the Ru(0001) electrode surface, CO co-adsorbs with the oxygen-containing adlayers to form mixed [CO+(2x2)-O(H)] domains. The electro-oxidation of the Ru(0001) surface leads to the formation of active (1x1)-O(H) domains, and the oxidation of adsorbed CO then takes place at the perimeter of these domains. At 20 degrees C, the adsorbed CO is present as rather compact islands. In contrast, at 60 degrees C, the COads is present as a relatively looser and weaker adlayer. Higher temperature was also found to facilitate the surface diffusion and oxidation of COads. No dissociation or electro-oxidation of methanol was observed at potentials below approximately 950mV; however, the Ru(0001) surface at high anodic potentials was observed to be very active. On both Pt and PtRu nanoparticle surfaces, only one linear bond CO adsorbate was formed from methanol adsorption, and the PtRu surface significantly promoted both methanol dissociative adsorption to CO and its further oxidation to CO2. Increasing temperature from 20 to 60 degrees C significantly facilitates the methanol turnover to CO2.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

A microbial fuel cell in contaminated ground delineated by electrical self-potential and normalized induced polarization data

There is a growing interest in the use of geophysical methods to aid investigation and monitoring of complex biogeochemical environments, for example delineation of contaminants and microbial activity related to land contamination. We combined geophysical monitoring with chemical and microbiological analysis to create a conceptual biogeochemical model of processes around a contaminant plume within a manufactured gas plant site. Self-potential, induced polarization and electrical resistivity techniques were used to monitor the plume. We propose that an exceptionally strong (>800 mV peak to peak) dipolar SP anomaly represents a microbial fuel cell operating in the subsurface. The electromagnetic and electrical geophysical data delineated a shallow aerobic perched water body containing conductive gasworks waste which acts as the abiotic cathode of microbial fuel cell. This is separated from the plume below by a thin clay layer across the site. Microbiological evidence suggests that degradation of organic contaminants in the plume is dominated by the presence of ammonium and its subsequent degradation. We propose that the degradation of contaminants by microbial communities at the edge of the plume provides a source of electrons and acts as the anode of the fuel cell. We hypothesize that ions and electrons are transferred through the clay layer that was punctured during the trial pitting phase of the investigation. This is inferred to act as an electronic conductor connecting the biologically mediated anode to the abiotic cathode. Integrated electrical geophysical techniques appear well suited to act as rapid, low cost sustainable tools to monitor biodegradation.