Sharif Digital Repository

In this study; a Sugeno type ANFI model which describes the relationship between the bio surfactant concentration as a model output and the critical medium components as its inputs has been constructed. The critical medium components are glucose, urea,SrCl2 and MgSo4 .The experimental data for training and testing capability of the model obtained by a statistical experimental design which have been captured from literatures. Six generalized bell shaped membership function have been selected for each of input variables and based on the training data ANFI model has been trained using the hybrid learning algorithm. The yielded biosurfactant concentration values from the model prediction shows... 

In this article, I have tried to design an intelligent system which can separate and classify perfect and defective circular plates according to their size. After preprocessing, specifications of defects and size are determined through image processing, and finally, a system is proposed through perceptron neural networks methods, neuro fuzzy method, and Lvq to separate these products on basis of their size and defects. In the designing of this system, when input and its related intend is obvious before training network, perceptron neural networks give more exact results. If input and its related output have been clarified but the output have been related to some sub-inputs, lvq method is... 

In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat... 

Stable control of water level of drum is of great importance for economic operation of power plant steam generator systems. In this paper, a linear model of the boiler unit with time varying parameters is used for simulation. Two transfer functions between drum water level (output variable) and feed-water and steam mass rates (input variables) are considered. Variation of model parameters may be arisen from disturbances affecting water level of drum, model uncertainties and parameter mismatch due to the variant operating conditions. To achieve a perfect tracking of the desired drum water level, two sliding mode controllers are designed separately. Results show that the designed controllers... 

An important aspect of corrosion prediction for oil/gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data, and theoretical models to obtain realistic assessments of corrosion rates. This study presents a new model to predict corrosion rates by using artificial neural network (ANN) systems. The values of pH, velocity, temperature, and partial pressure of the CO2 are input variables of the network and the rate of corrosion has been set as the network output. Among the 718 data sets, 503 of the data were implemented to find... 

Ionic liquids (ILs) have been considered as a good candidate to be replaced by the conventional solvent in recent years due to their potential consumptions and unique properties. In the present study, artificial neural network was used to predict the ternary viscosity of mixtures containing ILs. A collection of 729 experimental data points were gathered from the previously public shed literatures. Different topologies of a multilayer feed forward artificial neural network (MFFANN) were examined and optimum architecture was determined. Ternary viscosity data from the literature for 5 ILs with 547 data points have been used to train the network. In addition, to differentiate dissimilar... 

The rapid growth in use of renewable intermittent energy resources, like wind turbines (WTs) and solar panels, in distribution networks has increased the need for having an accurate and efficient method of handling the uncertainties associated with these technologies. In this paper, the unsymmetrical two point estimate method (US2PEM) is used to handle the uncertainties of renewable energy resources. The uncertainty of intermittent generation of WT, photo voltaic cells, and also electric loads, as input variables, are taken into account. The variation of active losses and imported power from the main grid are defined as output variables. The US2PEM is compared to symmetrical two point... 

A multilayer back propagation learning algorithm was used as an artificial neural network tool to predict the mechanical properties of porous NiTi shape memory alloys fabricated by press/sintering of the mixed powders. Effects of green porosity, sintering time and the ratio of the average Ti to Ni particle sizes on properties of the product were investigated. Hardness and tensile strength of the compacts were determined by hardness Rockwell B method and shear punch test. Three-fourths of 36 pairs of experimental data were used for training the network within the toolbox of the MATLAB software. Porosity, sintering time and particle size ratios were defined as the input variables of the model.... 

Stable control of water level of drum is of great importance for economic operation of power plant steam generator systems. In this paper, a linear model of the boiler unit with time varying parameters is used for simulation. Two transfer functions between drum water level (output variable) and feed-water and steam mass rates (input variables) are considered. Variation of model parameters may be arisen from disturbances affecting water level of drum, model uncertainties and parameter mismatch due to the variant operating conditions. To achieve a perfect tracking of the desired drum water level, two sliding mode controllers are designed separately. Results show that the designed controllers... 

Chatter suppression in machining processes results in more material removal rate, high precision and surface quality. In this paper, two control strategies are developed to suppress chatter vibration in the turning process including a worn tool. In the first stage, a sinusoidal spindle speed variation around the mean speed is modulated to disturb the regenerative mechanism. The optimal amplitudes of the speed modulations are found based on a genetic algorithm such that the input energy to the turning process is minimized. In the second stage, to improve the response of the system which is associated with small ripples under the steady state condition, an adaptive controller is designed. In... 

This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy... 

One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm. These constraints are usually expressed as some simplicity assumptions on the target. For instance, in the Vapnik–Chervonenkis (VC) theory the space of possible hypotheses is considered to have a limited VC dimension One way to formulate the simplicity assumption is via information theoretic concepts. In this paper, the constraint on the entropy H(X) of the input variable X is studied as a simplicity assumption. It is proven that the sample complexity to achieve an ∊-δ Probably Approximately Correct (PAC) hypothesis is bounded by [Formula... 

This paper presents the application of a fuzzy logic controlled Static Compensator (STATCOM) to improve stability of power system. The nonlinear fuzzy logic controller is used to overcome the problems generated by different uncertainties exist in power systems. Different input variables are used to design the controller. Parameters of the proposed controller are adjusted by means of Neural Network techniques to improve performance of the system. Proposed controller is implemented on a single machine infinite bus system to confirm the performance of the controller through simulation results  

The economical operations of power plants and environmental awareness are the major factors affecting the importance of control in boiler-turbine units. In this paper, a multivariable nonlinear model of boiler-turbine unit is considered. Drum pressure, electric output and water level of drum (as output variables) are adjusted at desired values by manipulation of valve positions for fuel, steam and feed-water flow rates (as input variables). Nonlinear dynamics of the unit is investigated through the concepts of bifurcation and limit cycles behaviour. In the presence of harmonic disturbances, some coefficients of the dynamic model, fuel and steam flow rates play as the bifurcation parameters.... 

The economical operations of power plants and environmental awareness are the major factors affecting the importance of control in boiler-turbine units. In this paper, a multivariable nonlinear model of boiler-turbine unit is considered. Drum pressure, electric output and water level of drum (as output variables) are adjusted at desired values by manipulation of valve positions for fuel, steam and feed-water flow rates (as input variables). Nonlinear dynamics of the unit is investigated through the concepts of bifurcation and limit cycles behaviour. In the presence of harmonic disturbances, some coefficients of the dynamic model, fuel and steam flow rates play as the bifurcation parameters.... 

It is always crucial to estimate process capability index (PCI) when the quality characteristic does not follow normal distribution, however skewed distributions come about in many processes. The classical method to estimate process capability is not applicable for non-normal processes. In the existing methods for non-normal processes, probability density function (pdf) of the process or an estimate of it is required. Estimating pdf of the process is a hard work and resulted PCI by estimated pdf may be far from real value of it. In this paper an artificial neural network is proposed to estimate PCI for right skewed distributions without appeal to pdf of the process. The proposed neural... 

Background: Asymmetric lifting activities are associated with low back pain. Methods: A finite element biomechanical model is used to estimate spinal loads during one- and two-handed asymmetric static lifting activities. Model input variables are thorax flexion angle, load magnitude as well as load sagittal and lateral positions while response variables are L4-L5 and L5-S1 disc compression and shear forces. A number of levels are considered for each input variable and all their possible combinations are introduced into the model. Robust yet user-friendly predictive equations that relate model responses to its inputs are established. Findings: Predictive equations with adequate... 

Response surface methodology is used to establish robust and user-friendly predictive equations that relate responses of a complex detailed trunk finite element biomechanical model to its input variables during sagittal symmetric static lifting activities. Four input variables (thorax flexion angle, lumbar/pelvis ratio, load magnitude, and load position) and four model responses (L4-L5 and L5-S1 disc compression and anterior-posterior shear forces) are considered. Full factorial design of experiments accounting for all combinations of input levels is employed. Quadratic predictive equations for the spinal loads at the L4-S1 disc mid-heights are obtained by regression analysis with adequate...