Sharif Digital Repository

New chemical process design strategies utilizing computer-aided molecular design (CAMD) can provide significant improvements in process safety by designing chemicals with required target properties and the substitution of safer chemicals. An important aspect of this methodology concerns the prediction of properties given the molecular structure. This study utilizes one such emerging method for prediction of a hazardous property, flash point (FP), which is in the center of attention in safety studies. Using such a reliable data set comprising 1651 organic and inorganic chemicals, from 79 diverse material classes, and robust dynamic binary particle swarm optimization for the feature selection... 

This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole- and triazolesubstituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure-activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x-y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R2, and... 

The objective of this research is geometrical optimization of half toroidal Continuously Variable Transmission (CVT) in order to achieve high power transmission efficiency. The dynamic analysis of CVT is implemented and contact between the disk and the roller is modeled viaelastohydrodynamic (EHL) lubrication principles. Computer model is created using geometrical, thermal and kinetic parameters to determine the efficiency of CVT. Results are compared by other models to confirm the model validity. Geometrical parameters are obtained by means of Particle Swarm Optimization (PSO) algorithm, while the optimization objective is to maximize the power transmission efficiency. Optimization was... 

Optimal well placement is a crucial step in efficient reservoir development process which significantly affects the productivity and economical benefits of an oil reservoir. However, it is a complex and challenging problem due to the different engineering, geological and economical variables involved. This leads to a very large number of potential scenarios that must be evaluated using numerical reservoir simulations. The key points in such an optimization process are using a fast function evaluation tool and development of an efficient and robust optimization algorithm that can find good solutions with a minimum required number of function evaluations. This study presents an approach that... 

This study presents a parallel evolutionary optimization approach to determine optimal management strategies of large-scale coastal groundwater problems. The population loops of evolutionary algorithms (EA) are parallelized using shared memory parallelism to address the high computational demands of such applications. This methodology is applied to solve the management problems in an aquifer system in Kish Island, Iran using a three-dimensional density-dependent groundwater numerical model. EAs of continuous ant colony optimization (CACO), particle swarm optimization, and genetic algorithm are utilized to solve the optimization problems. By implementing the parallelization strategy, a... 

In this investigation, a systematic sequential intelligent system is proposed to provide the surgeon with an estimation of the state of the tool-tissue interaction force in laparoscopic surgery. To train the proposed intelligent system, a 3D model of an in vivo porcine liver was built for different probing tasks. To capture the required knowledge, three different geometric features, i.e. Y displacement of the nodes on the upper surface and slopes on the closest node to the deforming area of the upper edge in both X-. Y and Z-. Y planes, were extracted experimentally. The numerical simulations are conducted in three independent successive stages. At the first step, a well-known... 

Capacitor placement/setting is one of the main means for loss reduction and voltage profile improvement in distribution systems. If capacitor placement is meant, the objective function will be the cost of energy losses besides the capacitors costs in a specified period of time. Here, reconfiguration can be used as a strategy to reform the base configuration of the distribution network in order to place the capacitors more efficiently with lower costs. On the other hand, if capacitors are already available in a network, optimum capacitors setting and network reconfiguration should be performed for power loss minimization. In this paper both power loss minimization and capacitor placement cost... 

The quantification of carbon dioxide (CO2) dissolution in oil is crucial in predicting the potential and long-term behavior of CO2 in reservoir during secondary and tertiary oil recovery. Accurate predicting carbon dioxide molecular diffusion coefficient is a key parameter during carbon dioxide injection into oil reservoirs. In this study a new model based on adaptive neuro-fuzzy inference systems (ANFIS) is designed and developed for accurate prediction of carbon dioxide diffusivity in oils at elevated temperature and pressures. Particle Swarm Optimization (PSO) as population based stochastic search algorithms was applied to obtain the optimal ANFIS model parameters. Furthermore, a simple... 

A novel bi-objective three-echelon supply chain problem is formulated in this paper in which cross-dock facilities to transport the products are modeled as an M/M/m queuing system. The proposed model is validated using the epsilon constraint method when applied to solve some small-size problems. Since the problem belongs to the class of NP-hard and that it is of a bi-objective type, a multi-objective particle swarm optimization (MOPSO) algorithm with a new solution structure that satisfies all of the constraints is developed to find Pareto solutions. As there is no benchmark available in literature, three other multi-objective meta-heuristic algorithms called non-dominated ranking genetic... 

A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results... 

Application of fractional order PID (FOPID) controller to an automatic voltage regulator (AVR) is presented and studied in this paper. An FOPID is a PID whose derivative and integral orders are fractional numbers rather than integers. Design stage of such a controller consists of determining five parameters. This paper employs particle swarm optimization (PSO) algorithm to carry out the aforementioned design procedure. PSO is an advanced search procedure that has proved to have very high efficiency. A novel cost function is defined to facilitate the control strategy over both the time-domain and the frequency-domain specifications. Comparisons are made with a PID controller and it is shown... 

This paper proposes a fuzzy-based speed controller for the doubly fed induction generator (DFIG)-based wind turbines with the rotor speed and wind speed inputs. The controller parameters are optimized using the particle swarm optimization algorithm. To accelerate tracking the maximum power point trajectory, the conventional controller is augmented with a feed-forward compensator, which uses the wind speed input and includes a high-pass filter. The proposed combined speed controller is robust against wind measurement errors and as the accuracy of anemometers increases the speed regulation tends toward the ideal controller. The cutoff frequency of the applied filter is determined considering a... 

In this work, an ultrasound experimental setup was designed to investigate the feasibility of using low-frequency ultrasonic waves as a substitute to reduce the consumption of chemical demulsifiers in the pretreatment of crude oil. The experiments were planned to study the effects of irradiation time, ultrasonic field intensity and initial water content on the efficiency of separation. The results of experiments showed that by selecting a proper irradiation time and field intensity, it is possible to decrease the usage of demulsifiers by 50%. Moreover, a population balance model was proposed to explicate the experimental data. A hybrid coalescence model was developed to determine the... 

This paper presents design of a typical Guided Flying Vehicle (GFV) using the multidisciplinary design optimization (MDO). The main objectives of this multi-disciplinary design are maximizing the payload’s weight as well as minimizing the miss distance. The main disciplines considered for this design include aerodynamics, dynamic, guidance, control, structure, weight and balance. This design of GFV is applied to three and six Degree of Freedom (DOF) to show comparison of simulation results. The hybrid scheme of optimization algorithm is based on Nelder-Mead Simplex optimization algorithm and Nondominated Sorting Genetic Algorithm II (NSGA II), called Simplex-NSGA II. This scheme is... 

A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, GA and PSO are performed to conduct fine tuning for the obtained parameter set of the premise parts and consequent parts in the... 

Diabetes mellitus is a medical condition examined by data miners for reasons such as significant health complications in affected people, the economic impact on healthcare networks, and so on. In order to find the main causes of this disease, researchers look into the patient's lifestyle, hereditary information, etc. The goal of data mining in this context is to find patterns that make early detection of the disease and proper treatment easier. Due to the high volume of data involved in therapeutic contexts and disease diagnosis, provision of the intended treatment method become almost impossible over a short period of time. This justifies the use of pre-processing techniques and data... 

Various urban, industrial, and agricultural pollutions discharge more than river self-purification potential damages river ecosystem and increases water treatment costs. As different decision-makers and stakeholders are involved in the water quality management in river systems, a new bankruptcy form of the game theory is used to resolve the existing conflict of interests related to waste load allocation in downstream river. The river restoration potential can allocate to the conflicting parties with respect to their claims, by using bankruptcy solution methods. In this research, dischargeable pollution loads to Karun River are determined by pollution sources in various scenarios using... 

In the last years, an appreciable effort has been directed toward developing empirical models to link the relative permeability of gas condensate reservoirs to the interfacial tension and velocity as well as saturation. However, these models suffer from non-universality and uncertainties in setting the tuning parameters. In order to alleviate the aforesaid infirmities in this study, comprehensive modeling was carried out by employing numerous smart computer-aided algorithms including Support Vector Regression (SVR), Least Square Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), and Gene Expression Programming... 

New gains tuning and impedance control method were addressed for flexible base moving manipulators. Slow and fast dynamics of robot are decoupled using singular perturbation method. Then, using sliding mode control method, an impedance control law was derived for the slow dynamics. Combined control law was proposed comprising the impedance control law and a feedback control law for the fast dynamics. As fist time, we proposed a new online particle swarm optimization algorithm for gain tuning of impedance control at the contact moments of end effector and unknown environments. This proposed Sliding Mode Impedance Controller and online PSO were simulated for a Flexible Base Moving Manipulator.... 

A reasonable amount of spinning reserve is essential to satisfy system security constraints when the power system encounters a contingency. Most ofthe methods previously developed for spinning reserve assessment ignore the probabilistic aspects of system components. This paper presents the design of a market for energy and spinning reserve services. This dispatching method has been developed to minimize the cost associated with theses services while maintaining the system security. Particle swarm optimization (PSO) is used for determining the global optimal solution for this dispatch problem. The effectiveness of the proposed approach is examined by application to the IEEE Reliability Test...