Electrical Conductivity Model Research Articles

Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling

This study investigated the impact of temperature and nanoparticle mixing ratio on the thermophysical properties of hybrid nanofluids (HNFs) made with graphene nanoplatelets (GNP) and iron oxide nanoparticles (Fe2O3). The results showed that increased temperature led to higher thermal conductivity (TC) and electrical conductivity (EC), and lower viscosity in HNFs. Higher GNP content relative to Fe2O3 also resulted in higher TC but lower EC and viscosity. Artificial neural network (ANN) and response surface methodology (RSM) were used to model and correlate the thermophysical properties of HNFs. The ANN models showed a high degree of correlation between predicted and actual values for all three properties (TC, EC, and viscosity). The optimal number of neurons varied for each property. For TC, the model with six neurons performed the best, while for viscosity, the model with ten neurons was optimal. The best ANN model for EC contained 18 neurons. The RSM results indicated that the 2-factor interaction term was the most significant factor for optimizing TC and EC; while, the linear term was most important for optimizing viscosity. The ANN models performed better than the RSM models for all properties. The findings provide insights into factors affecting the thermophysical properties of HNFs and can inform the development of more effective heat transfer fluids for industrial applications.

Uncertainty and sensitivity analysis of a travel‐time distribution‐based stream water electrical conductivity model

AbstractIn this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.

Precipitate-mediated enhancement of mechanical and electrical properties in HPTE-processed Al–Mg–Si alloy

In this study the effect of high-pressure torsion extrusion (HPTE) and artificial aging (AA) on the strength and electrical conductivity of an Al–Mg–Si alloy was investigated. The resulting material shows remarkable improvement in ultimate strength reaching 380 MPa and in electrical conductivity with 53 % IACS (International Annealed Copper Standard), after post-processing aging at 160 °C for 10 h. X-ray diffraction analysis and electron backscatter diffraction were employed to analyze the structure of the alloy caused HPTE followed by AA. Transmission electron microscopy (TEM) and energy-dispersive X-ray (EDX) spectroscopy were used to study the morphology, elemental composition, and crystal structure of the secondary phases. The study confirms that Guinier-Preston (GP) zones and nano-scale precipitates created during the deformation and subsequent heat treatment are evenly distributed inside the grains. Chemical composition analysis of the precipitates indicated the presence of one main phase based on the Mg/Si ratio coexisting with a low volume fraction of other phases in this Al grade. High-resolution TEM revealed precipitates either with a partially ordered structure or a fully crystalline structure. Strength and electrical conductivity modeling were carried out based on the microstructural characterization results. The findings of this study indicate that HPTE together with thermal treatment has the potential as new approach for processing of commercially significant aluminum alloys.

An electrical conduction model for calcium-bearing shaly sandstone based on the effective medium Hanai-Bruggeman theory

An electrical conduction model for calcium-bearing shaly sandstone based on the effective medium Hanai-Bruggeman theory

Theoretical and practical investigation of ion-ion association in electrolyte solutions.

In this study, we present a new equation of state for electrolyte solutions, integrating the statistical associating fluid theory for variable range interactions utilizing the generic Mie form and binding Debye-Hückel theories. This equation of state underscores the pivotal role of ion-ion association in determining the properties of electrolyte solutions. We propose a unified framework that simultaneously examines the thermodynamic properties of electrolyte solutions and their electrical conductivity, given the profound impact of ion pairing on this transport property. Using this equation of state, we predict the liquid density, mean ionic activity coefficient, and osmotic coefficient for binary NaCl, Na2SO4, and MgSO4 aqueous solutions at 298.15K. Additionally, we evaluate the molar conductivity of these systems by considering the fraction of free ions derived from our equation of state in conjunction with two advanced electrical conductivity models. Our results reveal that, while ion-ion association has a minimal influence on the modification of the predicted properties of sodium chloride solutions, their impact on sodium and magnesium sulfate solutions is considerably more noticeable.

Qualitative Model of Electrical Conductivity of Irradiated Semiconductor

There is constructed a qualitative model of the electrical conductivity of semiconductors irradiated with sufficiently high-energy particles. At certain conditions (irradiation temperature and dose, and subsequent thermal treatment), high-energy particles fluence, in addition to primary and secondary point radiation defects, forms a number of nano-sized disordered regions, highly conductive (“metallic”) compared to the semiconductor matrix. Their high total volume fraction can lead to the charge major carriers’ effective Hall mobility significantly exceeding that of the matrix. Due to elastic stresses created by these disordered inclusions, a high concentration of point radiation defects tends to form defective shells. In certain temperature ranges, such nanosized core-shell structures act as capacitors storing the electric charge sufficient for the Coulomb blockade of the major current carriers. Transformation of high-conductive inclusions into low-conductive (“dielectric”) ones manifests in a noticeable decrease in effective Hall mobility. The proposed model qualitatively explains all the experimental data available on single-crystalline n- and p-type silicon irradiated with high-energy electrons and protons and isochronously annealed.

The Subsurface Structure of the Kīlauea Caldera Before Its 2018 Collapse Inferred From Ground Magnetic, SP, and Temperatures Anomalies

AbstractThe 2018 crisis of Kīlauea volcano stands as a major event in its evolution with a large down‐rift effusive eruption that drained a shallow magma reservoir at the summit. The characterization of such active magmatic systems and associated hazardous events remains a necessity and a challenge. The summit area is hydrothermally active and strongly altered as indicated by geological mapping. A unique data set of geophysical measurements was collected around Halemaʻumaʻu crater before its collapse. Magnetic data are interpreted here in combination with geological information, temperature anomalies at the surface, self‐potential measurements, and a model of electrical conductivity. 3D forward modeling shows that the main magnetic dipole‐like anomaly observed around the crater is not only caused by the crater topography but suggests the presence of an important volume of weakly magnetic material beneath the crater, which may be caused by higher temperature and/or hydrothermal alteration. 3D inversion of the data allows us to explore the first order geometry of the magnetic structures. We complement this inversion with 2D forward modeling in order to refine the geometry of major structures. This study shows the presence of major geological structures in the 2018 collapsed area that may have been associated with mechanical weaknesses and could have played a role in the geometry of the collapse. Therefore, mapping magnetic anomalies and monitoring their temporal evolution are of great interest for constraining the nature and mechanical properties of the underlying formations and their temporal evolution in order to help predict future behavior.

On the Construction of a Gas-Dynamic Model of Electrical Conductivity of an Ionized Gas Based on a Supercomputer Simulation of Electron Kinetics

On the Construction of a Gas-Dynamic Model of Electrical Conductivity of an Ionized Gas Based on a Supercomputer Simulation of Electron Kinetics

Mapping saltwater intrusion via Electromagnetic Induction (EMI) for planning a Managed Aquifer Recharge (MAR) facility in Maltese Island

In coastal areas, saltwater intrusion causes a depletion of the resource by reducing potable and irrigation freshwater supplies and causing severe deterioration of groundwater quality. This trend is observed in Pwales Valley, in the North part of Malta where the management of water resources plays a crucial role for the environmental sustainability of the area, given the importance of intensive agricultural activity along this valley. In order to tackle such phenomenon, actions or adaptation measures against climate change are strongly required. For example, Managed Aquifer Recharge (MAR) is an increasingly important water management strategy to maintain, enhance and secure stressed groundwater systems and to protect and improve water quality. To accurately plan a Managed Aquifer Recharge scheme, it is crucial to define a hydrogeological model of the studied area, with the use of traditional hydrogeological measurements and innovative unconventional techniques. In recent years, Electromagnetic Induction measurements, based on induction of em fields, have been increasingly used for investigating the saltwater intrusion dynamics due to their high sensitivity to the salinity. In the study area of Pwales Valley, a Managed Aquifer Recharge scheme is being planned and, for this aim, a hydrogeological model has been developed through an Electromagnetic Induction survey. More than 20,000 apparent electrical conductivity (ECa) data were collected to generate a quasi 3D high-resolution model of electrical conductivity of the Pwales Valley. The results highlighted the spatial extension of the tongue-shape salt water intrusion from east to west along the valley, as well as some geological-hydrogeological peculiarities such as the thickness of the salt wedge and the irregular top surface of the bottom impermeable layer, otherwise undetectable with other direct techniques at the field scale resolution. The approach was confirmed to be a useful tool for an effective hydrogeological characterisation, essential for planning adaptation measures to a changing climate, such as the implementation of a Managed Aquifer Recharge scheme.

Comparison of multi-coil and multi-frequency frequency domain electromagnetic induction instruments

IntroductionCharacterization of the shallow subsurface in mountain catchments is important for understanding hydrological processes and soil formation. The depth to the soil/bedrock interface (e.g., the upper ~5 m) is of particular interest. Frequency domain electromagnetic induction (FDEM) methods are well suited for high productivity characterization for this target as they have short acquisition times and do not require direct coupling with the ground. Although traditionally used for revealing lateral electrical conductivity (EC) patterns, e.g., to produce maps of salinity or water content, FDEM inversion is increasingly used to produce depth-specific models of EC. These quantitative models can be used to inform several depth-specific properties relevant to hydrological modeling (e.g. depths to interfaces and soil water content).Material and methodsThere are a number of commercial FDEM instruments available; this work compares a multi-coil device (i.e., a single-frequency device with multiple receiver coils) and a multi-frequency device (i.e., a single receiver device with multiple frequencies) using the open-source software EMagPy. Firstly, the performance of both devices is assessed using synthetic modeling. Secondly, the analysis is applied to field data from an alpine catchment.ResultsBoth instruments retrieved a similar EC model in the synthetic and field cases. However, the multi-frequency instrument displayed shallower sensitivity patterns when operated above electrically conductive grounds (i.e., 150 mS/m) and therefore had a lower depth of investigation. From synthetic modeling, it also appears that the model convergence for the multi-frequency instrument is more sensitive to noise than the multi-coil instrument.ConclusionDespite these limitations, the multi-frequency instrument is smaller and more portable; consequently, it is easier to deploy in mountainous catchments.

Comparison of simulations of the gas conduction in HVDC GIL by application of nonlinear conductivity model and ion-drift-diffusion model

PurposeGas insulated systems, such as gas insulated lines (GIL), use insulating gas, mostly sulfur hexalfluoride (SF6), to enable a higher dielectric strength compared to e.g. air. However, under high voltage direct current conditions, charge accumulation and electric field stress may occur, which may lead to partial discharge or system failure. Therefore, numerical simulations are used to design the system and determine the electric field and charge distribution. Although the gas conduction shows a more complex current–voltage characteristic compared to solid insulation, the electric conductivity of the SF6 gas is set as constant in most works. The purpose of this study is to investigate different approaches to address the conduction in the gas properly for numerical simulations.Design/methodology/approachIn this work, two approaches are investigated to address the conduction in the insulating gas and are compared to each other. One method is an ion-drift-diffusion model, where the conduction in the gas is described by the ion motion in the SF6 gas. However, this method is computationally expensive. Alternatively, a less complex approach is an electro-thermal model with the application of an electric conductivity model for the SF6 gas. Measurements show that the electric conductivity in the SF6 gas has a nonlinear dependency on temperature, electric field and gas pressure. From these measurements, an electric conductivity model was developed. Both methods are compared by simulation results, where different parameters and conditions are considered, to investigate the potential of the electric conductivity model as a computationally less expensive alternative.FindingsThe simulation results of both simulation approaches show similar results, proving the electric conductivity for the SF6 gas as a valid alternative. Using the electro-thermal model approach with the application of the electric conductivity model enables a solution time up to six times faster compared to the ion-drift-diffusion model. The application of the model allows to examine the influence of different parameters such as temperature and gas pressure on the electric field distribution in the GIL, whereas the ion-drift-diffusion model enables to investigate the distribution of homo- and heteropolar charges in the insulation gas.Originality/valueThis work presents numerical simulation models for high voltage direct current GIL, where the conduction in the SF6 gas is described more precisely compared to a definition of a constant electric conductivity value for the insulation gas. The electric conductivity model for the SF6 gas allows for consideration of the current–voltage characteristics of the gas, is computationally less expensive compared to an ion-drift diffusion model and needs considerably less solution time.

Students’ Understanding of Microscopic Models of Electrical and Thermal Conductivity: Findings within the Development of a Multiple-Choice Concept Inventory

Solid-state physics has important applications in the development of modern technologies. Although solid-state concepts, such as electric and thermal conductivity, are included in the curricula at all educational levels, even university students have many incorrect ideas about them. The incorrect ideas identified in previous studies are mainly related to macroscopic aspects of solid-state physics. With the aim of gaining a more comprehensive insight into students’ understanding of solid-state physics, we have developed a multiple-choice concept inventory on microscopic models of electric and thermal conductivity of solids (METCS). The inventory has been field-tested with a sample consisting of 233 first-year medical faculty and engineering students from the University of Rijeka (Croatia). METCS proved to have good psychometric features and it helped to uncover many incorrect ideas that have not been reported in the earlier physics education literature. The findings from this study could be a good starting point for the development of evidence-based, university-level tutorials on microscopic models of electric and thermal conductivity.

Modeling pH changes and electrical conductivity in surface water as a result of mining activities

Purpose. To develop comprehensive models for predicting the pH and electrical conductivity of surface water in Maiganga coal mine and environs affected by mining activities. Methodology. The research utilizes a combination of in-situ measurement, laboratory analysis, modeling technique using Ansys Workbench and Linear Regression for predicting the content of pollutants. In-situ measurement/data collection in the upstream and downstream were carried out to evaluate the potential impact of mining activities on surface and ground water quality. Electrical conductivity and pH were measured on the samples that were collected using Oakton 5/6 pH meter and TDS/EC meter. Findings. According to the results, the regression statistics model of pH and electrical conductivity (EC) shows that the predicted values have a pH range of 4.7–7.05 and a mean pH value of 5.5. In contrast, while the EC ranges from 454.52 to 2,720.68 s/cm (EC) with a mean value of 905 µs/cm of the downstream flow which is completely dependent on the mine inlet (pH-in and EC-in). The findings show a direct correlation between surface water pH, electrical conductivity, and mining activities in the Maiganga coal mine area and their detrimental effects on the ecosystem and water quality. Originality. The results were obtained directly from the mine site during field visit and can be compared to data from active coal mine sites. Practical value. The detrimental effect of the results of mining activities can be controlled if monitoring sensors are introduced at mines’ effluent outlet to alert the mine management of possible danger in real time.

An Improved Rock Resistivity Model Based on Multi-Fractal Characterization Method for Sandstone Micro-Pore Structure Using Capillary Pressure

Micro-pore structures are an essential factor for the electrical properties of porous rock. Theoretical electrical conductivity models considering pore structure can highly improve the accuracy of reservoir estimation. In this study, a pore structure characterization method based on a multi-fractal theory using capillary pressure is developed. Next, a theoretical electrical conductivity equation is derived based on the new pore structure characterization method. Furthermore, a distinct interrelationship between fractal dimensions of capillary pressure curves (Dv) and of resistivity index curves (Dt and Dr) is obtained. The experimental data of 7 sandstone samples verify that the fitting result by the new pore structure characterization method is highly identical to the experimental capillary pressure curves, and the accuracy of the improved rock resistivity model is higher than the Archie model. In addition, capillary pressure curves can be directly converted to resistivity index curves according to the relationship model between fractal dimensions of capillary pressure curves (Dv) and resistivity index curves (Dt and Dr). This study provides new ideas to improve the accuracy of pore structure characterization and oil saturation calculation; it has good application prospects and guiding significance in reservoir evaluation and rock physical characteristics research.

GIS Interpolation and Mapping of Soil Physicochemical Properties in Deep Medium Black Soils of Established Citrus Orchards

Soil properties are an important factor for orchard establishment, precise nutrient management and sustainable production of fruit crops. Therefore, it is important to assess the spatial distribution of fundamental soil properties in well-established orchards. Hence an attempt has been made to assess the extent of soil properties and its spatial distribution in citrus orchards in medium black soils of Madhya Pradesh. The present study was conducted for the assessment of the spatial distribution of physicochemical properties viz. pH, electrical conductivity (EC) and soil organic carbon (SOC) of citrus orchards in medium-deep black soils of India. Results revealed that soil pH ranged from 6.83-8.84 (mean 7.80), soil EC varied from 0.07-0.34dS m-1 (mean 0.18 dS m-1) and soil organic carbon ranged from 0.13-0.89% (mean 0.47%) in 0-20 cm of surface soil layer. Geostatistical analysis showed that the slope of the prediction function of best-fit model (exponential) for soil pH, EC and SOC was 0.31, 0.22 and 0.77, respectively. The corresponding values of root mean square error (RMSE) were 0.35, 0.03, and 0.14. Interpolation of soil properties indicated that 89.2 % area had soil pH between 7.20 to 8.00, 83.4 % area had soil EC between 0.10 to 0.20 dS m-1, while>90 % area had SOC content ranged from 0.25 to 0.75%. Geo-statistical analysis revealed that spatial dependency was moderate for pH and strong spatial dependency was estimated for EC and SOC content. Based on RMSE and slope of prediction function, an exponential model was best-fit model in ordinary kriging for interpolation of measured soil properties.

Modeling Anisotropic Electrical Conductivity of Blood: Translating Microscale Effects of Red Blood Cell Motion into a Macroscale Property of Blood.

Cardiovascular diseases are a leading global cause of mortality. The current standard diagnostic methods, such as imaging and invasive procedures, are relatively expensive and partly connected with risks to the patient. Bioimpedance measurements hold the promise to offer rapid, safe, and low-cost alternative diagnostic methods. In the realm of cardiovascular diseases, bioimpedance methods rely on the changing electrical conductivity of blood, which depends on the local hemodynamics. However, the exact dependence of blood conductivity on the hemodynamic parameters is not yet fully understood, and the existing models for this dependence are limited to rather academic flow fields in straight pipes or channels. In this work, we suggest two closely connected anisotropic electrical conductivity models for blood in general three-dimensional flows, which consider the orientation and alignment of red blood cells (RBCs) in shear flows. In shear flows, RBCs adopt preferred orientations through a rotation of their membrane known as tank-treading motion. The two models are built on two different assumptions as to which hemodynamic characteristic determines the preferred orientation. The models are evaluated in two example simulations of blood flow. In a straight rigid vessel, the models coincide and are in accordance with experimental observations. In a simplified aorta geometry, the models yield different results. These differences are analyzed quantitatively, but a validation of the models with experiments is yet outstanding.

An advanced hybrid deep learning model for predicting total dissolved solids and electrical conductivity (EC) in coastal aquifers

For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO3, SO4, and Cl− to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore, the current developed hybrid model could be used by private and public water sectors for predicting TDS and EC for enhancing water quality in coastal aquifers.

A new strategy for prediction of water qualitative and quantitative parameters by deep learning-based models with determination of modelling uncertainties

ABSTRACT This study presents a new method based on three types of deep learning-based models (DLM) for estimation of water parameters. The DLM models were recurrent neural networks (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The study areas were the Colorado River basin in the United States and the Mighan Wetland in Iran. The electrical conductivity (EC), dissolved oxygen (DO), total dissolved solids (TDS), chloride ions (Cl), and river flow rate (debi) were simulated by the DLM models. The Wilson score (WS) uncertainty analysis results for Colorado modelling showed that LSTMdebi, RNNDO, and RNNEC were the best models in simulating due to having the lowest errors (Mean ei equal to 0.36, −1.50, and −0.59), respectively. Finally, the highest value of the R2 index, 0.998, was achieved by the LSTM model in modelling the debi parameter, and 0.996 in EC modelling, in the Mighan Wetland.

On the thermodynamic properties of hydrolysed urine: Experimental data, predictive and correlative models for the design of concentrative urine treatment technologies

Source-separated urine contains the potential for water, nutrient and energy recovery. During the recovery of such resources, urine treatment technologies undergo temporal changes in the thermodynamic properties of urine. To inform the design of thermal, membrane and electrochemical urine treatment processes, this study experimentally measured and modelled the thermophysical properties of synthetic hydrolysed urine (vapour pressure, osmotic pressure, density, and electrical conductivity) across temperature and concentration profiles using two approaches: predictive thermodynamic equations based on an ionic speciation model and correlative modelling. The vapour pressure and osmotic pressure were accurately predicted to 14 wt% for the predictive model, within an error of 0.30% and 0.46%, respectively, while the correlative model based on the experimental data from this study demonstrated an error of <1% for both parameters across the whole range of salt concentrations and temperatures (4.5 to 32.2 wt%; 333 to 373 K). The predictive and correlative models for density and electrical conductivity showed adequate agreement with the experimental data, where error did not exceed 2.12% and 1.2%, respectively, across the entire range of concentrations and temperatures.The value of the models for the development of sustainable urine treatment technologies was demonstrated by their application to determine the optimum heating surface area of a urine evaporator and the energy requirements of a reverse osmosis process.

Electrical conductivity and hopping conduction mechanism by VRH model in halide modified tellurite glasses

Electrical properties of halide doped tellurium glasses with composition 60TeO2-(25-x) Bi2O3-15B2O3-x ZnCl2, where x varies from 0 to 20 with step-size 5 has been studied in frequency range 10-1 Hz–105 Hz and temperature range of 200°C–300°C. The frequency-dependent conductivity was found to obey Almond-West's universal powers law and parameters such as frequency exponent (s), DC conductivity, and crossover frequency were obtained by fitting the ac conductivity experimental data with Almond-West's universal powers law. Based on estimated s values the electrical conduction model was depicted, and it was found that the correlated barrier hopping (CBH) model provides a pretty realistic depiction of the primary AC conduction mechanism. Further, the migratory enthalpy (Hm) and the enthalpy required to remove the cation from its initial position near a charge-compensating centre (Hf) were calculated. Further, Variable range hopping, or VRH, was found to be the process by which charges move between localized states.The electric modulus (M″) fitting with the Kohlrausch-Williams-Watts (KWW) function revealed non-Debye-type relaxation in the examined glass samples. Activation energy estimated from conductivity (1.13–1.20 eV), and electric modulus (1.13–1.22 eV) analysis are in good agreement indicating that polarons had to overcome a constant energy barrier throughout both the relaxation and conduction phases. One master curve was produced for all compositions and temperatures as a result of an impressive convergence in the scaling spectra of the AC conductivity and electric modulus (M′ and M″). Therefore, it was concluded that neither temperature nor composition impacted the conduction or relaxation mechanisms seen in the glass samples under study. The dielectric properties studies suggested non-debye type behavior.