Developing a simple-to-use predictive model for prediction of hydrate formation temperature

Hydrate formation may be a common occurrence during oil and gas drilling and production operation when temperature of these solid crystalline compounds that formed in the presence of free water decreases at elevated pressure. Also, they have often been found responsible for operating difficulties at wellheads, pipelines and other processing equipment. Nowadays, because of the importance of predicting hydrate formation condition, different accurate methods have been used. Besides the experiential correlations that are common for predicting, the developments in the field of modelling led to the use of different methods in a thermodynamic way. In fact, because of the risk of experimental uncertainties and to remove the need for intricate analytic equations and empirical correlations, the computational intelligence model, which result in the lowest error and based on experimental data, is strongly proposed, in attempts to solve complex industrial problems. In this article, in order to predict gas hydrate formation condition, two smart techniques are established based on feed-forward neural network (artificial neural network (ANN)) which is optimised by imperialist competitive algorithm (ICA). The ICA-ANN model is conducted utilising the empirical data released in the literature and finally the performance of ICA-ANN model is compared with the conventional ANN model. Furthermore, they have been compared with an accurate thermodynamic model at different operating conditions. The outcomes, contrary to expectations, establish that the ICA-ANN model has poor performance when compared with the ANN.     

Effect of gas phase oxidation on the structure and intertube adhesion force of a brush-like assembly of carbon nanotubes

Brush-like assemblies of carbon nanotubes produced using chemical vapor deposition were oxidized in stationary air in different oxidation conditions. Effects of oxidation on their structural properties were examined using microscopy and spectroscopy to evaluate changes in mass, CNT diameter, and the amount of disorder in samples. Separation force spectroscopy was also used, indicating a 50% increase in the adhesion force between CNTs in a mild oxidation regime, and indicating a sharp decrease in harsh oxidation conditions. By analyzing the relation between structural and chemical changes of CNT surfaces, we explored reasons for such trends in the forces interacting between CNTs.     

Amine and thiol functionalization of SBA-15 nanoparticles for highly efficient adsorption of sulforaphane

This study presents a novel material for highly efficient adsorption of sulforaphane based on SBA-15. The SBA-15 nanoparticles were synthesized using natural silica by hydrothermal method and functionalized with thiol and amine groups by the post-synthesis grafting procedure. All adsorbents were characterized by various techniques including BET, LAXRD, FT-IR, FE-SEM, and CHNS. The batch adsorption experiments revealed that the adsorption efficiency of SBA-15 for sulforaphane can be mainly influenced by the introduction of functional groups and the best adsorption performance was observed for amine-functionalized SBA-15 (SBA-15-NH₂) compared to pure SBA-15 and thiol-functionalized SBA-15 (SBA-15-SH) with the highest adsorption efficiency of about 97%. Preliminary adsorption studies of sulforaphane onto SBA-15-NH₂ were performed in aqueous solutions with different pH values and various organic solvents. Also, the effects of adsorbent dosage, contact time, and initial sulforaphane concentration on sulforaphane adsorption using SBA-15-NH₂ were studied. Langmuir and Redlich-Peterson isotherm models were the best-fitting models for the experimental data, followed by Temkin, Freundlich, and Dubinin-Radushkevich models. The adsorption process could be well described by the pseudo-first-order kinetic model based on kinetic parameters, correlation coefficient, and error functions. Meanwhile, the intra-particle diffusion model showed a multi-linear plot with three steps demonstrating slow diffusion into mesopores as a rate-limiting step.     

Isotropic compression behavior of granular assembly with non-spherical particles by X-ray micro-computed tomography and discrete element modeling

The particle morphological properties, such as sphericity, concavity and convexity, of a granular assembly significantly affect its macroscopic and microscopic compressive behaviors under isotropic loading condition. However, limited studies on investigating the microscopic behavior of the granular assembly with real particle shapes under isotropic compression were reported. In this study, X-ray computed tomography(μCT) and discrete element modeling(DEM) were utilized to investigate isotropic compression behavior of the granular assembly with regard to the particle morphological properties,such as particle sphericity, concavity and interparticle frictions. The μCT was first used to extract the particle morphological parameters and then the DEM was utilized to numerically investigate the influences of the particle morphological properties on the isotropic compression behavior. The image reconstruction from μCT images indicated that the presented particle quantification algorithm was robust, and the presented microscopic analysis via the DEM simulation demonstrated that the particle surface concavity significantly affected the isotropic compression behavior. The observations of the particle connectivity and local void ratio distribution also provided insights into the granular assembly under isotropic compression. Results found that the particle concavity and interparticle friction influenced the most of the isotropic compression behavior of the granular assemblies.     

Effect of fiber hybridization on energy absorption and synergy in concrete

In the present study, steel and polypropylene (PP) fibers have been utilized with the intent of obtaining hybrid fiber-reinforced concrete (HFRC) with desirable mechanical properties. An attempt has been made to scrutinize the properties of HFRC with the main concentration being on energy absorption characteristics of concrete and the efficacy of fiber hybridization in producing synergy. Accordingly, a total of 180 specimens, representing 20 different mixtures have been cast and evaluated through compressive, split tensile, and flexural tests. The relevant flexural toughness of the specimens was calculated using ASTM C1018, ASTM C1609, JSCE, and PCS methods, and the effectiveness of these methods was evaluated based on the experimental results. It was observed that steel fibers are more effective in the improvement of flexural toughness in the presence of PP fibers. Furthermore, synergy associated with the combination of fibers at different stages of deflection of the beam specimens was observed and analyzed.     

Distributed lifetime optimization in wireless sensor networks using alternating direction method of multipliers

Due to the limited energy of sensor nodes in wireless sensor networks, extending the network lifetime is a major challenge that can be formulated as an optimization problem. In this paper, we propose a distributed iterative algorithm based on alternating direction method of multipliers with the aim of maximizing sensor network lifetime. The features of this algorithm are the use of local information, low overhead of message passing, low computational complexity, fast convergence, and, consequently, reduced energy consumption. In this study, we present the convergence results and the number of iterations required to achieve the stopping criterion. Furthermore, the impact of problem size (number of sensor nodes) on the solution and constraints violation is studied, and, finally, the proposed algorithm is compared with one of the well‐known subgradient‐based algorithms.     

Sufficient conditions for the convergence of a class of nonlinear distributed consensus algorithms

This paper is concerned with the convergence of a class of continuous-time nonlinear consensus algorithms for single integrator agents. In the consensus algorithms studied here, the control input of each agent is assumed to be a state-dependent combination of the relative positions of its neighbors in the information flow graph. Using a novel approach based on the smallest order of the nonzero derivative, it is shown that under some mild conditions the convex hull of the agents has a contracting property. A set-valued LaSalle-like approach is subsequently employed to show the convergence of the agents to a common point. The results are shown to be more general than the ones reported in the literature in some cases. An illustrative example demonstrates how the proposed convergence conditions can be verified.