Ahmad Shukri Fazil Rahman
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Preferred name
Ahmad Shukri Fazil Rahman
Official Name
Fazil Rahman, Ahmad Shukri
Alternative Name
Rahman, Ahmad Shukri Fazil
Rahman, Ahmad Shukribin Fazil
Main Affiliation
Scopus Author ID
8328081000
Researcher ID
CQM-4923-2022

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Publication

Design of a hybrid controller for solar and ocean wave energy harvester

2017 , Ahmad Shukri Fazil Rahman

This thesis presents an approach of hybrid system implementation between Photovoltaic and ocean waves. These renewable energy sources are abundant, clean and beneficial compared to existing fossil fuel. Common method of extracting energy from these sources normally utilized a single energy source for energy production. Through hybrid system, two or more sources integration is possible. Merging of multiples energy sources will complement and support any inadequacy attribute accompanying these sources. However, system complexity will increase as source increases resulting in complicated system. Thus a proper controlling method is required for effective source management. Therefore, this research was initiated to develop a controller for two system harvester modules, Photovoltaic and wave energy converter for hybrid power system. The controller should fully exploit energy potential characteristic by harnessing it to the maximum. This research provides an effective method of harvesting Photovoltaic and ocean waves. Photovoltaic source is dependable toward sun intensity while the ocean waves’ intermittent energy is unsuitable through conventional harvesting method. The established controller will integrate Photovoltaic and ocean waves and compensate power fluctuations. Proper integration was successfully executed through buck and boost converter module. The wave energy converter module was developed using ratchet mechanism and the generator unit was extracted from mini ceiling fan motor. An additional monitoring system was added and performs wireless transmission to operator. The developed Photovoltaic and Wave Energy Converter (WEC) sources progress rapidly with average power produced are 76.91mW and 82.237W respectively. The proposed controller excels in performance and produce effective hybrid energy management with measured with power extraction efficiency at 58%. The hybrid system was successfully executed within the prescribe scope boundaries.

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Induction Heating as Cleaner Alternative Approach in Food Processing Industry

2021-06-11 , Abdul Rahim Abdul Razak , Norlia Mohamad Ibrahim , Ahmad Shukri Fazil Rahman , Fayzul M. , Azizan M.M. , Uda Hashim , Basir I.

Food processing of fucuk making from soy milk from conventional method is evaluated as the case study. The cost, time, fuel supply security and environmental issue anticipated within the process had called for a new approach innovation. Electromagnetic induction heating has been distinguished between other technologies for the purpose. The design, test, analysis and field test of the proposed system has been presented in this report. The installed system seems to outfit and satisfy the industry requirements and can be expanded to other food processing field as well. The advantage and limitation are also discussed within the report.

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Techno-Economic Feasibility to Generate Electricity by Using PSO Technique for the Urban City in Iraq: Case Study

2020-01-01 , AL-Shammari Z.W.J. , Azizan M.M. , Ahmad Shukri Fazil Rahman

For developing nations such as Iraq, electricity access in rural areas, especially those which are remote, is limited. Thus, the present study explores the electrical needs of the city of Zerbattiya, Iraq. The proposed system's components include solar panels, wind turbines, diesel generators, and batteries. This research proposes a techno-economically feasible and optimal sizing for each component to generate electricity for the city. Particle swarm optimization (PSO) algorithm was used in this research by using MATLAB. The ideal setting of a hybrid renewable energy system (HRES) is achieved by considering the lowest possible cost of energy (COE) with the highest reliability (REL) and possible value of renewable energy penetration (REP). Results showed that the respective optimal values for NPV (30), NWT (30), NDG (3), NBT (281), cost of energy (COE) was (0.142 US$/KWh), loss of power supply probability (LPSP) was (0.0534 %), reliability (REL) was (99.9466 %) and renewable energy penetration (REP) was (56.35 %). The findings further demonstrate that the algorithm was able to achieve optimal solutions to reduce overall cost, quickly and accurately. In conclusion, implementation of HRES was found to be an apt method of meeting electrical needs of remote rural areas in Iraq, and other developing nations with similar climates.

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Optimal sizing of a hybrid system through particle swarm optimization for rural areas in Iraq

2022-11-01 , Al-Shammari Z.W. , Algeboory A.H. , Al-Jebory S.H. , Taha I.A. , Almukhtar H. , Azizan M.M. , Ahmad Shukri Fazil Rahman , Hasikin K.

In today's modern world, any community has the right to access basic electricity. With this in mind, efforts are being made to provide electric power to even the most remote locations. Solar and wind energy are examples of renewable energy sources that are both clean and versatile. For a distant rural school in south-eastern Iraq, this research presents particle swarm optimization (PSO) to reduce the cost of energy (COE) according to the maximum dependability of a hybrid renewable energy system (HRES) by utilizing an integrated electrical generation system. The suggested hybrid system consists of photovoltaics (PV), wind turbines (WT), and batteries (BT), all of which are subject to a specific investment restriction. Results showed that the optimal sizing of the number of photovoltaics (NPV) is equal to (9), the number of wind turbines (NWT) equal to (6), the number of batteries (NBT) of (29), the cost of energy (COE) (0.536 US$/kwh), loss of power supply probability (LPSP) (0.091%), reliability (REL) (99.909%) and renewable factors (RF) (100%) with (59%) PV penetration, and (41%) WT penetration. As a result, the use of the hybrid system is justified from a technological, economic, and humanitarian standpoint.

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Assessment of renewable energy sources to generate electricity for remote areas, South Iraq

2022-12-01 , Kother A.H. , Jawad Z.W.A.S.W. , Kother S. , Taha I.A. , Almukhtar H. , Azizan M.M. , Ahmad Shukri Fazil Rahman , Hasikin K.

With the rising need for utilizing renewable energy instead of traditional energies in electricity generation across the world, a broad assessment of these energies' performance is required to make the most of them everywhere. This paper looks at the techno-economics of renewable energy resources for a distant health clinic in a rural location of southern Iraq. Cost, dependability, and availability are the parameters that were considered in this study, which took into consider the power load in this scenario. Because of its efficacy, the particle swarm optimization (PSO) technique was chosen for the suggested study. Results showed that the respective optimal values for number of photovoltaics (NPV) equal to (10), number of wind turbines (NWT) equal to (5), and number of batteries (NBT) of (33), cost of energy (COE) of (0.518 US$/kWh), loss power supply probability (LPSP) of (0.073%), reliability (REL) of (99.927%) and renewable factors (RF) of (100%) with (66 %) solar energy penetration, and (34%) wind energy penetration. Finally, it was discovered that implementing a hybrid renewable energy system (HRES) is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates.

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Optimal sizing of standalone for hybrid renewable energy system by using PSO optimization technique

2023-03-01 , Al-Shammari Z.W.J. , Azizan M.M. , Ahmad Shukri Fazil Rahman , Hasikin K.

Providing electricity to rural regions is difficult for developing countries, such as Iraq, particularly in remote parts without grid connections. The electrical demands of Zerbattiya, a community in southern Iraq near the Iranian border, are discussed in this paper. The proposed system includes wind turbines, solar panels, diesel engines, batteries, etc. This study suggests a techno-economic viable and optimal size for each component to generate electricity for this area. This research uses particle swarm optimization techniques (PSO). The best hybrid renewable energy system (HRES) design is achieved by balancing the lowest possible cost of energy (COE) with the lowest possible loss of power supply probability (LPSP) and the greatest possible reliability factor value. As a result of the findings, the respective ideal values of number of photovoltaics (NPV), number of wind turbines (NWT), number of diesel generator (NDG), number of batteries (NBT), COE, LPSP, and reliability are 138, 43, 2, 324, US$/KWh 0.129, 0.0508%, and 99.9492%, respectively. Finally, it was discovered that implementing a HRES is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates.

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Corneal arcus classification for hyperlipidemia detection using gray level co-occurrence matrix features

2020-01-07 , Ramlee R.A. , Subramaniam S.K. , Shamshul Bahar Yaakob , Ahmad Shukri Fazil Rahman , Saad N.M.

The arcus cornea is an eye problem that is often encountered among older people, but the situation is risky to teenagers and young people, in which it gave a sign the presence of lipid abnormalities in their blood and the risk of disease problems such as diabetes and heart disease. This paper presents the classification of the arcus cornea, using the extraction of texture features of the gray level co-occurrence matrix, along with several models of the classifiers, namely as scale conjugate gradient, Bayesian regulation, and Levenberg-Marquardt. Percentage fractions for training, testing and validation for classifier are 70%, 15%, and 15% respectively. The comparison of the classifiers used by the past researchers for classification the eye abnormalities, also were analyzed and studied in this work. In this experiment, a total of 125 image eyes were used, consisting of two classes of the eye image, which is normal and abnormal. The best result demonstrated in this proposed framework using Bayesian regulation classifier is, a sensitivity of 96%, and a specificity of 100%. However, this classifier did not achieve perfectly classification or an accuracy of 100%. Nevertheless, it is able and evident that the system is effective by the output of 98.4% accuracy.

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