Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

This studies deals with investigated the potential of a Iraqi bentonite clay for the adsorption of bromo phenol red dye from contaminated water. Impulse adsorption experiments were performed. The contact time influence of initial dye concentration, temperature, pH, ionic strength, partical size adsorbent and adsorbent dosage on bromo phenol red adsorption are investigated in a series of batch adsorption experiments. Adsorption equilibrium data were analyzed and described by the Freundlich, Langmuir and temkin isotherms equations. Thermodynamic parameters inclusive the Gibbs free energy (∆G• ), enthalpy (∆H• ), and entropy (∆S• ), were also calculated. These parameters specified that adsorption of bromo phenol red onto bentonite

The removal of direct blue 71 dye from a prepared wastewater was studied employing batch electrocoagulation (EC) cell. The electrodes of aluminum were used. The influence of process variables which include initial pH (2.0-12.0), wastewater conductivity (0.8 -12.57) mS/cm , initial dye concentration (30 -210) mg/L, electrolysis time  (3-12) min, current density (10-50) mA/cm2   were studied in order to maximize the color removal from wastewater. Experimental results showed that the color removal yield increases with increasing pH until pH 6.0 after that it decreased with increasing pH. The color removal increased with increasing current density, wastewater conductivity, electrolysis time, and decreased with increasing the concen

Removal of Congo red, Rhodamine B, and Dispers Blue dyes from water solution have been achieved using Flint Clay as an adsorbent. The adsorption was studied as a function of contact time, adsorbent dose, pH, and temperature under batch adsorption technique. The equilibrium data fit with Langmuir, Freundlich and Toth models of adsorption and the linear regression coefficient R2 was used to elucidate the best fitting isotherm model. Different thermodynamic parameters, namely Gibb’s free energy, enthalpy and entropy of the on-going adsorption process have also been evaluated. Batch technique has been employed for the kinetic measurements and the adsorption of the three dyes follows a second order rate kinetics. The kinetic investigations al

The catalytic wet air oxidation (CWAO) of phenol has been studied in a trickle bed reactor

using  active  carbon  prepared  from  date  stones  as  catalyst  by  ferric  and  zinc  chloride activation (FAC and ZAC). The activated carbons were characterized by measuring their surface area and adsorption capacity besides conventional properties, and then checked for CWAO using a trickle bed reactor operating at different conditions (i.e. pH, gas flow rate, LHSV, temperature and oxygen partial pressure). The results showed that the active carbon (FAC and ZAC), without any active metal supported, gives the highest phenol conversion. The reaction network proposed account

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

This investigation was carried out to study the treatment and recycling of wastewater in the cotton textile industry for an effluent containing three dyes: direct blue, sulphur black and vat yellow. The reuse of such effluent can only be made possible by appropriate treatment method such as chemical coagulation. Ferrous and ferric sulphate with and without calcium hydroxide were employed in this study as the chemical coagulants.
The results showed that the percentage removal of direct blue ranged between 91.4 and 94 , for sulphur black ranged between 98.7 and 99.5 while for vat yellow it was between 97 and 99.

In this paper, two types of iron oxide nanomaterial (Fe3O4) and nanocomposite (T-Fe3O4) were created from the bio-waste mass of tangerine peel. These two materials were utilized for adsorption tests to remove cefixime (CFX) from an aqueous solution. Before the adsorption application, both adsorbents have been characterized by various characterizations such as XRD, FTIR, VSM, TEM, and FESEM. The mesoporous nano-crystalline structure of Fe3O4 and T-Fe3O4 nanocomposite with less than 100-nm diameter is confirmed. The adsorption of the obtained adsorbents was evaluated for CFX removal by adjusting several operation parameters to optimize the removal. The optimal conditions for CFX removal were found to be an initial concentration of 40 and 50 m

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data