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Microbial electrochemical technologies (METs) have a number of potential technological applications. In this work, we report the use of screen-printed electrodes (SPEs) as a tool to analyze the microbial electroactivity by using Geobacter sulfurreducens as a model microorganism. We took advantage of the small volume required for the assays (75 μL) and the disposable nature of the manufactured strips to explore short-term responses of microbial extracellular electron transfer to conductive materials under different scenarios. The system proved to be robust for identifying the bioelectrochemical response, while avoiding complex electrochemical setups, not available in standard biotechnology laboratories. We successfully validated the system for characterizing the response of Geobacter sulfurreducens in different physiological states (exponential phase, stationary phase, and steady state under continuous culture conditions) revealing different electron transfer responses. Moreover, a combination of SPE and G. sulfurreducens resulted to be a promising biosensor for quantifying the levels of acetate, as well as for performing studies in real wastewater. In addition, the potential of the technology for identifying electroactive consortia was tested, as an example, with a mixed population with nitrate-reducing capacity. We therefore present SPEs as a novel low-cost platform for assessing microbial electrochemical activity at the microscale level.

A laboratory-scale analysis using coal from an underground mine was carried out, emulating a mixture from the gob area in an actual mine, consisting of waste, coal, and free space for the flow of air. Experimental tests and computational fluid dynamics modelling were done to define and verify the behavior of the collapsed region in a time-dependent analysis. In addition, the characteristics of coal were defined, regarding the self-combustion, combustion rate, and pollutants generated in each stage of the fire. The results achieved are useful for determining the behavior of the collapsed area in full-scale conditions and to provide valuable information to study different scenarios of a potential fire in a real sublevel coal mine regarding how the heat is spread in the gob and how pollutants are generated.

The organic geochemical features of 30 sampled oils from the northern Bolivar Coastal Complex (Lake Maracaibo Basin, NW Venezuela) were examined by combining carbon isotope, classical biomarker, and extended diamondoid analyses to clarify source facies and to assess the thermal maturity and extent of biodegradation of the oils analyzed. In this work, oils are understood as a mixture of two episodes of petroleum generation from the La Luna Formation: a paleobiodegraded oil pulse during Paleogene times and a late pulse having a higher maturity in the post-Oligocene. For the oil samples analyzed, results revealed a mixing composed of different proportions of almost a terrestrially derived siliciclastic-sourced and a purely marine carbonate-sourced oil pulse. Moreover, two main groups of oils were identified by means of hierarchical cluster analysis. Finally, inter- and intrafield variations in the extent of biodegradation were also assessed using two classification schemes (Peters and Moldowan, and Manco scales).

A fire has been analyzed in a real underground coal mine, using a sublevel method, during an entire year. The study was focused on the collapsed area, reproducing a real mixture formed by coal, waste, and air gap. The analysis was done by means of an experimental analysis, a computational fuid dynamic model (CFD), and simulations using a mine ventilation software. Three scenarios were determined and studied regarding their influence on the evolution of the fire: (a) development of the fire without taking any action, (b) sealing off the affected areas, and (c) sealing and reducing the ventilation in the affected area and surrounding drifts. The study revealed the behavior of the fire in a real mine and the effectiveness of the main fire-fighting measures over time, verifying that none of the measures taken could eliminate the fire-induced in the collapsed area.

The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited parameters for modeling and control of crack growth, and it is broadly used at the industrial level. We have modeled, through multivariate analysis techniques, the relationships among CTOD values and other material properties (such as hardness, chemical composition, toughness, and microstructural morphology) in high-thickness offshore steel welded joints. In order to create this model, hundreds of tests were done on 72 real samples, which were welded with a wide range of real industrial parameters. The obtained results were processed and evaluated with different multivariate techniques, and we established the significance of all the chosen explanatory variables and the good predictive capability of the CTOD tests within the limits of the experimental variation. By establishing the use of this model, significant savings can be achieved in the manufacturing of wind generators, as CTOD tests are more expensive and complex than the proposed alternatives. Additionally, this model allows for some technical conclusions.

Remaining useful life (RUL) estimation is considered as one of the most central points in the prognostics and health management (PHM). The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS), which have been successfully adopted for regression problems, with the artificial bee colony (ABC) technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables) for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.