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Ahmed Z. Al-Garni

Bio: Ahmed Z. Al-Garni is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Weibull distribution & Solar still. The author has an hindex of 18, co-authored 85 publications receiving 1436 citations.


Papers
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Journal ArticleDOI
01 Nov 1997-Energy
TL;DR: Autoregressive integrated moving average (ARIMA) models were developed using data for 5 yr and evaluated on forecasting new data for the sixth year as mentioned in this paper, and the optimum model derived is a multiplicative combination of seasonal and non-seasonal autoregressive parts, each being of the first order, following first differencing at both the seasonal and nonseasonal levels.

177 citations

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TL;DR: In this paper, the mechanical and metallurgical properties of friction-welded steel-aluminum and aluminium-copper bars are investigated using electron and optical microscopy.

134 citations

Journal ArticleDOI
TL;DR: In this paper, the tribological and mechanical properties of plasma-nitrided Ti6Al-4V alloy have been investigated and it was found that the wear resistance improved considerably after the nitriding process.
Abstract: The present study was conducted to investigate the tribological and mechanical properties of plasma-nitrided Ti6Al4V alloy. Specimens were nitrided in an H2N2 (1:8 ratio) plasma. The nitrogen concentration along the nitrided zone was obtained using the nuclear reaction analysis technique. The workpiece temperature was varied from 450 to 520 °C during the nitriding process. Pin-on-disc wear tests were carried out to evaluate the wear properties of the resultant samples and a ball-on-disc experiment was conducted to measure the friction coefficient. Microhardness tests, Scanning electron microscopy and X-ray diffraction were carried out to investigate the phases developed in the nitrided zone. It was found that the wear resistance improved considerably after the nitriding process. Three distinct layers were identified: (i) an inner layer where δ-TiN + e-Ti2N phases formed, (ii) an intermediate layer where α-(TiN) with or without e phase developed and (iii) an outer layer where precipitations were dominant.

124 citations

Journal ArticleDOI
TL;DR: In this paper, a nonlinear dynamic model of an overhead crane which represents simultaneous travel, traverse, and hoisting/lowering motions is considered, and numerical results are obtained in such a way that specified boundary conditions and the functional constraints for the states and controls are satisfied while minimizing the sway and final time.

97 citations

Journal ArticleDOI
TL;DR: An artificial neural network model is developed to relate the electric energy consumption in the Eastern Province of Saudi Arabia to the weather data, global solar radiation and population, and comparison with a regression model shows that the neural networkmodel performs better for predictions.

80 citations


Cited by
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Journal Article
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

1,814 citations

01 Jan 2016

1,633 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a review of recent developed models for predicting building energy consumption, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods, and further prospects are proposed for additional research reference.
Abstract: The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.

1,509 citations

Journal ArticleDOI
TL;DR: In this paper an attempt is made to review the various energy demand forecasting models to accurately predict the future energy needs.
Abstract: Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

1,002 citations