International Journal of Engineering
Volume:33 Issue: 12, Dec 2020

Alkanolamines are used to remove acidic gases such as CO2 and H2S from natural gas. In this study, thermodynamic modeling of the binary component CO2+MDEA, three component MDEA+H2O+CO2 and the quaternary MDEA+AEEA+H2O+CO2 systems were developed using an additional Gibbs argillic model for the first time in the modeling of CO2 solubility in different solutions. The appropriate model was considered using the assumption of an entirely molecular system without any occurrence of chemical reactions and saturated gas phase from the CO2 gas. The nonelectrolyte Wilson nonrandom factor (N-Wilson-NRF) model and the activity coefficient method (γ_φ Aproach) were used to calculate solubility of CO2. The two-component water- CO2 model was modeled and the results were obtained by the accuracy of 1.38 of experimental results. In a three-component, water-CO2-MDEA system with the amount of 6.913, the optimization was developed. The quaternary water-CO2-MDEA-AEEA system was optimized with an overall approximation of 19.537 for all experiment data.

In textile and garment industry, the physiological comfort of fabric as one of the important parameters, can be improved by the fabric finishing treatment. Nevertheless, the toxic chemicals produced in this process leads to the pollution of the environment. Therefore, this study aims to improve the physiological comfort of the cotton fabric without applying the finishing process as green technology. Accordingly, air permeability and moisture transfer as two important parameters of the fabric physiological comfort are evaluated with the structural parameters of the cotton yarn using experimental and theoretical procedures. For theoretical evaluations, a novel neuro-fuzzy network (ANFIS) is proposed and used for modelling and estimation. The structural parameters of yarn are the yarn linear density, yarn twist and fineness of fibers, which are defined as inputs and air permeability and moisture transfer of the cotton samples are considered as the outputs of developed ANFIS model. According to the experimental and modeling results, the fiber fineness, yarn linear density (Ne) and yarn twist have the same effect on the output parameters. It is also found that both parameters of the physiological comfort sensory can be improved effectively without finishing process. Simulation results show the novel proposed ANFIS that has high learning capability, fast convergence and accuracy greater than 99% and negligible error value smaller than of 1% can be reasonably used in textile industry. In addition, for the winter garments, the optimum points of turns per meter (T.P.M) coefficient, English count of yarn, and fibers fineness are 4.5, 25 and 3, respectively.

In the process of extracting oil and gas from hydrocarbon reservoirs, the formation of depositions inside pipes, fittings, and storage tanks, not only accelerates corrosion but also reduces a significant volume of operating capacities. The most critical step in solving the problem of deposition formation is their early and timely detection. In industries, internal surfaces of the pipeline are usually inspected by nondestructive testing (NDT) methods. The detection of depositions should operationally be difficult if there were special conditions for accessing the back of pipelines. Therefore, a suitable method is encouraged to detect deposition in the pipes and tubes when one side or a small part of them is accessible. In this paper, the Monte Carlo simulation tool was applied to use backscatter radiography (as an NDT inspection technique) for in-situ detection of depositions inside the metallic pipelines. In fact, the simulation process shows the correctness and efficiency of the backscatter radiography technique. It would determine some significant factors such as photon energy, angle of irradiation, or location of detectors which affect the design before experiment. The results showed that backscatter radiography as a viable technique could properly detect the location of depositions inside the pipes.

Severe environmental conditions in many parts of Iran could adversely affect infrastructures, especially bridge piers. This research evaluates the efficiency of fiber-reinforced polymer-modified self-compacting concrete in patch repair of bridge piers. The efficiency of this material for repair has been verified using ASTM C 928 and a novel testing method (patch test) that uses a cylindrical compression test simulating indirect force transfer from old (existing) concrete to new (patch repaired) concrete. To investigate the efficiency of self-compacting concrete in patch repair and derive the correlation between bond strength and patch strength, two sets of specimens have been included in the experimental program. These include 24 and 27 specimens which are prepared for patch and slant shear tests, respectively. Test results show significant improvement in strength due to use of polymer modified and fiber reinforced self-compacting concrete in slant shear tests, where strength enhancement as much as 50% (compared to undamaged specimens) was observed. Meanwhile, in the patch tests, repaired specimens are only able to barely exceed the original strength of undamaged specimens. In the slant shear tests, the use of polymer is very effective in increasing bond strength and using fibers reduces observed variation in the strength of repaired specimens. Considering force path in the patch repair and regardless of materials used for repair, the results show that judging the efficiency of the repair method based on the slant shear test, as proposed by ASTM C 928, in the case of patch repaired elements could be misleading.

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant colony optimization algorithm (ACO) is based on collective intelligence inspired by ants' behavior in finding the best path in search of food. In this paper, the ACO algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constant values for the parameters of the algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy ant colony optimization algorithm is applied on several images to illustrate the performance of the proposed algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quantitatively by the investigation of the time and entropy of conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.

Organizations can enhance the speed of well-informed decision-making by correctly understanding and using data. Since there is a tremendous gap between the speed of data processing and data generation in the world, exploring data mining in the digital world becomes inevitable. In the Persian language, similar to other languages, with the expansion of communications through social networks, the spelling of words has become abridged and the engagement of foreign loan words and emoticons has been increasing on a daily basis. Given the richness of Persian and its typographical-grammatical similarities to Arabic, research in Persian can be applied to other akin languages as well.  In this regard, the current study deals with data mining of Persian non-standard sentences in order to find the function of each word in the sentence. The volume of computation might be limited in traditional methods of natural language processing for each factor contributing to functions. That is because the minimum number of computations is (5 × number of words 9) + (5 × number of words 15). Therefore, this study adopted the Gated Recurrent Unit (GRU) method to process such computations. The newly proposed method reinforces the results of word function identification by using two categories of "independent" and "dependent" Persian language functions as well as five factors contributing to the functions of words in sentences as five output gates. Meanwhile, the values of the training tables in this method are fuzzy, where the center-of-gravity fuzzy method is adopted to decide on the fuzzy values as well as to reduce the complexity and ambiguity of such computations on the probability of each event occurring. Therefore, the new method is briefly called "fuzzy GRU". The results show that the proposed algorithm achieves 80 % reduction in the amount of calculations per gate of updates and reinforcement is approximately 2 % up from 67 % in standard sentences to 69 % of the non-standard sentences.

Resampling is a critical step in Particle Filter (PF) because of particle degeneracy and impoverishment problems. Independent Metropolis Hasting (IMH) resampling algorithm is a robust and high-speed method that can be used as the resampling step in PF. In this paper, a new algorithm based on IMH resampling is first proposed. The proposed algorithm classifies the particles before entering to the resampling module. The classification causes those essential particles are only routed to the IMH resampler. Then we propose a distributed architecture to reduce the execution time and high-speed processing for resampling. Simulation results for tracking a signal indicate that the PF with the proposed resampling architecture has acceptable tracking performance in comparison to other resampling methods. The PF architecture with the novel Improved IMH (IIMH) resampling algorithm has 33% more speed than the best-reported method in PF. Also, the proposed distributed PF architecture achieve 79% more speed compared with the best-reported method in PF. FPGA-based implementation results indicate that the utilization of the proposed IIMH resampling algorithm in PF and also distributed architecture lead to hardware resource and area usage reduction.

In a three-phase distribution system, due to unequal distribution of single-phase loads, load diversities, the different consumption patterns, and growing penetration of renewable energy resources in smart grids, the problem of unbalanced power flow becomes more challenging. In this paper, we propose a new innovative phase imbalance mitigation (PIM) scheme performed by smart meters. With aid of the proposed optimal phase assignment for 3-phase power distribution input feeders known as phase rearrangement (PR), Electrical storages (ES), and the Renewable energy sources (RES), smart meter owners are inspired to assist the distribution system operator (DSO) in diminishing the phase imbalance. This is achieved by employing a proposed connection point assignment system which has the flexibility of selecting the power input among the three phases and management of ESs and RESs.  We model this problem into a mixed integer linear program, where smart meter owners minimize their electricity bill. Simulation results confirm the proposed approach and show smart meter owners will save on their electricity bill and the DSO will get benefit by improving the power quality of the grid and significant decrements of the power flow imbalance.

The pandemic coronavirus diseases 2019 are generating different data sets in different regions of the world. The data sets are observed to be available in geographically separated medical entities. However, the demand for accessing and reliable delivery of such datasets through a web-based module is increasing gradually. In this work, we propose a novel cycle of reliability evaluation model for deployment of Software as a Service-based prototype for the coronavirus disease data processing system. We call it as PwCOV. The prototype generates clinical remarks through the paradigm of service-oriented computing, cluster-based load balancing web servers, and loosely coupled software principles. The applicability of PwCOV for processing isolated disease datasets is discussed against different stress of set of user entities. The validity and applicability of the proposed model are evaluated through statistical analysis. The reliability of the PwCOV is observed by evaluating the recorded status of the business logic execution, failure count and failure rate. The study reveals that the PwCOV is affective for processing disease data set for a collaborative set of tenants. A novel methodology is designed for the deployment of software as a service for the COVID-19 data processing system using a load balancing cluster base web server, where the roles of service-oriented computing are segregated among different layers. The limitation of such deployment is also discussed for multi-tenant environment.

Given that disasters are unavoidable, and many people are suffering from them each year, we should manage the emergencies and plan for them well to reduce mortality and financial losses. One of the measures that organizations must take after the disaster is the assessment of the conditions and needs of the people. We consider some characteristics for sites and roads and two teams for assessment as well as the uncertain assessment time to modeling. A multi-objective model is proposed in this study. The first objective function maximizes the gain from the assessment of areas and roads. The second and third objective functions maximize total coverage at damaged areas and roads. We use the LP-metric technique to solve small size problems in the GAMS software and the Grasshopper Optimization Algorithm (GOA) as a Meta-heuristic algorithm to solve a case study.  Numerical results are presented to prove the credibility and efficiency of our model.

Redundancy technique is used to improve performance and achieving to increase the lifetime of a system. Nowadays, the redundancy method is applied in many industries. One of the common methods of redundancy is its utilization in the switching systems. In switching systems, one or more components are considered active mode and the others in the standby state to be used by switch if necessary. In order to be fully utilized all the components in the redundant device, the switch unit must perform its function, such as switching, perfectly. Successful coverage by switch unit is expressed with a probability. In this paper, a new approach to the likelihood of switch success is proposed, and showing that as increases switching in the system, the efficiency and performance of the switch gradually decreases. The analysis of this method was based on the stress-strength method. Finally, a few numerical examples for the validation of results were applied.

This study focuses on the effect of friction pressure on the welding joint strength of AISI 316. Single factor method was used to evaluate the influence of friction pressure, whilst the other conditions kept constant. The experimental data were achieved by temperature measurement using infrared thermometer and thermometer by touch, where hardness Hv10 and micro-hardness Hv0.1 realized along the axial direction, tensile test specimen with 8 mm effective diameter, scanning electronic microscopy (SEM) to observe tensile fracture surface and x-ray diffraction (XRD) to analyze the concentration of gamma iron. The results by high friction pressure provide increased temperature during friction and forging phase, elevated hardness and micro-hardness values at the welding center, improved ductility and ultimate tensile strength (UTS). Whilst the central region of tensile fracture seemed most ductile mode and presence of micro-porosities with different forms and dimensions, hence concentration of face centered cubic (FCC) structure of gamma iron clearly revealed at level of 111.

Today, the charpy impact test is required as a general quality control test in various industries. Several industrial standards have been formulated to perform the test accurately. It is important to determine the dynamic fracture energy in the charpy impact test and its relation to the fracture toughness through semi-empirical equations. In the present study, the charpy impact test on AZ31 magnesium alloy with standard ASTM E23 sample size is measured by the effect of groove depth, temperature and angle of groove on fracture energy. Taguchi and L18 arrays have been used to design the experiments and obtain the optimal state according to the number of factors studied. The effect of each input variable on the target parameter was analyzed by using ANOVA and the values of input parameters were extracted to maximize the amount of fracture energy by signal to noise method. The results showed that the groove depth has the greatest effect on the fracture energy and decreased with increasing groove depth. Also the best combination to maximize fracture energy was obtained in the non-grooved sample at -10 °C with a groove angle of 60 °.

Many legged robots have been designed and built by universities, research institutes and industry; however, few investigations regard energy consumption as a crucial design criterion. This paper presents a novel configuration for legged robots to reduce the energy consumption. The proposed leg can be either used as a single leg or easily attached to bodies with four, six and eight legs. This mechanism is a parallel four-bar linkage equipped with one active and four passive joints. In fact, the usage of the passive elements leads to simple feed-forward control paradigms. Moreover, another distinctive feature of this design is the arrangement of one-way clutches and flat springs to store the potential energy for utilizing it in the next step. A locomotion prototype of the proposed mechanical structure is built and its simulation is also presented in this paper. Comparing the results with other structures demonstrates the superiority and efficiency of this work regarding energy consumption problem.

Equal channel angular pressing (ECAP) process of AA7075 billet with the copper casing is comprehensively investigated. Firstly, ECAP process is simulated based on finite element method (FEM) in ABAQUS software and then is verified in comparison to the experimental data. The design of experiments using response surface methodology (RSM) is performed in order to investigate the processing parameters. The main effect of four considered parameters (channel angle, corner angle, friction coefficient and thickness of casing) on the maximum required force and strain was studied. Also, the regression models for estimating the maximum forming force and strain are represented in high reliability using analysis of variance (ANOVA). The results indicated that channel angle by 93.5% of contribution is the most effective parameter on the required forming force. It is concluded that the thickness of copper casing does not affect the forming force. Also, all terms of the presented regression model are effective on the strain value, according to the obtained results. Based on ANOVA results, channel and corner angel are the most effective parameters on the strain by 80 and 16% of the contribution, respectively. Also, the friction coefficient and the thickness of copper casing have almost no significant effects on the strain.

Currently, non-destructive testing is widely used to investigate various mechanical and structural properties of materials. In the present study, non-destructive ultrasonic testing was applied to study the relationship between the tensile strength value and the velocity of longitudinal ultrasonic waves. For this purpose, fourteen specimens of composites with different formulations were prepared. The tensile strength of the composites and the velocity of longitudinal ultrasonic waves inside them was measured. The relevance vector machine regression analysis, as a new methodology in supervised machine learning, was used to define a mathematical expression for the functional relationship between the tensile strength and the velocity of longitudinal ultrasonic waves. The accuracy of the mathematical expression was tested based on standard statistical indices, which proved the expression to be an efficient model. Based on these results, the developed model has the capability of being used for the online measurement of the tensile strength of rubber with the proposed formulation in the rubber industry.

Frost formation is a renowned phenomenon in HVAC, aeronautical and refrigeration industries. In this paper, numerical modeling and parametric study of the frost formation in the interrupted Micro Channels Heat sinks (MCHS) is investigated considering microfluidic effects in slip flow regime. For numerical modeling, basic equations of humid air and frost including continuum, momentum, energy and phase change mechanism are numerically solved and results are compared with reported data. Knudsen number (Kn) is changed so that slip flow regime requirement is accomplished. This requirement is also considered for setting boundary conditions. The effect of different parameters like cold surface temperature, time and Kn are studied on the frost formation and details of the flow field. Results revealed that with an increase in time and a decrease in Kn and cold surface temperature, weight and thickness of the frost increase. Moreover, with thicker frost maximum flow velocity rises in the microchannel. The details of frost formation and flow field, revealed by the numerical results can remarkably assist designing interrupted microchannel.

The flowforming process is a chipless metal forming process that is used to produce precise thin walled tubes. Manufacturing of internal gears using flowforming process is a difficult-to-achieve, but very interesting process in which the gear may be produced without the need for high forming forces and r high tooling cost. In this study, manufacturing of internal gears using flowforming process is studied. The process has been numerically analyzed and simulated. The plastic behavior of the material, and friction conditions were determined using tensile and friction tests, respectively. Several controlled test were performed to evaluate the validity of simulation results. A comparison of simulation and experimental results indicates very good agreement. Once the simulation is verified, the effects of roller diameter, thickness reduction percentage, feed rate and attack angle on tooth height were obtained using design of experiments (DOE) procedure. According to DOE results, attack angle (α), thickness reduction percentage (T), interaction between roller diameter and attack angle (D×α), and interaction between roller diameter and feed rate (D×f) are the most significant parameters affecting the tooth height. The tooth height increases with increasing the roller diameter and thickness reduction, but decreases with increasing the feed rate and attack angle.

Wire and tube heat exchanger has been utilized in refrigerators whose cooling performance depends on how much the wire are releasing heat. Wire efficiency is an important factor of the performance. The woven matrix is a new design of wire configuration on wire and tube heat exchanger. This research focused on optimization design of woven matix by varying wire pitch (pw 5,7,9 mm) and three inlet massflows with controlling the hot fluid temperature at 353K.  Computational Fluid Dynamic Simulation is used to determine heat transfer distribution of fluid in tube. The validation was conducted experimentally by measuring 9 temperature points at heat exchanger.  This research  revealed that pw 7 mm with massflow rate 0.000571kg/s can decrease fluid temperature until it reaches 30oC with all wires working to release the heat and it results 74% wire efficiency. Then, pw 9 mm with massflow 0.0011kg/s has 64% wire efficiency, it was because the heat exchanger cannot decrease the fluid temperature to 303K. At high massflow, heat exchanger need more wire to decrease the temperature down to 303K. This research is recommended for cooling system widely applied in food industry, an optimal cooling system will reduce the cost of electricity consumption for cooler.