Revista global de tecnología y optimización

The special class of a nonlinear mathematical programming problem which is addressed in this paper has a structure characterized by a subset of variables restricted to assume discrete values, which are linear and separable from the continuous variables. The strategy of releasing non-basic variables from their bounds, combined with the “active constraint” method and the notion of super-basics, has been developed for efficiently tackling the problem. After solving the problem by ignoring the integrality requirements, this strategy is used to force the appropriate non-integer basic variables to move to their neighborhood integer points. A study of criteria for choosing a non-basic variable to work with in the integer zing strategy has also been made. Successful implementation of these algorithms was achieved on various test problems. The results show that the proposed integer zing strategy is promising in tackling certain classes of mixed integer nonlinear programming problems.

Process Integration (PI) is a powerful tool for designing and optimising processes for energy efficiency and sustainability. It has been widely extended and become both a part of most good degree studies curricula as a routine tool for advanced design and optimisation in various industries. However, sometimes its simplicity is still misunderstood. Even PI and in this contribution specifically heat integration (HI) has some potential pitfalls related to the problem formulation and data extraction. Regardless of the precision used, the results largely depend on solving the correct problem – i.e. if the formulation reflects the reality adequately and if the appropriate data have been extracted. An incorrect data extraction has been the reason for conclusions that PI did not work. When revisiting most of those problems, it becomes obvious that it was not a fault of the PI methodology, but an inexperienced user.

New concepts of active electricity networks represent a (r) evolution in the production and efficient use of electricity. On the one hand, the developed system-oriented solutions will allow high penetration of distributed energy resources and, on the other, consumer-oriented solutions an efficient use of energy by endusers. In the paper, the European Strategic Energy Technology Plan is presented. Main drivers for changing the existing power networks are discussed. The question arises why changes are necessary and what are the characteristics of today’s (yesterday’s) networks compared to the networks of tomorrow? Further, the impact of Distributed Energy Resources on network operation, the concept and implementation of active networks from the Slovenian perspective is presented. An overview of some national projects and the state of play is given.

Faults studies form an important part of power system analysis. The problem consists of determining bus voltages and line currents during various types of faults. If the fault location is known, the problem is easy to solve. But if the fault location is unknown, the problem will become more complex. The problem of fault location has been studied deeply for transmission lines due its importance in the power system. Different methods for sags prediction have been developed. The most used are “critical distance” and “fault positions”. The critical distance method is based on the concept of potential divider, which is correctly and easily applicable to a radial network. The extension of this method to large meshed networks has been discussed but yet non of the existing researches could provide proper solution for the problem. In this paper, an elegant, analytical method is developed to calculate the critical distance of a three-phase fault on transmission line that will cause certain voltage dip at a bus in meshed power system. The method is based in Gauss-Seidel iteration. The proposed method is tested on 6-bus transmission network and the results showed significant advantages of the proposed method .

There are several speed drives and electronic devices in the market used to control the speed of ac motors. The recent invented development kit is the Microchip dsPICDEM MC1 motor control development board which is rather huge in size and bulky. In fact, it is not bringing convenience to be mobilized from one place o another. In consequence, based on the advantages brought to the world of technology by dsPICDEM, smaller equipment is designed. This compact sized development board is favourable due to its convenient size. The objective of this paper is to evaluate the process flow of the signal in the converter module and fabricate small size electronic components that obtain the same values required for motor speed control. The small driver is able to convert the input current from the humidity and temperature sensor to the motor speed in order to develop a sensitive motor with the atmosphere. Hence, senseless vector control or direct torque control can be employed in the microcontroller program to improve the performances of the development kit. This therefore, calculates speed and position of the rotor based on the feedback output voltage and current.

During the last decades, the rapid development of power semiconductor devices has allowed the increased use of adjustable speed ac drives in a variety of applications, especially in the process-control industry. In many applications, the capability of controlling the speed effectively can improve the efficiency of the ac motors and thus lead to large savings in energy. Among the several approaches used to control ac motors is the direct torque control (DTC), occupies an important place. DTC of ac motors is known to have very favorable control performance and implementation properties. The control scheme is based on the control of torque and flux utilizing the stator flux field orientation. Field orientation is achieved using advanced motor theory to calculate the torque directly and without using modulation. DTC enables the control of speed and torque over a very broad range. The torque response is particularly fast and it is possible to maintain constant speed, even when the mechanical load imposes sudden and unexpected mechanical shock. Thus the advancement of this ac drive technology enables the machine to achieve excellent dynamic performance. This paper is an attempt to investigate and evaluate the characteristics and operating principle of DTC scheme. Experimental tests have been carried out using ABB speed drive unit (ACS800 model), squirrel-cage induction motor and three-phase pendulum machine with integrated torque pick-up to validate the effectiveness and feasibilities of this controlling technique.

Web OS is a virtual desktop on the web, accessible via a browser that provided user access to operating system to manage and organize its data free from hardware in every place easily. Using semantic web technology in Web OS is a new approach that can be used in order to increase of quality configuration of resource management as well as resource allocation. Resource management is the efficient and impressive strategy which can be apply for positioning of Web OS resources on occasion. Load balancing is considered as the main challenge in the resource management domain. So, it can play a challenging, complicated, and significant role in the performance of web OS. In this paper, the effects of semantic technology in order to improve resource management in common web operating systems are studied. In addition, we show how annotating web OS resources can increase the quality of resource management status as well as the fault tolerant in front of Web OS files.

Six Sigma is one of the most popular quality methods. It utilizes a statistical unit of measurement to measure the capability of the process, then achieve defect free performance, and ultimately increase the bottom-line and customer satisfaction. The concept of Six Sigma can be applied in the process of quality control in general and acceptance sampling in particular. Many sampling plans have been constructed using six sigma quality levels, under the assumption that the lot size is too large or infinite. It is unconvincing to say that the lot is accepted or rejected based on a fixed sample size irrespective of the huge lot size. So, a sampling plan is required which depends on the lot size. The major objective of this paper is to determine the size of the lot of a six sigma based on double sampling plan with Poisson distribution. A table is also constructed for the easy selection of the plan.

Since there are various difficulties associated with auscultation techniques (e.g., the detection/recognition of murmurs and sound tone changes within approximately one second), we have proposed an audio-visual based technique to examine and visualize heart sounds for both physicians and patients. To overcome auscultation difficulties, the technique can be used to assist in the understanding of the heartbeat, the detection of heart disease, and the digital database management of the auscultation examination. Auscultatory sounds are visualized by both an FFT image and a wavelet image to detect any abnormal heart sounds. A simple technique of pattern classification has been established using short-time Fourier transform and wavelet analyses to detect abnormal sounds. This new technique is expected to be simple and practical in this era of computer-managed clinical data. The result indicates that there is a possibility of developing a fully automatic detection system based on a map of standard sounds in the near future. A simple auscultation method is also expected to be developed for in-home use.

Decentralized power generation from renewable energy (RE) represents a significant transformation of the electricity industry especially to remote locations where grid connected is not viable. Out of the various RE, solar and wind had been identified to be the most promising RE to meet for future demands. However, due to the intermittency of these resources, back-up from other non-variable RE such as hydro and biomass or energy storage is essential to mainly the reliability of the system. This study is carried out to optimize the RE integrated electricity generation structure comprising of intermittent RE, non-variable RE and energy storage through the development of a MILP model. The technology selected for this study is solar power plant, biomass power plant, and bulk NaS battery system respectively. The model is then implemented in GAMS where the capacity of selected technologies, choice of RE for electricity generation, and scheduling of the designed system over a period of 24 hours was decided. The results shows that the required capacity of solar power plant is 1047 kW, biomass power plant is 403 kW and NaS battery of 691.94 kW. The total profit from this system over a life period of 25 years is $ 5,081,872.87.

In modern Industrial era the demand for electricity is increasing exponentially with each passing day. Distribution transformer is the most vital component for efficient and reliable distribution and utilization of electrical energy. With the increased demand in energy it has become essential for utilities to expand the capacity of their distribution networks significantly resulting in tremendous increase in demand of distribution transformers of various ratings. So the economic optimization by minimizing the mass of distribution transformer is of critical importance. This research paper focuses on the global minimization of the cost function of 3- phase core type oil immersed distribution transformer. The methodology used in this research work is based on nonlinear constrained optimization of the cost function subjected to various nonlinear equality and inequality constraints. The non-linear mathematical model comprising of the cost function and a set of constraints has been implemented successfully by using Mathematica software which provides a very robust and reliable computational tool for constrained nonlinear optimization that ensures the solution of the problem to be the global minimum. Finally, based on the above mentioned optimization technique, a 25 kVA 3-phase core type distribution transformer has been designed according to the latest specifications of PEPCO (Pakistan Electric and Power Company). It is found that the innovative optimization technique for transformer design that is developed during this research resulted in considerable cost reduction.

Clinical databases have accumulated large quantities of information about patients and their clinical histories. Data mining is the search for relationships and patterns within this data that could provide useful knowledge for effective decision-making. Classification analysis is one of the widely adopted data mining techniques for healthcare applications to support and improving the quality of medical diagnosis. This paper presents individual, ensembles and hybrid of computational intelligence techniques such as Support Vector Machine (SVM), Neural Networks (NN), Function Network (FN) and Fuzzy Logic (FL) to classify real bioinformatics datasets. The performance of the proposed computational techniques measured using well known bioinformatics datasets. As expected, the performance of the proposed ensembles and hybrid computational intelligence models is better compared to the monolithic models and overcome the weaknesses of existing classifiers particularly in the classification accuracy.

Machine breakdowns in a production schedule may occur on a random basis that make hard combinatorial problem of Job Shop Scheduling Problems (JSSP) becomes more complex. In this paper a new algorithm Fuzzy Tabu Priority List (FTPL) is proposed. Tabu search technique is applied to search optimal solution whereas FTPL is used to handle machine breakdowns. There are two tabu lists employed: one to keep moves during searching for optimal solution, another one is to keep broken machine if breakdown occurs. Period of how long the machine will be kept on the list is determined by fuzzy membership function. In order to avoid solution of being trapped into a local optimum Monte Carlo acceptance criterion is applied. Our techniques are tested to the benchmark data of JSSP available on the Operation Research library. From the experiment, we found that our algorithm is promising to help a decision maker to face the event of machine breakdowns.

This paper presents an application of Particle Swarm Optimization (PSO) technique to estimate energy demandof Turkey, based on economic indicators.The ec onomic indicators that are used during the model development are: gross national product (GNP), population, import and export figures of Turkey. Energy demand and other economic indicators in Turkey from 1979 to 2005 are considered as the case of this study. The energy estimation model based on PSO (EEPSO) is developed in two forms (linear (EEPSOL) and quadratic (EEPS OQ))and applied to forecast energy demand in Turkey. PSOQ form provided better-fit solution due to fluctuations of the economic indicators. In order to show the accuracy of the algorithm, some comparisons are made with previous studies which are using Ant Colony Optimization (ACO) and PSO. The future energy demand is calculated under different scenarios. The relative estimation errors of the proposed models are the lowe st when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.