Estimation of void fraction for homogenous regime of two-phase flows in unstable operational conditions using gamma-ray and neural networks

 Almost all the multi-phase flow meters (MPFMs) using gamma-ray attenuation, are calibrated for liquid and gas phases with constant density and pressure. When operational conditions such as temperature and pressure change in pipelines, the radiation-based multi-phase flowmeters would measure the flow rate with error. Therefore, performance of MPFMs would be improved by eliminating any dependency on the fluid properties such as density. In this work, a method based on dual modality densitometry combined with Artificial Neural Network (ANN) is proposed in order to estimate the void fraction in homogenous regime of gas-liquid two-phase flows in unstable operational conditions (changeable temperature and pressure) in oil industry. An experimental setup was implemented to generate the optimum required input data for training the network. ANNs were trained on the registered counts of the transmission and scattering detectors in various liquid phase densities and void fractions. Void fractions were predicted by ANNs with mean relative error of less than 0.78% in density variations range of 0.735 up to 0.98 g/cm

Simulation of water movement and its distribution in a soil column under a water source using pore - scale network modelling

The relationship between capillary pressure and saturation has a remarkable value in investigating interactions of two immiscible fluids in porous media. Pore network models, as predictors of fluids movement in porous media, can play undeniable role in determining the mentioned relationship. In the current study, the results of numerical pore network modelling, which represents pore structure of a real porous medium with networks of pore bodies connected with pore throats, are used for computing the macroscopic relationship between capillary pressure and saturation. A notable volume of soil is influenced by water source and according to the results of previous studies, it seems practically impossible to simulate water movement in macro scale dimensions by use of pore scale models. Meanwhile a suitable solution was found in this study, by considering a thin vertical soil column under the water source which was divided to smaller volumes as cells, by horizontal crossings. Each cell was considered as a pore network unit, so the soil column was consisted of series of pore network units which were vertically jointed to each other. The moisture distribution and also wetting front movement in the column were determined by application of pore network model, using the dynamic update saturation solution method. The solution was conducted between each pair of consequent cells in an alternative manner within a time step since arrival time of the water from an upper cell to the lower one. Moreover, for evaluation of the model ability, soil moisture profiles in a sandy soil of an experimental tank under the water source were studied. Comparison of the simulation and observation data confirmed the high ability of the column pore network model in prediction of the moisture distribution and wetting front movement in a soil column

Simulation of water movement and its distribution in a soil column under a water source using pore - scale network modelling

The relationship between capillary pressure and saturation has a remarkable value in investigating interactions of two immiscible fluids in porous media. Pore network models, as predictors of fluids movement in porous media, can play undeniable role in determining the mentioned relationship. In the current study, the results of numerical pore network modelling, which represents pore structure of a real porous medium with networks of pore bodies connected with pore throats, are used for computing the macroscopic relationship between capillary pressure and saturation. A notable volume of soil is influenced by water source and according to the results of previous studies, it seems practically impossible to simulate water movement in macro scale dimensions by use of pore scale models. Meanwhile a suitable solution was found in this study, by considering a thin vertical soil column under the water source which was divided to smaller volumes as cells, by horizontal crossings. Each cell was considered as a pore network unit, so the soil column was consisted of series of pore network units which were vertically jointed to each other. The moisture distribution and also wetting front movement in the column were determined by application of pore network model, using the dynamic update saturation solution method. The solution was conducted between each pair of consequent cells in an alternative manner within a time step since arrival time of the water from an upper cell to the lower one. Moreover, for evaluation of the model ability, soil moisture profiles in a sandy soil of an experimental tank under the water source were studied. Comparison of the simulation and observation data confirmed the high ability of the column pore network model in prediction of the moisture distribution and wetting front movement in a soil column

Herbicidal Activity of Coumarin When Applied as a Pre-plant Incorporated into Soil

<p class="Continutabstract">Due to having a short half-life and novel site of action, the herbicidal potential of natural compounds are lionized. Coumarin is a secondary metabolite from <em>Lavandula</em> sp., family Lamiacae. The impact of eight concentrations of coumarin (0, 100, 200, 400, 800, 1600, 3200 and 6400 ppm) were separately used as a pre-plant incorporated into soil on six plant species under greenhouse conditions. Generally, coumarin had phytotoxic effect against all plant species. The phytotoxic effect was concentration-dependent. The high concentrations could inhibit the emergence of seedlings (probably by stopping germination of seeds). Based on ED₅₀ parameter, the ranking of plant species for tolerance to coumarin was <em>S. halepense</em> &gt; <em>Z. mays</em> &gt; <em>C. album</em> &gt; <em>A. retroflexus</em> &gt; <em>E. cruss-gali</em> &gt; <em>P. oleracea</em>. Based on selectivity index, coumarin at a concentration of 365.69 ppm can control <em>P. oleracea </em>without damaging <em>Z. mays</em>, whereas any concentration it cannot control other weeds without damaging <em>Z. mays</em>.</p

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet&ndash;GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash&ndash;Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively

Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products

Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources