Ability of indigenous Bacillus licheniformis and Bacillus subtilis in microbial enhanced oil recovery

Microbially produced lipopeptide have been isolated and studied for microbial enhanced oil recovery. About 60 gram positive bacteria isolated from soil contaminated with crude oil, near the crude oil storage tank in Tehran Refinery, Tehran, Iran. However, most of these studies have produced lipopeptide by one of the pure-culture microbes isolated in a laboratory. Among the isolates, heamolytic tests revealed two biosurfactant producers. The isolated strains were designated as C2, E1. By using morphological, biochemical and molecular biology tests (16 SrRNA), the strains identified as Bacillus licheniformis and Bacillus subtitlis, respectively. Emulsification activity and measurement of surface tension indicated that, the isolates were high producers of biosurfactant. The product of C2 and E1 is mainly lipopeptide. This product reduce surface tension from 65 to 30 mN/m. Emulsified activity of crude oil was 92% for C2 and 90 % in case of E1. This is the first report of indigenous Bacillus licheniformis and Bacillus subtilis from a soil contaminated with oil in an Iranian refinery with ability to produce biosurfactant.     

Assessment of slope stability in cohesive soils due to a rainfall

The primary focus in this work is on proposing a methodology for the assessment of stability of natural/engineered slopes in clayey soils subjected to water infiltration. In natural deposits of fine‐grained soils, the presence of water in the vicinity of minerals results in an interparticle bonding. This effect cannot be easily quantified as it involves complex chemical interactions at the micromechanical level. Here, the evolution of strength properties, including the apparent cohesion resulting from initial suction at the irreducible fluid saturation, is described by employing the framework of chemoplasticity. The paper provides first the formulation of the problem; this involves specification of the constitutive relation, development of an implicit return mapping scheme, and the outline of a coupled transient formulation. The framework is then applied to examine the stability of a slope subjected to a prolonged period of intensive rainfall. It is shown that the water infiltration may trigger the loss of stability resulting from the degradation of material properties. Copyright © 2013 John Wiley & Sons, Ltd.     

New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This...     

On modeling of discrete propagation of localized damage in cohesive‐frictional materials

In this paper, the problem of propagation of localized deformation associated with formation of macrocracks/shear bands is studied in both tensile and compressive regimes. The main focus here is on enhancement of the constitutive law with embedded discontinuity to provide a discrete representation of the localization phenomenon. This has been accomplished by revising the formulation and coupling it with the level‐set method for tracing the propagation path. Extensive numerical studies are conducted involving various fracture modes, ranging from brittle to frictional, and the results are compared with the experimental data as well as those obtained using XFEM methodology. Copyright © 2015 John Wiley & Sons, Ltd.     

Correction to: New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology -     

Machine learning for accelerating 2D flood models: Potential and challenges

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.     

Nonparametric kernel estimation of annual precipitation over Iran

In this paper, annual precipitation data sets from five old rain gauge stations (Bushehr, Isfahan, Meshed, Tehran, and Jask) in Iran were fitted to nonparametric kernel function by using rectangular, triangular, and Gaussian or normal as kernel functions. The smoothing parameter was calculated by four methods including rule of thumb, Adamowski criterion, least squares cross-validation, and Sheater and Jones plug-in. The Adamowski criterion showed a better performance compared to other methods due to goodness of fit tests. The results of these proposed nonparametric methods will be then compared to the results of the parametric density functions including normal, two and three parameter log-normal, two parameter gamma, Pearson and log-Pearson type 3, Gumbel or extreme value type 1 and also Fourier series method which were applied by a previous study for the same stations. It was concluded that the annual precipitation data were fitted to nonparametric methods better than parametric methods.