ABSTRACT

This paper aims to focus on the selection of TESOL research participants. It was noted that many EFL postgraduate students face problems regarding the selection of participants for their TESOL research. Therefore, this paper reviews the literature on sampling research in general and within the discipline of TESOL. The paper clarifies the terms and strategies used in selecting research participants. By understanding and applying the principles and methods of sampling designs, EFL postgraduate students can enhance the validity and reliability of their research findings and contribute to the advancement of knowledge in the field of TESOL research. Keywords: TESOL (Teaching English to Speakers of Other Languages), Selection of research participants, EFL post graduate students

ABSTRACT

This article presents another method to improve the performance

and efficiency of a gas-fired power plant in Al-Zawia, Libya. In

order to improve power plant efficiency and increase power, the

principle of a heat recovery steam generator (HRSG), which is

mainly based on the waste heat energy of exhaust gases generated

by a simple gas turbine unit, is a method that can be used for

improvement. The present system was simulated using Aspen

HYSYS software. For validation purposes, the simulation results

were compared with the actual operating data, net power and

thermal efficiency of the cycle under the same conditions. These

results show a high level of agreement between theoretical and

practical data. This agreement enables and provides good

implementation to use this software to simulate an entire combined

cycle. In addition, operating parameters that influence power plant

performance were examined. The results show that the combined

cycle improved the thermal efficiency and net power output by

49.8% and 50%, respectively, under the same operating conditions.

The results also showed that compressor pressure ratio and inlet air

temperature have significant effects on thermal efficiency and net

power produced in both simple and combined power plants

Net Zero Energy (NZE) buildings play a crucial role in meeting the Sustainable Development Goals (SDG) and creating environmentally friendly residential areas. These buildings are designed to generate as much energy as they consume, resulting in a net balance of zero energy consumption from the grid. By integrating innovative technologies and sustainable design principles, NZE buildings minimize their carbon footprint and contribute to a more sustainable future. The acquired result has been presented and discussed. The concept of Net Zero Energy Buildings (NZEBs) has gained significant attention in recent years as a crucial strategy for achieving sustainability in the built environment. NZEBs are designed to produce as much energy as they consume, resulting in a net energy balance of zero over a specified period.

Abstract—Fault detection and classification is critical to the reliability of modern control systems in different industries, where detecting and classifying faults in operational processes are very important things while failure to detect and classify them, may cause irreparable damage. In this paper, fault detection and classification approach is presented. The first step, multi stage recursive least squares parameter estimation approach for controlled autoregressive autoregressive moving average systems (CARARMA) is developed with a view to estimate the parameters of the system, additionally, improve the effectiveness of the computation. By means of multi stage approach, the (CARARMA) system is decomposed into three simple identification models, and the parameters of each simple model is identified one by one. These parameters estimated by this approach are referred to as features, and not all of them have the same useful data about the system. In order to select the valuable features and improve a classification accuracy, the Linear Discriminant Analysis (LDA) approach based on scattering matrices is applied for dimension reduction. The classification between these reduced classes is done based on the Naive Bayes classifier. Finally, the obtained results explain the performance of this proposed approach.

The classical Model Predictive Control (MPC) is still considered in many industrial applications, although advanced control methods have seen  significant development over the last few years. However, a lot of MPC strategies still suffer from  robustness to cope with a variety of process dynamics. For the MPC controller to work effectively,  it must be properly tuned. However, MPC is challenging and there is no ideal analytic method to  obtain exact solutions that result in the best desired responses. Based on that, this paper  investigate the robustness performance for a MPC tuning strategy. Three formulae were derived  (Proposed , Proposed  and Proposed ). These formulae are used for calculating the suppression coefficient. The three  formulae were derived by fitting the optimal empirical suppression coefficients for varies process  dynamics commonly found in industry. Simulation results show that the use of the proposed strategy results in good performance compared to other strategies previously proposed. The proposed   strategy also demonstrated a robust performance with respect to modelling errors of process  parameters. The results demonstrate the effectiveness and validity of proposed strategy when  compared with conventional strategies.