Hydrogeochemical study of the clastic aquifer in the Umm Gudair field,Kuwait

The Kuwait Group aquifer is a major source of brackish groundwater in the State of Kuwait. The aquifer ranges in thickness from 150–400 m. In the Umm Gudair well field, the water has total dissolved solids ranging from 3,130 to 4,790 mg/l. According to the Sulin classification, all the Kuwait Group aquifer samples are characterized as Cl–Mg or Cl–Ca genetic water types. The chemical data indicate that the aquifer is generally in equilibrium with respect to calcite, dolomite and gypsum but undersaturated with respect to halite and polygorskite. The study has indicated that de-dolomitization is the major geochemical process controlling the chemistry of this aquifer, increasing the major complexes of sulphate and carbonate due to the dissolution of gypsum. The ratio of Cl/Na indicates that the Na⁺ ions have been taken from the Kuwait Group aquifer by reverse ion exchange.      

The effect of ultrasound in combination with thermal treatment on the germinated barley’s alpha-amylase activity

The effects of ultrasound as emerging technology along with thermal treatment were investigated on the activity of barley’s alpha-amylase after germination. All experiments were carried out at 20 kHz on an ultrasonic generator by considering the three effective factors, temperature (30, 50 and 70°C) and ultrasonic intensities (20, 60 and 100% setting from total power of device (460 W)) in different time intervals (5, 10 and 15 min). For determining the effects of these parameters, the enzymatic activity was assayed by measuring the reducing sugars released as a result of the alpha-amylase action on soluble starch using 3,5-dinitrosalicylate regent (DNS). The results of these assays were analyzed by Qualitek4 software by using the Taguchi statistical method to evaluate the factor’s effects on the enzyme activity. Consequently, the results of assays showed that the activity of this enzyme from germinated barley was reduced after thermosonication by comparing to the blank.     

Divided-wall distillation column design using molecular tracking

Divided-wall column (DWC) is an intensified separation process and so far developing a simple procedure for designing these units has been challenging. In this work, the concept of molecular tracking has been integrated with conventional methods to build a simple and easy-to-use methodology for designing DWCs for multicomponent separations. Application of the proposed approach is highlighted through several three- and four-component mixtures. The configuration obtained using molecular tracking gives a design with lower energy demands for the column reboiler, compared to other design methodology, which directly impacts the OPEX of the system.     

Genetic-based multi-objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

The purpose of this research is to find the optimal operating point in the production process of the cumene. Therefore, the production process was optimized through statistical and genetic algorithm-based methods. The performance of an alkylation reactor was optimized through maximizing the yield of cumene production. Response surface methodology (RSM) with design type of central composite was applied for design of experiment, modelling, and optimizing the process. The analysis of variance (ANOVA) was performed for finding the important operative parameters as well as their effects. The effects of three parameters including temperature, reactor length, and pressure on the alkylation process were investigated. Further, two types of feed-forward neural network were applied to model the alkylation reactor. To develop the neural network model, leave-one-out method was used. The best prediction performance belonged to a fitting network with 2 and 8 neurons in the hidden layer, respectively. This model was used for optimizing the performance of the alkylation reactor. The statistical and artificial intelligence systems were capable of prediction of cumene production yield in different conditions with R² of 0.9098 and 0.9986, respectively. Genetic algorithm-based optimization was performed by the developed neural network model. The maximum accessible value of cumene production yield was 0.7771, which can be achieved when the temperature, length of reactor, and column pressure are 160°C, 2 m, and 4000 kPa, respectively. By finding the optimal operating point in the cumene production process, capital cost, energy consumption, and other operating costs can be significantly reduced.     

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.     

Frame‐type load bearing system for long‐span cantilevers

Cantilevers experience high risks of vulnerability against progressive collapse and vertical ground motion effects. In addition, despite common engineering practice that regards cantilevers dominated by vertical loads, it is shown that while subjected to lateral forces, they might undergo large deflections due to the formation of plastic hinges in the supporting structural subassemblage; even if cantilevers satisfy deflection limits proposed by design codes. To overcome such vulnerabilities, successive cantilever beams can be coupled in the height of the structure using vertical elements to develop a framed cantilever system. Frame behavior in cantilevers is formulized using spring models and by employing optimization procedures, economic efficiency is compared to the conventional method. The optimization results for 6 2‐D steel moment frames showed that employing framed cantilever system has the potential to reduce required material weight up to 40%. Furthermore, nonlinear pushdown and incremental dynamic analyses were conducted to extract capacity and fragility curves. The results reveal the superior performance of framed cantilevers in both lateral and vertical loads while offering better resistance against the progressive collapse.     

Seismic design and assessment of structures with viscous dampers at limit state levels: Focus on probability of damage in devices

During large earthquakes, the seismic demand of viscous dampers may exceed their capacity. In this regard, current design codes must consider extreme conditions and preserve the damper at limit state levels. Here, by adjusting the damping coefficient, a procedure is introduced to mitigate device damages during severe earthquakes. To assess the procedure, 15 special moment resisting frames with a different number of stories (two, four, and eight) were designed by three methods: The recommended novel procedure, the seismic provisions of ASCE7, and the procedure proposed by Miyamoto et al. 1 for structures, installed with supplemental damping devices. A series of incremental dynamic analyses were then performed by modeling the limit state behavior of viscous dampers. Results indicated that the novel method reduces the damage probability of dampers as well as the maximum demands on the structure at different seismic hazard levels.     

Predicting probabilistic distribution functions of response parameters using the endurance time method

The main objective of this study is the development of endurance time (ET) excitations in order to take structural response uncertainty into account for use in performance‐based earthquake engineering. There are several uncertainties in earthquake engineering, including earthquake occurrence, structural response, damage, and loss. In the current research, structural response uncertainty is directly included in the ET method, which is an analysis method used for performing structural behavior assessment under seismic actions. Conventional practice of the ET method does not provide any information about seismic response distribution. Despite the simplicity of the ET method, it is an accurate dynamic analysis approach in which structures are subjected to predesigned intensifying acceleration functions, also known as ET excitation functions (ETEFs). In this study, the ETEF generating procedure is modified in order to include the exceedance probability of structural responses observed at an intensity measure. This proposed method is applied to generate new ETEFs; then they are utilized in assessing distribution responses in three structure case studies. Finally, response distributions obtained by the ET method are compared with incremental dynamic analysis so as to investigate the proposed method efficiency. Results show that response probabilistic distributions that are predicted using the ET method match those obtained by incremental dynamic analysis.     

Multiobjective optimal placement of active tendons to control irregular multistory buildings with soil–structure interaction

In recent decades, many researchers have conducted research studies on structural control to improve the safety and serviceability of high‐rise buildings against earthquakes and strong winds. On the other hand, applying active control systems and controlling strategies in buildings are costly process, and it is necessary to reduce the number of controllers. In this paper, a multiobjective genetic algorithm is proposed to optimize the placement of active tendons in a 2D shear frame and a 3D irregular building considering soil–structure interaction effect to reduce active control cost and response of structures at the same time. For multiobjective optimization, multiobjective genetic algorithm of the MATLAB toolbox is used to find a set of Pareto optimal solutions for a multiobjective minimization. The results indicate that the method is capable of finding the number and location of the required active tendons in both 2D shear frame and 3D irregular building with 10 and 20 stories while the base shear of structure is minimized. The specific advantage of the employed algorithms is to reduce the number of mounted active tendons approximately by 50%.     

Titania nanostructured coating for corrosion mitigation of stainless steel

Anatase nanostructured coating has been prepared on 316 L stainless steel by sol-gel dip coating. The topography of the coatings surface has been analyzed using atomic force microscopy. The anticorrosion performance of the coatings has been evaluated using polarization curves. Effects of calcination temperature, withdrawal speed and times of coating on corrosion protection have been studied. The results showed calcination temperature of 400°C and withdrawal speed of 10 cm/min are desirable conditions to achieve high corrosion protection of 316 L stainless steel in chloride containing environments. Coatings with 3 times exhibit better resistance against corrosion in 0.5 molar NaCl solutions. This protection against corrosion arises from photocatalytic properties of anatase nanoparticles.