Simulating the effect of subsurface stresses and transient pore pressure on wellbore stability in subsea horizontal wells

The most prominent challenge associated with offshore horizontal drilling is wellbore stability. In this paper, simulation of in-situ stresses around the wellbore is conducted to study the effects of transient pore pressure on the stability of horizontal wells. The rock mechanical analysis based on finite element technique lead to investigate a unique behavior found in subsea horizontal wells known as transient pore pressure behavior and near wellbore pressure gradients. The results demonstrate that near wellbore pore pressure gradient is only active in rock formations which possess transient pore pressure behavior; therefore, simulated solutions require adjustment to achieve accurate results.     

Simulating pavement structural condition using artificial neural networks

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.     

Local difference patterns for drunk person identification

In this work, facial thermal infrared images are employed for intoxicated person discrimination. Specifically, the region of the forehead of the face of the sober and the corresponding intoxicated person is used to test if the employed Local Difference Patterns (LDPs) constitute discriminative features. For an intoxicated person, vessels on the forehead become more active so that the intensity of the pixels in this region is affected accordingly. The LDPs employed ignore orientation of the pixels distribution and give emphasis on the first and second norms of the differences as well as the ordered values of the pixels in the employed kernels. The statistics of the LDPs for the drunk person are different from those of the sober one and accordingly drunkenness can be ascertained by comparing the thermal infrared image of the corresponding sober and intoxicated person. Six from the eight LDPs examined to be used as features for drunk identification were proved successful. Their classification success rate was over 73 and up to 85%. The proposed method can be incorporated into a non-invasive inspection commercial system to be used by the police as a first step for intoxicated person detection. Forty one participants in the experiment have contributed to the creation of the unique sober–drunk database which is available on the web and contains over 4.000 images.     

Using functional programming to recognize named structure in an optimization problem: Application to pooling

Branch‐and‐cut optimization solvers typically apply generic algorithms, e.g., cutting planes or primal heuristics, to expedite performance for many mathematical optimization problems. But solver software receives an input optimization problem as vectors of equations and constraints containing no structural information. This article proposes automatically detecting named special structure using the pattern matching features of functional programming. Specifically, we deduce the industrially‐relevant nonconvex nonlinear Pooling Problem within a mixed‐integer nonlinear optimization problem and show that we can uncover pooling structure in optimization problems which are not pooling problems. Previous work has shown that preprocessing heuristics can find network structures; we show that we can additionally detect nonlinear pooling patterns. Finding named structures allows us to apply, to generic optimization problems, cutting planes or primal heuristics developed for the named structure. To demonstrate the recognition algorithm, we use the recognized structure to apply primal heuristics to a test set of standard pooling problems. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3085–3095, 2016     

Modeling of fermentative hydrogen production from sweet sorghum extract based on modified ADM1

The Anaerobic digestion model 1 (ADM1) framework can be used to predict fermentative hydrogen production, since the latter is directly related to the acidogenic stage of the anaerobic digestion process. In this study, the ADM1 model framework was used to simulate and predict the process of fermentative hydrogen production from the extractable sugars of sweet sorghum biomass. Kinetic parameters for sugars’ consumption and yield coefficients of acetic, propionic and butyric acid production were estimated using the experimental data obtained from the steady states of a CSTR. Batch experiments were used for kinetic parameter validation. Since the ADM1 does not account for metabolic products such as lactic acid and ethanol that are crucial during the fermentative hydrogen production process, the structure of the model was modified to include lactate and ethanol among the metabolites and to improve the predictions. The modified ADM1 simulated satisfactorily batch experiments although further modifications could be made in order to further improve the predictions for the hydrogenogenic process.     

Solvothermal synthesis,nanostructural characterization and gas cryo-adsorption studies in a metal–organic framework (IRMOF-1) material

A nanoporous metal–organic framework material, exhibiting an IRMOF-1 type crystalline structure, was prepared by following a direct solvothermal synthesis approach, using zinc nitrate and terephthalic acid as precursors and dimethylformamide as solvent, combined with supercritical CO₂ activation and vacuum outgassing procedures. A series of advanced characterization methods were employed, including scanning electron microscopy, Fourier-transform infrared radiation spectroscopy and X-ray diffraction, in order to study the morphology, surface chemistry and structure of the IRMOF-1 material directly upon its synthesis. Porosity properties, such as Brunauer–Emmet–Teller (BET) specific area (～520 m²/g) and micropore volume (～0.2 cm³/g), were calculated for the activated sample based on N₂ gas sorption data collected at 77 K. The H₂ storage performance was preliminary assessed by low-pressure (0–1 bar) H₂ gas adsorption and desorption measurements at 77 K. The activated IRMOF-1 material of this study demonstrated a fully reversible H₂ sorption behavior combined with an adequate gravimetric H₂ uptake relative to its BET specific area, thus achieving a value of ～1 wt.% under close-to-atmospheric pressure conditions.     

Estimation of generalized entropies with sample spacing

In addition to the well-known Shannon entropy, generalized entropies, such as the Renyi and Tsallis entropies, are increasingly used in many applications. Entropies are computed by means of nonparametric kernel methods that are commonly used to estimate the density function of empirical data. Generalized entropy estimation techniques for one-dimensional data using sample spacings are proposed. By means of computational experiments, it is shown that these techniques are robust and accurate, compare favorably to the popular Parzen window method for estimating entropies, and, in many cases, require fewer computations than Parzen methods.