Rustom Mamlook

Multi-criteria selection of electric power plants using analytical hierarchy process

Abstract This paper uses analytical hierarchy process (AHP) methodology to perform a comparison between the different electricity power production options in Jordan. The systems which were considered, in addition to fossil fuel power plants, are nuclear, solar, wind, and hydro-power. Results on cost-to-benefit ratios show that solar, wind, end hydro-power may be the best alternatives for electric power production. Nuclear electricity turns out to be the worst choice, followed by fossil fuel electric power.

Fuzzy sets programming to perform evaluation of solar systems in Jordan

Abstract This paper uses fuzzy set methodology to perform the comparison between different solar systems for various applications. The aim of the paper is to determine the order in which solar systems should be given higher priority to be used in Jordan. The systems considered are solar distillation, solar water heating, solar space heating and ventilation, solar water pumping, photovoltaics and solar electric power production. They were compared according to their benefits and costs. Based on benefit to cost ratios, the results show that solar distillation is found to be the best choice and should be given the highest priority in terms of research and development. It is followed by the solar pond for electric power production, then, solar water pumping followed by solar space heating and photovoltaics. Finally, solar water heating may not need further development.

A neuro-fuzzy program approach for evaluating electric power generation systems

This paper uses neuro-fuzzy programming to perform a comparison between the different electricity power generation options for Jordan. Different systems are considered: in addition to fossil fuel power plants, nuclear, solar, wind, and hydropower systems are evaluated. Based on cost-to-benefit ratios, results show that solar, wind, and hydropower are considered to be the best systems for electricity power generation. On the other hand, nuclear electricity turns out to be the worst choice, followed by fossil fuel electric power.

Testing of two models for computing global solar radiation on tilted surfaces

Solar radiation data computerly measured at Amman, Jordan was used to verify the performance accuracy of two models for estimation of global radiation on tilted surfaces. The measurements used in this study were the daily totals of global radiation on a horizontal surface, diffuse radiation on a horizontal surface and global radiation on a 45° tilted surface. The measured radiation using microprocessor based weather station was compared with the computerized results obtained from two widely used models for estimating total radiation on tilted surfaces. These are the isotropic model and Hay’s anisotropic model. The study showed that the models are in good agreement with the isotropic model producing slightly better results during the summer months. Hay’s model resulted in better results for the rest of the year.

An advanced fuzzy controller

A methodology for the reduction of complexity of the fuzzy if-then rules for a robust controller system is presented. An analysis of the fuzzy if-then rules is performed by viewing the fuzzy sets in the rules as crisp sets. The knowledge of the analysis is used to reduce the number of variables and the number of fuzzy if-then rules. By applying fuzzy logic to the reduced linguistic if-then rules, a robust controller system based on fuzzy if-then rules is reconstructed. The simulation results show that the reconstructed controller is more efficient and performs as well as the original controller.

Toward clean environment: evaluation of solar electric power technologies using fuzzy logic

The rapid expansion of the use of solar energy power plants worldwide is a subject that is being followed with interest. Fuzzy logic methodology is used for evaluating the solar thermal power technology, it compresses huge amount of data into smaller sets, and it has the ability to decide between different solar technologies on the basis of their benefits and costs. The most often considered solar technologies were parabolic trough, central receiver, dish sterling engine, compact linear Fresnel reflector (CLFR), solar chimney, photovoltaic (PV), and solar pond. The aim of our research is to provide the needed information to make a judgment or a decision of adopting the most preferred solar technology in terms of installation and development using fuzzy set methodology. The criteria of the evaluation were based on different parameters, i.e., power capacity, efficiency, availability, capacity factor, storage capability, cost, maturity, water usage, land usage, and safety. The key barriers and features for each technology on the basis of benefit-to-cost ratios are addressed. The results showed that CLFR was found to be the best choice in terms of research, development, and implementation, followed by parabolic trough technology, then the central receiver technology, dish sterling engine, solar chimney, PV, and solar pond, according to the order of preference.

Evaluation of Solar Electric Power Technologies in Jordan Using Fuzzy Logic

Jordan is considered one of the sun-belt countries, which possesses high solar radiation on its horizontal surface. The present study will be concerned on the uses of fuzzy sets methodology to perform evaluation between the most suitable solar technologies for power generation in Jordan, namely, solar ponds and photovoltaic (PV) technologies. The criterion of the evaluation were based on different parameters, i.e., power capacity, efficiency, availability, capacity factor, storage capability, cost, maturity, land usage and safety, they are planned as the technologies for the near foreseen term. Based on benefit to cost ratios, the results showed that photovoltaic technology found to be the better choice in terms of generating electricity, research and development and more effective programs of support and installation.Copyright

Fuzzy logic image-clustering algorithm

A fuzzy logic clustering algorithm to classify a given image into targets and backgrounds is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. The algorithm features an adaptive mechanism for selecting the number of clusters, and it features an adaptive threshold. The problem of threshold selection is considered and the convergence of the algorithm is shown. The algorithm also does not require the number of clusters been known a priori. An example is given to illustrate the application of the algorithm.

Self-organizing k-means classification algorithm

A self-organizing k-means algorithm to classify the inputs (data) into classes is presented. This algorithm provides solutions to the problems that the k-means classification algorithm faces. The k-means classification algorithm has the problem of selecting the threshold(s). It also requires that the number of classes be known a priori. This algorithm forms clusters, removes noise, and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of input data. The algorithm consists of two phases. The first phase is similar to the Carpenter/Grossberg classifier, and the second phase is a modified version of the k-means algorithm. An example is given to illustrate the application of this algorithm and to compare this algorithm with the k-means algorithm.