International Journal of Engineering
Volume:30 Issue: 11, Nov 2017

In this study, the hydrolysis of pretreated sorghum stem and rice husk was investigated at various initial enzyme concentrations and substrate loadings. The slowdown in enzymatic hydrolysis of lignocellulosic materials with conversion has often been attributed to decreasing the activity of enzyme. A kinetic model was developed and expressed mathematically based on enzyme deactivation for enzymatic hydrolysis of lignocellulosic materials. The decline in activation of the adsorbed enzyme is represented by a second order reaction. The models were used to fit experimental data of sorghum stem and rice husk hydrolysis. The models basic parameters which can explain the effects observed experimentally were determined and discussed. The model performed well in predicting hydrolysis trends at experimental condition.

This study was conducted to investigate the effect of tofu wastewater (TW) addition on the S. platensis growth. The TW addition was varied in range of 0 – 8%v/v. The results showed that the growth rate (μ) of S. platensis at TW addition of 0, 2, 4, 6, 8 %v/v was 0.007, 0.084, 0.074, 0.088, 0.086 mg/L/d respectively. The medium with TW addition of 6%v/v (Carbon:Nitrogen:Phosphorous = 161:17:1; Carbon/Nitrogen = 9.55) was the best medium for biomass production. The growth rate of S. platensis was successfully modeled by using modified Gompertz equation (R2 = 0.93 – 0.98). In prediction through modified Gompertz, the highest maximum biomass (2.17 mg/L) was resulted from medium IV (TW of 6%v/v). Medium IV (TW of 6%v/v); II (TW of 2%v/v); III (TW of 4%v/v) resulted biomass containing the highest protein (66.62%), the highest carbohydrate (61.23%), the highest lipid (19.66%) respectively.

The aim of this research was to evaluate the effective parameters such as pH, current density (mA/cm2), H2O2/Fe2 molar ratio, volume ratio of H2O2 to pharmaceutical wastewater (PhW) (ml/l) and reaction time (min) on the electro-Fenton process for the ibuprofen (as a pharmaceutical waste in water) removal. Since a synthetic wastewater with the same concentration of ibuprofen in a real pharmaceutical wastewater (400 ppm) was chosen in this research, the sample was tested in terms of the chemical oxygen demand (COD). The parameters were statistically optimized under response surface mythology (RSM). The software was also applied to minimize the number of runs. The optimum conditions for 98.290% COD removal were at pH of 2.43, current density of 23.08 mA/cm2, H2O2/Fe2 molar ratio of 2.69, volume ratio of H2O2/PhW of 1.84 ml/l and reaction time of 28.08 min.

Municipal and industrial wastewater contains a lot of contaminants. The major contaminant of concern is the heavy metals. Heavy metals are known to be toxic, non-biodegradable and have a long half-life. The release of untreated wastewater containing heavy metals can cause serious problems to human, plants and animals. In this study, activated carbon was developed from sugarcane bagasse and its effectiveness in absorbing lead ions from wastewater was examined. Sugarcane bagasse activated carbon was developed in a tube furnace at a temperature of 900 °C, a heating rate of 10 °C/min, the residence time of 3 hrs, and at a nitrogen flow rate of 100 mL/min. Batch adsorption experiments were carried out to investigate the effects of pH and initial lead concentrations on the adsorption process. The batch adsorption test showed that extent of lead adsorption by SCBA was dependent upon pH and initial lead concentrations. The optimum pH for lead adsorption was found to be pH 5.0. Removal of lead decreases with the increase in initial metal concentrations. The adsorption of lead ions onto SCBA is a pseudo-second-order reaction model. The rate limiting step is a chemisorption or chemical adsorption that involves valence forces by means of electrons exchange between the SCBA and lead ions.

In the present work, the effect of Soil-Structure Interaction (SSI) in structures resting on a soft soil, through numerical modelling and shaking table tests have been studied. In theoretical studies two types of models namely fixed base and flexible base structure were subjected to three selected earthquake records. Fully Nonlinear method was employed for analyzing all of the numerical models and Finite Element Method (FEM) was used for modelling soil beneath the structure. In order to verify the outputs of the numerical modelling, shaking table tests were carried out. In experimental tests, scaled form of the main structure, according to scaling laws was built. Laminar Shear Box (LSB) as a container, was built to implement soil in the soil-structure model on the shaking table. By comparison between the numerical modelling and the shaking table tests results, a good agreement was observed. Therefore, the results of the comparison between the fixed base and the flexible base, in the numerical modelling are reliable. In this study, was demonstrated that if the structure rested on the soft soil, is located in a site which is susceptible to experience strong earthquakes, the necessity of considering SSI effects is avoidable.

A parametric formulation for preliminary design of tubed-system tall buildings, in which some optimality criteria and practical constraints are considered, is presented. Here, a minimum compliance optimization formulation, developed by other researchers, is applied to a framed-tube structure. The tube behavior is modeled as a cantilevered box beam. Independent variable in this problem is thickness of the box, and a formulation for its optimal value is proposed. The challenge in this research was treatment of the lower bound constraint on thickness in an analytical manner. To deal with this constraint, a critical height parameter (CH) is introduced, and the design domain is divided into two zones of constant thickness (CT) and constant curvature (CC). This definition allows for computation of optimal thickness distribution along the structure through an analytic dimensionless equation. Different static loading patterns are considered; including the concentrated, uniform, triangular and quadratic forms. A numerical example is presented to demonstrate the ease of the proposed method in application, and the analysis results are presented by charts to validate the efficiency of it.

This study focuses on evaluating the unconfined compressive strength (UCS) of improved fine-grained soils. A large database of unconfined compressive strength of clayey soil specimens stabilized with fly ash and blast furnace slag based geopolymer were collected and analyzed. Subsequently, using adaptive neuro fuzzy inference system (ANFIS), a model has been developed to assess the UCS of stabilized fine-grained soils. Types of additives and their compositions as well as soil characteristics were considered as the most important parameters affecting the resistance of stabilized soil. Subsequently, the accuracy of the proposed model was examined. Finally, a parametric study was conducted to investigate the performance of the proposed model and also the effect of each influential parameter on the UCS of amended soil specimens. The results demonstrate that the ANFIS-based model, that was developed based on experimental results, can be successfully applied for assessment of unconfined compressive strength of stabilized fine-grained soils.

Cement substitution in self compacting concrete (SCC) is emphasized to conserve environment, reduce cost and utilize waste materials. This paper focuses on comparing the permeability and drying shrinkage of SCC containing Wollastonite micro fiber (WMF), a cheap pozzolanic fiber with respect to flyash. Microsilica was added for providing required viscosity to maintain homogeneity of the mixes. Trials to check flowability, passability and segregation resistance were conducted on binary, ternary and combined mixes of binder material. Results showed that drying shrinkage reduced by 49% for WMF reinforced concrete, whereas it increased by 1.25% for flyash’s ones as compared to normal concrete. Permeability coefficient decreased by 82% and 74%, respectively. Capillary voids influenced the permeability of hardened concrete but drying shrinkage was largely influenced by the rate of gain of tensile strength and expanding ettringite. Notably, flyash is not a reliable admixture for controlling drying shrinkage of high flow concretes.

Structural walls commonly used as efficient structural elements to resist lateral and vertical loads. Different performance of bearing walls in past earthquakes, motivates investigation on the adequacy of current seismic design provision for these walls. This study considers seismic performance of model walls of bearing wall system and building frame system designed as ordinary and special shear walls. Performance of the model walls are evaluated through static pushover and incremental dynamic analyses. Results show the superior performance of bearing wall system, which shows justification for possible increase in the response modification factor of this sytsem in the design codes.

This paper deals with a new method to distinguish the printed digits, without regard to font and size, using neural networks.Unlike our proposed method, existing neural network based techniques are only able to recognize the trained fonts. These methods need a large database containing digits in various fonts. New fonts are often introduced to the public, which may not be truly recognized by the Optical Character Recognition (OCR). Therefore, the existing OCR systems may need to be retrained or their algorithm be updated. In this paper we propose a self-organizing map (SOM) neural network powered by appropriate features to achieve high accuracy rate for recognizing printed digits problem. In this method we use a limited sample size for each digit in training step. Two expriments are designed to evaluate the performance of the proposed method. First, we used the method to classify a database including 2000 printed Persian samples with twenty different fonts and ten different sizes from which 98.05% accuracy was achieved. Second, the proposed method is applied to unseen data with different fonts and sizes with those used in training data set. The results show 98% accuracy in recognizing unseen data.

As the internet and its applications are growing, E-commerce has become one of its rapid applications. Customers of E-commerce were provided with theopportunity to express their opinion about theproduct on the web as a text in the form of reviews. In the previous studies, mere founding sentiment from reviews was not helpful to get theexact opinion of the review. In this paper, we have used Aspect-Based Opinion Mining to get more Interesting aspects of a product’ssentiment from unlabelled textual data. First noun phrases algorithm was used to get all the aspect term of a review sentence. Secondly get sentiment algorithm was applied on the result of thenoun-phrase algorithm. Finally using relativeimportance algorithm important aspects were presented to the user. Our proposed methodology has achieved 77.03% of accuracy compared to previews studies. The proposed methodology can be applied for any product reviews in the form of text without any label, and it does not require any training dataset.

Software Defined Network is a new architecture for network management and its main concept is centralizing network management in the network control level that has an overview of the network and determines the forwarding rules for switches and routers(the data level).Although this centralized control is the main advantage of SDN, it is also a single point of failure. If this main control is made unreachable for any reason, the architecture of the network is crashed. A DDoS attack is a threat for the SDN controller that can make it unreachable. Most of the previous works in DDoS detection in SDN focus on early detection of DDoS and not enough work have been done on improvement of accuracy in detection. The proposed solution of this research can detect DDoS attack on SDN controller with a noticeable accuracy and prevents serious damage to the controller .For this purpose, fast entropy of each flow is computed at certain time intervals. Then by the use of adaptive threshold, the possibility of a DDoS attack is investigated. In order to achieve more accuracy, another method, computing flow initiation rate, is used alongside. After observation the results of this two methods, according to the conditions described later, the existence of an attack is confirmed or rejected, or this decision is made at the next step of the algorithm, with further study of flow statistics of network switches by the perceptron neural network.

Machine learning based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slow-er learning rate and higher computational cost. Feature selection is expected to deal with the high dimensionality of data-sets in terms of reduced feature set. Feature selection improves the performance of classification accuracy particularly performing with less number of features in decision making process. In this paper Random Forest (RF) is employed for the diagnosis of cardiovascular disease. The first phase of proposed system aims at constructing various feature selection algorithm such as Principal Component Analysis (PCA), Relief- F, Sequential Forward Floating Search (SFFS), Sequential Backward Floating Search (SBFS) and Genetic Algorithm (GA) for reducing the dimension of cardiovascular disease datasets. The second phase switched to model construction based on RF algorithm for cardiovascular disease classification. The obtained outcome shows that the combination with GA and RF delivered the highest classification accuracy of 93.2% by help of six features.

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Virtualization technology forms a key concept for new cloud computing architectures. The data centers are used to provide cloud services burdening a significant cost due to high energy consumption. Data centers are provisioned to accommodate peak demand rather than average demand and cloud applications consume much more electrical energy than they need. Thus, it necessitates that cloud computing solutions not only minimize operational costs, but also reduce the power consumption. In this paper, we investigate load balancing and power saving methods in virtualized cloud infrastructures. Imbalanced distribution of workloads across resources can lead to performance degradation and much electrical power consumption in such data centers. We present an architectural framework and principles for energy-efficient cloud computing environments. Resource provisioning and allocation algorithms, named Load-Power-aware, are proposed in this architecture. The algorithm employs a heuristic to dynamically improve the energy efficiency in data center, while guarantees the Quality of Service (QoS). The architecture significantly improves the energy efficiency in a given dynamic scenario.

Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a method to identify network traffics. In this method, for cleaning and preparing data, we perform effective preprocessing approach. Then effective features are extracted using the behavioral analysis of application. Using the effective preprocessing and feature extraction techniques, this method can effectively and accurately identify network traffics . For this purpose, two network traffic databases namely UNIBS and the collected database on router are analyzed. In order to evaluate the results, the accuracy of network traffic identification using proposed method is analyzed by machine learning techniques. Experimental results show that the proposed method has improved the accuracy of network traffic identification methods.

The type-2 fuzzy set theory is one of the most powerful tools for dealing with uncertainty and imperfection in a dynamic and complex environment. The applications of type-2 fuzzy sets and soft computing methods are rapidly emerging in the ecological fields such as air pollution and weather prediction. The air pollution problem is a major public health problem in many cities of the world. Prediction of natural phenomena always suffers from uncertainty in the environment and incompleteness of data. There are many studies reported for air quality index prediction but all of them suffers from uncertainty and imprecision associated to the incompleteness of knowledge and imprecise input measures. This work takes advantages of learning and adoption of adaptive neural networks alongside in new environment. Furthermore, it presents an adaptive neuro-type-2 fuzzy inference system (ANT2FIS) to address the uncertainty and imprecision in air quality prediction. The dataset of this study was collected from Tehran municipality official website for last five years (2012-2017). The result reveals that the ANT2FIS method prediction is more reliable and is capable of handling uncertainty compared to the other counterpart methods. The performance results on real dataset shows advantage of the type2- ANFIS system in the prediction process with an average accuracy of 94% (AUC 99%) compared to other related works.

In this paperý, ýthe impacts of premium bounds of put option contracts on the operation of put option and day-ahead electricity markets are studiedý. ýTo this endý, ýfirst a comprehensive equilibrium model for a joint put option and day-ahead markets is presentedý. ýInteraction between put option and day-ahead marketsý, ýuncertainty in fuel price, impact of premium bounds, and elasticity of consumers to strike priceý, ýpremium price, and day-ahead price are taken into account in this modelý. ýThen, a new method for put option pricing is proposed. By applying the presented model to a test system the impacts of premium bounds on equilibrium of joint put option and day-ahead markets are studiedý.

In this paper, a 7-level Diode Clamped Multilevel Inverter (DCMLI) is simulated with three different carrier PWM techniques. Here, carrier based Sinusoidal Pulse Width Modulation (SPWM), Third Harmonic Injected Pulse Width Modulation (THIPWM) and Modified Carrier-Based Space Vector Pulse Width Modulation (SVPWM) are used as modulation strategies. These modulation strategies include Phase Disposition Technique (PD), Phase Opposition Disposition Technique (POD), and Alternate Phase Opposition Disposition Technique (APOD). In all the modulation strategies triangular carrier and trapezoidal triangular carrier signlas are compared with refrence signal and control pulses are generated. The detailed analysis of the results has been presented and compared with experimental results in terms of fundamental component of output voltage and % of THD.

Applying Bridgman method it was obtained that tetragonal single crystals of solid solution (Cd0.6Zn0.32Mn0.08)3As2, space group P42/nmc. Measurements of the temperature dependence of conductivity and magnetoresistance in the temperature range of 1.6K to 300K and in magnetic field up to 25 T were carried out. Close to helium temperatures it is set to a range of implementation of Mott\\\'s variable-range hopping conductivity mechanism. It was determined that the width of the coulomb D = 0.21 meV and a rigid gap δ = 0.026 meV in the density of localized states, concentration and localization radius of charge carriers.

Steering Knuckle is a basic part of vehicle which joins suspension and steering system, wheel and brake to the chassis. It experiences several loads subjected to different conditions, for example, multi-axial loads and vibration. Hence, the knowledge of its dynamics and vibrational behavior is very important. Several materials are used to produce steering knuckle, for example, SG Iron, white and grey cast iron. However, nowadays there is a tendency to use aluminum alloy by automakers. Material replacement which results in weight reduction and using advanced materials are highly preferable. One suggestion is the use of Metal Matrix Composite which has higher strength to weight ratio and better performance than a metal. The main aim of this research is to determine the best material for manufacturing of steering knuckle in order to reduce the weight by applying aluminum alloy and Metal Matrix Composite. To achieve this purpose, the Modal test has been performed to study vibrational behavior of steering knuckle. CAD Model has been prepared by using coordinate measuring machine (CMM). Finally, the finite element analysis has been performed to evaluate natural frequencies and mode shapes of knuckle. The obtained FEM results have been compared with experimental data to validate the simulation. Three groups of materials (iron, aluminum alloy and metal matrix composite with different fiber volume ratio) have been investigated to determine the best material for manufacturing. DIN 1.7035, unreinforced alumina and MMC-Al 15% Ti-C have been reported as the best materials in each groups. MMC materials has higher vibrational rigidity and by using it, about 63.65 percent weight reduction is possible. FEM results for different models including CMM and smooth model have been compared with test data. The CMM model is closer to reality and it contains all details such as barcode, data and surface defects. It is obvious that meshing of smooth surface is easier than CMM model, but some details will be ignored which could affect the results. However, it has been shown that use of CMM model creates about 5.21% errors related to test data in comparison of 2.58% when the smooth model is used.

The damping ratio of chassis suspension is a key parameter for damping matching of in-wheel motor vehicles (IWMVs). Because the motor is attached to the driving wheel, the initial design method of the damping ratio for traditional cars is not entirely suitable for IWMVs. This paper proposes an innovative initial design method of the damping ratio for IWMVs. Firstly, a traveling vibration model of occupant-vehicle-road (OVR) for IWMVs is established. The model involves the occupant, cushion, suspension, in-wheel motor, road, and running speed. Secondly, on the basis of the model, using a special form of infinite integral, a mathematical expression of the occupant root-mean-square (RMS) acceleration is derived. Thirdly, based on the RMS optimization criterion for ride comfort, an 8 order polynomial equation about the suspension optimal damping ratio is deduced. Subsequently, through factors analysis, the change principles of the optimal damping ratio versus vehicle parameters are unveiled. Finally, the reliability of the optimal damping ratio is validated by test using a commercially available IWMV. The relative deviation of the calculated optimal damping ratio and the tested damping ratio is 5.4%. The results show that the proposed optimal damping ratio can be used in the preliminary design phase for IWMVs.

Theoretical and experimental investigation of wear during coupling in internal gears coated with various polymeric coating materials was performed. In the theoretical part of the study, Archard’s wear formulation was adapted to internal gears and wear behaviour in different conditions was determined. Moreover, a fatigue and wear testing apparatus having similar working principle with FZG (Forschungsstelle für Zahnrader und Getreibbau) closed circuit power circulation system was designed and manufactured to experimentally investigate the wear in internal gears. Internal gear-pinion couples manufactured from St50 material were coated with various polymeric materials (PTFE (polytetrafluoroethylene), MoS2 bonded with polyamide, MoS2 bonded with epoxy) in the experimental study. An uncoated internal gear was also investigated to find out the performance of coated gears. Wear values in the teeth profiles of internal gears were determined theoretically and experimentally. Theoretical and experimental studies showed that polymeric coated internal gears have more wear resistance than uncoated ones. It is also observed that coating increases the wear strength of internal gears.

The study of rotary steering drilling technology is currently one of the hot topics in the drilling engineering field. It requires accurate well trajectory control instructions when rotary steerable tools were applied to achieve the well trajectory control goal. A drilling trajectory prediction model will benefit this progress. According to the continuous beam theory, a mechanical model of push-the-bit rotary steerable bottom hole assembly (RSBHA) was established to characterize the bit steering property. The relation of bit lateral force and bit tilt angle with the influencing parameters such as borehole parameters and drilling operation parameters was obtained. Then further considering the bit cutting anisotropy, the drilling trajectory prediction model was built which quantitatively estimated the variation of inclination and azimuth angle. The model calculation result showed a consistency with the field experimental data proving the prediction model reasonable in theory and feasible in engineering. This study could provide a guidance for selecting the steering parameters to meet the control goal.

This study is designed to consider the two important yet often neglected factors, which are factory recommendation and bit features, in optimum bit selection. Iimage processing techniques to consider the bit features have been used. A mathematical equation, which is derived from a neural network model, is used for drill bit selection to obtain the bit’s maximum penetration rate that corresponds to the optimum parameters for drilling. In the end, the bit with the maximum penetration rate is chosen. The results of this study showed that bit pattern can be inserted in the calculation through a proper bit image processing techniques. This is to ensure that each unique bit can be discriminated from other bits. The values of mean square error 0.0037 and coefficient of determination (R2) 0.9473 were found for the rate of penetration model. The image processing techniques were used to extract the bit features. The artificial neural network black box was converted to white box in order to extract a mathematical equation and visibility of the model.