Vipulanandan model for the rheological properties with ultimate shear stress of oil well cement modified with nanoclay

In this study, the effect of clay nanoparticles (NC) and temperature on the rheological properties with ultimate shear stress and weight loss of the oil well cement (class H) modified with NC was investigated. The NC content was varied between 0 and 1% by the weight of the cement. The total weight loss at 800 °C for the oil well cement decreased from 6.10% to 1.03%, a 83% reduction when the cement was mixed with 1% of NC. The results also showed that 1% of NC increased the rheological properties of the cement slurry. The NC modification increased the yield stress (τ_o) and plastic viscosity (PV) by 5%–65% and 3%–16% respectively based on the NC content and the temperature of the cement slurry. The shear thinning behavior of the cement slurry with and without NC has been quantified using the Vipulanandan rheological model and compared with the Herschel-Bulkley model. The Vipulanandan rheological model has a maximum shear stress limit were as the Herschel-Bulkley model did not have a limit on the maximum shear stress. Based on the Vipulanandan rheological model the maximum shear stress produced by the 0% and 1% of NC at the temperature of 25 °C were 102 Pa and 117 Pa respectively hence an increase of 15% in the ultimate shear stress due to the addition of NC. The addition of 1% of NC increased the compressive strength of the cement by 12% and 43% after 1 day and 28 days of curing respectively. The modulus of elasticity of the cement increased with the additional of 1% NC by 6% and 76% after 1 day and 28 days of curing respectively. Effects of NC content and the temperature on the model parameters have been quantified using a nonlinear model (NLM). The NLM quantified the effect of NC treatment on all the model parameters.     

Bond graph modeling,design and experimental validation of a photovoltaic/fuel cell/ electrolyzer/battery hybrid power system

This work presents a complete bond graph modeling of a hybrid photovoltaic-fuel cell-electrolyzer-battery system. These are multi-physics models that will take into account the influence of temperature on the electrochemical parameters. A bond graph modeling of the electrical dynamics of each source will be introduced. The bond graph models were developed to highlight the multi-physics aspect describing the interaction between hydraulic, thermal, electrochemical, thermodynamic, and electrical fields. This will involve using the most generic modeling approach possible for managing the energy flows of the system while taking into account the viability of the system. Another point treated in this work is to propose. In this work, a new strategy for the power flow management of the studied system has been proposed. This strategy aims to improve the overall efficiency of the studied system by optimizing the decisions made when starting and stopping the fuel cell and the electrolyzer. It was verified that the simulation results of the proposed system, when compared to simulation results presented in the literature, that the hydrogen demand is increased by an average of 8%. The developed management algorithm allows reducing the fuel cell degradation by 87% and the electrolyzer degradation by 65%. As for the operating time of the electrolyzer, an increment of 65% was achieved, thus improving the quality of the produced hydrogen. The Fuel Cell's running time has been decreased by 59%. With the ambition to validate the models proposed and the associated commands, the development of this study gave rise to the creation of an experimental platform. Using this high-performance experimental platform, experimental tests were carried out and the results obtained are compared with those obtained by simulation under the same metrological conditions.     

Adaptive neuro-fuzzy inference system (ANIFS) and artificial neural network (ANN) applied for indium (III) adsorption on carbonaceous materials

Abstract

In this paper, we present an initial study relating the adsorption of indium (III) onto carbonaceous materials, namely the activated carbon (AC), multiwalled carbon nanotubes functionalized with OH (MWCNT–OH), and the multiwalled carbon nanotubes functionalized with COOH (MWCNT–COOH). The main objective of this study is the development of the adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) for predicting the adsorption capacity in different operating conditions for different materials. Both models take into account the adsorbent type, adsorbent dosage (0.05, 0.25, 0.5, 1.0, 1.5, and 2.0?g L^?1), and the contact time (5, 20, 60, and 120?min) for predicting the adsorption capacity, which varied from 12.896 to 981.000?mg g^?1, a total record of 72 was used. Both modeling methodologies applied can represent the experimental data, taking into account the statistical values obtained. The ANFIS achieved the best performance when the hybrid method was selected, this leads into R of 0.9998, RMSE of 48,373 with 250 epochs. On the other hand, the ANN can represent the best performance when using the Levenberg–Marquardt algorithm, reaching an R of 0.9831, MSE of 0.0180 and 9 epochs. Considering the modeling and experimental aspects indicates that the increase of the adsorbent dosage diminished the adsorbent capacity. The increase of the contact time causes the effect to increase the adsorption capacity until its equilibrium. Lastly, it is possible to conclude that the MWCNT–COOH is the most suitable adsorbent to be used between the selected materials.     

Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.     

Multiscale insights into hydrogen charging behavior in metal hydride reactor packed with porous ZrCo particles

The primary purpose of this work is to develop a novel model for comprehensively investigating the hydrogen storage performance under the framework of diffusion of hydrogen atoms through hydride layer. The proposed model is constructed upon perfectly mathematical-physical equations, by taking into account complicated multi-scale and multi-physics coupling actions. Importantly, three-dimensional numerical simulations are performed to explore the coupling effects of micro diffusion, mesoscopic permeation, and macroscopic fluid flow. An analytical approach accounting for the characteristics of reaction bed, particle, and crystal grain is presented as well. In addition, a parametric analysis is conducted to reveal that the hydride particle dimension, particle porosity, grain size, and diffusion coefficient of reacted layer have a significant effect on overall hydrogen storage performance, highlighting that grain size and hydrogen diffusion coefficient are vital factors that need to be considered for material preparation and design.     

Design of geocell reinforced roads through fragility modeling

Several studies have confirmed the geocell reinforcement system as potential road material. However, there is a wide gap between the number of research studies evaluating the geocell in the laboratory and those dealing with road design methods using the geocell. Due to this gap, the geocell system has not reached its full potential in highways. The present study proposes fragility modeling to design low volume roads by considering the geocell reinforced layer's modulus. A predictive model was developed to estimate the geocell layer's modulus using laboratory and finite element analysis results. The results indicate that geocell reinforcement reduces the stresses on the underlying road layers. The developed fragility approach is then used to examine three road designs for Texas's low volume road involving different geocell reinforced layers. The obtained fragility curves indicate the reliability of each of the three road designs against the traffic load and can thereby assist decision-makers in selecting the optimum design. By designing geocell reinforced roads via fragility modeling, highway officials will be able to integrate any uncertainties in the design inputs and check designs against road performance criteria such as rutting and fatigue cracking, and against decision criteria such as cost, emissions, etc.     

Analysis of TBA‐Based ETBE Production by Means of an Optimization‐Based Process‐Synthesis Approach

The current article presents the extension of a recently developed optimization‐based approach to process synthesis for process intensification. It generates phenomena‐based flowsheet options using superstructure optimization and provides a dedicated translation into equipment‐based flowsheets. The considered case‐study illustrates the application of the method for the analysis of ethyl tert‐butyl ether production, based on the conversion of tert‐butyl alcohol, under consideration of variable configurations of a rector network and a newly introduced pervaporation‐based membrane‐reactor block.     

Degradation rate quantification of solid oxide fuel cell performance with and without Al2TiO5 addition

Degradation rates of electrical current during constant voltage operation of SOFCs with anodes made using NiO precursor powders from two different manufacturers with and without the addition of aluminum titanate (ALT) added by either mechanical mixing or anode infiltration have been quantified using a novel MATLAB algorithm. Because the algorithm has been used to quantify degradation rates for many different SOFC tests, it is thought that the method can be applied to most measured SOFC data to quantify the instantaneous cell degradation rate as a function of time for the entire SOFC performance measurement. Degradation rates determined at different times have been plotted against varying concentrations of ALT addition, facilitating the estimation of optimum ALT concentration for SOFC anodes made with NiO from a specific manufacturer. The algorithm used to determine degradation rates is available upon request to the corresponding author.     

Thermodynamic description of the Cu-Ge-Pb system: Experiment and modeling

Decades of scientific work dedicated to the investigation of phase diagrams gave significant benefit to industry and science. After all those years of phase diagram investigation still there is missing information about phase diagram of some ternary systems. One of those systems is Cu-Ge-Pb. It is known importance of Cu-based alloys and Ge-based alloys in electro industry. Since such combination is not tested before this work will provide information about phase diagram of ternary Cu-Ge-Pb system. In this work ternary Cu-Ge-Pb system has been tested experimentally and analytically by using Calphad model. Two isothermal sections at 600 and 400 °C and three vertical sections are experimentally tested and results were compared with calculated corresponding phase diagrams. None of the ternary compound and large solubility of third element in binary compound is not confirmed. Liquidus projection, invariant reaction and scheme of invariant reaction are presented. Scheil and Lever simulation of solid phases for Cu₈₀Ge₁₀Pb₁₀ alloy were calculated.     

Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map

Residential natural gas consumption depends on several factors. Available tools and methods to identify, categorize, and validate effective factors have some limitations, making consumption modeling more complex. Once a comprehensive model of effective consumption factors is developed for residential gas consumers, it can predict consumption. In addition, such a model could be used to verify the accuracy of measuring devices in order to reduce unaccounted for gas (UFG). The key factors affecting residential gas consumption were identified based on previous studies and their mutual effects were analyzed using a fuzzy cognitive mapping (FCM) method. The most significant factors and their effects on natural gas consumption in the residential sector were determined. In this study, for the first time, the expected consumption for each consumer was estimated using a consumption index. Generally, if the estimated consumption is significantly different from the amount recorded by the meter, it could suggest a potential source of UFG. The proposed method was applied to the data collected from the residential gas consumers of a small region in Iran (Dasht-e Arjan region, Fars province), and the results demonstrate the effectiveness of the proposed method.