Application of Stochastic and Deterministic Techniques for Uncertainty Quantification and Sensitivity Analysis of Energy Systems

Sensitivity analysis (SA) and uncertainty quantification (UQ) are used to assess and improve engineering models. In this study, various methods of SA and UQ are described and applied in theoretical and practical examples for use in energy system analysis. This paper includes local SA (one-at-a-time linear perturbation), global SA (Morris screening), variance decomposition (Sobol indices), and regression-based SA. For UQ, stochastic methods (Monte Carlo sampling) and deterministic methods (using SA profiles) are used. Simple test problems are included to demonstrate the described methods where input parameter interactions, linear correlation, model nonlinearity, local sensitivity, output uncertainty, and variance contribution are explored. Practical applications of analyzing the efficiency and power output uncertainty of a molten carbonate fuel cell (MCFC) are conducted. Using different methods, the uncertainty in the MCFC responses is about 10%. Both SA and UQ methods agree on the importance ranking of the fuel cell operating temperature and cathode activation energy as the most influential parameters. Both parameters contribute to more than 90% of the maximum power and efficiency variance. The methods applied in this paper can be used to achieve a comprehensive mathematical understanding of a particular energy model, which can lead to better performance.Comment: References list is updated, the paper is converted to a more professional and easy to read template, the technical content stays the sam

Applications of Mining Arabic Text: A Review

Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches

The Impact of the Level of Political Stability on Sustainable Development A Case Study of the Countries of the Arab Spring Revolutions (2006-2018)

The study aims to identify the impact of the level of political stability on sustainable development in the countries of the Arab Spring revolutions during the period (2006-2018 AD), The study assumes that there is a positive relationship between political stability as an independent variable and sustainable development as a dependent variable.The study uses a systems analysis approach and the descriptive and analytical and the Statistical approach. The study concluded to prove the hypothesis that there is a statistically significant correlation between the level of political stability and indicators of sustainable development in the countries of the Arab Spring revolutions.It recommended the necessity of working to achieve political and economic reform in the Arab countries in a way that leads to a successful democratic transformation process that helps achieve political stability and sustainable development goals

The Political Implications of the Evolution of the Electoral System in Jordan

This study highlights the impact of the electoral system on the nature of the political system and applying it to the Jordanian case. The study followed the systems-analysis approach as the development of the electoral systems becomes an input that triggers change in the political system environment. Especially, In regard to the representation and the power of the political parties, levels of political participation, woman representation, fairness of parliamentary representation and the conflict management within the political system. The study concluded that the pattern of the electoral system translates the orientations of the political system and the objectives it seeks to achieve. In addition, the reform of the political system necessitates the reform or change of the electoral system. In the Jordanian case, the electoral system has witnessed a notable development, especially in the laws of 2012 and 2016, but their contribution to the promotion of the democratic levels of the political system remains marginal. Keywords: Electoral System, Elections, Political System, Political Implications, Democracy, Jordan. DOI: 10.7176/JLPG/99-12 Publication date:July 31st 202

Sequential set-point control of thermostatic loads using extended Markov chain abstraction to improve future renewable energy integration

Additional flexible resources are required to achieve resilience and sustainable power systems. Challenges emerged due to the increasing amounts of renewable generation penetrations at both the bulk power system and the distribution sides. System operators are required to deal with higher levels of variable and uncertain power outputs for various time-scales. Moreover, replacing existing thermal units with other inertial-less technologies, make the system sensitive to even small contingencies. Demand-side control is becoming an ingredient part of our future power system operation. Effective utilization of demand-side resources can make the system more elastic to integrate the future renewable plans. To help in resolving these challenges, this work develops a demand-side control framework on the Thermostatically Controlled Loads (TCLs) to support the grid with minimal impacts on customers\u27 comfort and devices\u27 integrity. The Markov chain abstraction method is used to aggregate the TCLs and describe their collective dynamics. Statistical learning techniques of hidden Markov chain analysis is used to identify the parameters of the resulting Markov chains at fixed temperature set-points. Various sensitivities are conducted to reveal the optimal Markov chain representation. To allow extracting or storing additional thermal energy, this thesis develops an Extended Markov Model(EMM) which describes devices\u27 transition when a new set-point is instructed. The results have shown that the EMM is able to capture both devices\u27 transient and steady-state behaviors under small and large set-point adjustments. Parameters heterogeneity affects the accuracy of the EMM model. In contrast to what proposed in the literature, more comprehensive heterogeneous parameters are defined and considered. The K-mean clustering approach is proposed in our analysis to minimize the heterogeneity error. Devices are divided into multiple clusters based on the power ratings and cycling characteristics. The results have shown that clustering highly improves the EMM performance and minimize the heterogeneity errors. Under temperature set-point control the TCLs\u27 aggregated power experience two main challenges before it converges to the new steady-state value, the abrupt load change, and the power oscillations. This is due to devices\u27 synchronous operations once a new operating set-point is ordered. Such power profiles may cause serious stability issues. Therefore, Model Predictive Control (MPC) with direct ON/OFF switching capability is proposed to apply the set-point control sequentially and prevent any possible power oscillations. The MPC can determine the optimal devices\u27 flow toward the new operating set-point. The results have shown that the proposed modeling and control approaches highly minimize the required switching actions. Control actions are required only during the transition between the set-points and finally converges to zero when all devices reach the new set-point setting. In contrast, the models proposed in the literature require very high switching rates which can cause damage or reducing devices\u27 life expectancy. The last part of this thesis proposes a dispatching framework to utilize the TCLs\u27 flexibility. The developed modeling and control techniques are used to support the grid with three demand response ancillary services. Namely, spinning reserves, load reduction, and load shifting. The three ancillary services are designed as demand response programs and integrated into the Security Constrained Unit Commitment (SCUC) Problem. Three participation scenarios are considered to evaluate the benefits of aggregating the TCLs in the day-ahead markets

Attribute Set Weighting and Decomposition Approaches for Reduct Computation

This research is mainly in the Rough Set theory based knowledge reduction for data classification within the data mining framework. To facilitate the Rough Set based classification, two main knowledge reduction models are proposed. The first model is an approximate approach for object reducts computation used particularly for the data classification purposes. This approach emphasizes on assigning weights for each attribute in the attributes set. The weights give indication for the importance of an attribute to be considered in the reduct. This proposed approach is named Object Reduct by Attribute Weighting (ORAW). A variation of this approach is proposed to compute full reduct and named Full Reduct by Attribute Weighting (FRAW).The second proposed approach deals with large datasets particularly with large number of attributes. This approach utilizes the principle of incremental attribute set decomposition to generate an approximate reduct to represent the entire dataset. This proposed approach is termed for Reduct by Attribute Set Decomposition (RASD).The proposed reduct computation approaches are extensively experimented and evaluated. The evaluation is mainly in two folds: first is to evaluate the proposed approaches as Rough Set based methods where the classification accuracy is used as an evaluation measure. The well known IO-fold cross validation method is used to estimate the classification accuracy. The second fold is to evaluate the approaches as knowledge reduction methods where the size of the reduct is used as a reduction measure. The approaches are compared to other reduct computation methods and to other none Rough Set based classification methods. The proposed approaches are applied to various standard domains datasets from the UCI repository. The results of the experiments showed a very good performance for the proposed approaches as classification methods and as knowledge reduction methods. The accuracy of the ORAW approach outperformed the Johnson approach over all the datasets. It also produces better accuracy over the Exhaustive and the Standard Integer Programming (SIP) approaches for the majority of the datasets used in the experiments. For the RASD approach, it is compared to other classification methods and it shows very competitive results in term of classification accuracy and reducts size. As a conclusion, the proposed approaches have shown competitive and even better accuracy in most tested domains. The experiment results indicate that the proposed approaches as Rough classifiers give good performance across different classification problems and they can be promising methods in solving classification problems. Moreover, the experiments proved that the incremental vertical decomposition framework is an appealing method for knowledge reduction over large datasets within the framework of Rough Set based classification

The application of social responsibility in Jordanian banks and its impact on the competitive feature from the point of view of banks’ employees

ABSTRACTThe study aims to identify the reality of the application of social responsibility in Jordanian banks, and its impact on competitive advantage. It also seeks to determine the most important pillars of the Jordanian banks that facilitate the success of social responsibility with a view to achieve its objectives in favor of the related parties. This happens through trying to identify the extent of the application of social responsibility within the nine following dimensions: community, environment, customers, employees, shareholders, government, suppliers, competitors, minorities and people with special needs. In order to achieve that, the researchers selected a random sample of 170 employees of the study population and the bank employees in the scope of various aspects of their work. Questionnaires were also distributed to managers of banks directorates and branches; they included 45 paragraphs about social responsibility and 16 paragraphs about competitive advantage. Data were integrated into the computer and processed using SPSS statistical program. The study concluded that social responsibility is a subject of interest, along with competitive advantage for banks. It also found out that there is a relationship between social responsibility and competitive advantage among directorates and branches under study

Dewaterability of sludge digested in extended aeration plants using conventional sand drying beds

Dewaterability of unconditioned sludge digested in full scale and lab scale experiments using either extended aeration (EA) or anaerobic digestion were compared on full and lab scale sand drying beds. Sludge digested in EA plants resulted in improvement in sludge dewaterability compared to  sludge digested anaerobically. This was demonstrated by comparing capillary suction time, time to filter a specific amount of water, the sludge volume index and the dry solids content. In addition, sieve analysis results from both types of sludge after drying in sand drying beds clearly shows that the grain portions in the fine range in case of anaerobically digested sludge are more than that in case of EA sludge. This was also clear in microscopic photos of samples. The microscopic photos of EA stabilized sludge are characterized by larger colonies of flocs and more open structure than anaerobically digested sludge