Mohd Fathullah Ghazli@Ghazali
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Preferred name
Mohd Fathullah Ghazli@Ghazali
Official Name
Mohd Fathullah, Ghazli@Ghazali
Alternative Name
Fathullah, Mohd
Ghazli, Mohd
Ghazli, M. F.
Ghazali, Mohd Fathullah
Fathullah, G. M.
Main Affiliation
Scopus Author ID
57428935900
Researcher ID
AAK-5597-2020

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Removal of Cu from Printed Circuit Board (PCBs) Leachates using Activated Carbon Derived from Foxtail Palm Fruit

2020-09-22 , Maizatul N.N. , Mohd Fathullah Ghazli@Ghazali , Nik Yusoff N.R.

Printed circuit boards (PCBs) are the e-waste generated from the end-of-life electronic equipment such as laptops and mobile phone. PCBs contain relatively abundant of valuable metals such as gold and platinum. However, e-waste is considered as an environmental contaminant as it consists plenty of hazardous materials such as cadmium and copper which can pose health threat to human and also environment. It has been reported that PCBs contain a large amount of copper (Cu) in the circuit boards as it is used as a base metal. Moreover, exposure to Cu will lead to adverse impact of human health. Therefore, the objective of the study is to determine the Cu concentration using FAAS and also to remove the Cu from PCBs leachate using activated carbon derived from foxtail palm fruits. In order to remove the Cu, hydrometallurgical process on PCBs will be conducted to leach the metal into solution. Once the metal has been leached to the solution, the metal removal process using activated carbon through adsorption process was conducted. In this study, foxtail palm fruit was tested as an effective low-cost adsorbent for Cu removal. The effect of adsorbent dosage (1 g and 5 g) with fix contact time (40 min) of the prepared activated carbon in selected metal removal were investigated. The Cu in PCBs leachate solution before and after metal removal process were quantified using flame atomic absorption spectrophotometer (FAAS). Result obtained showed that, the percentage removal of Cu was recorded to be higher at high adsorbent dosage which was 14.417% in 5 g dose and 11.219% in 1 g dose. Thus, it can be concluded that the higher the adsorbent dosage used, the greater the percentage removal of Cu metal.

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Thermal Insulation and Mechanical Properties in the Presence of Glas Bubble in Fly Ash Geopolymer Paste

2022-01-01 , Shahedan N.F. , Mohd. Mustafa Al Bakri Abdullah , Norsuria Mahmed , Liew Yun Ming , Shayfull Zamree Abd. Rahim , Aziz I.H.A. , Kadir A.A. , Sandu A.V. , Mohd Fathullah Ghazli@Ghazali

The density, compressive strength, and thermal insulation properties of fly ash geopolymer paste are reported. Novel insulation material of glass bubble was used as a replacement of fly ash binder to significantly enhance the mechanical and thermal properties compared to the geopolymer paste. The results showed that the density and compressive strength of 50% glass bubble was 1.45 g/ cm3 and 42.5 MPa, respectively, meeting the standard requirement for structural concrete. Meanwhile, the compatibility of 50% glass bubbles tested showed that the thermal conductivity (0.898 W/mK), specific heat (2.141 MJ/m3K), and thermal diffusivity (0.572 mm2/s) in meeting the same requirement. The improvement of thermal insulation properties revealed the potential use of glass bubbles as an insulation material in construction material.

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P-nitrophenol degradation by gold nanoparticles augmented cellulosic microcapsules: Influence of catalyst dosage, reaction temperature and kinetic analysis

2023-03-23 , Osman M.S. , Fadzil S.S. , Khairudin K. , Bashah N.A.A. , Mohd Fathullah Ghazli@Ghazali , Nasrudin N.A.

For the parametric research of P-nitrophenol degradation (PNP) performance, the alginate encapsulating method was used to improve gold nanoparticles using cellulosic microcapsules. The goals are to investigate the gold nanoparticles's kinetic model in the degradation of PNP using UV-Vis spectrometry and to determine the parametric of the effect of AuNP-cellulose catalyst dose on efficiency of its removal and the role of AuNP-cellulose in PNP degradation at a variation of temperatures. The gold nanoparticle was formulated by reducing citrate technique and synthesised with coconut husk cellulose before being transformed into microcapsules and then formed into AuNP-cellulosic microcapsule beads. An excess quantity of sodium borohydride, NaBH4, was used as a model reaction to assess the catalytic performance of AuNPs for the reduction of PNP. The quantities of AuNP-cellulose catalyst have been weighted at 5, 7, and 10 mg for the dose of AuNP-cellulose catalyst on PNP elimination efficiency. With a 10 mg catalyst dose, the maximum PNP removal rate of 96.1 percent is achieved. When AuNP-cellulose reactions in degrading PNP are compared at different temperatures (28°C, 35°C, 45°C, and 55°C), the degradation is quicker at 55°C, taking just 5 minutes. The catalytic system's activation energy (Ea) for PNP degrading into PAP is 13.63 kJ/mol. This research indicates that as the catalyst dose is raised, the removal efficiency improves, and high temperatures accelerate PNP decrease.

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Warpage optimisation using recycled Polycar-bonates (PC) on front panel housing

2021 , Nur Aisyah Miza Ahmad Tamizi , Shayfull Zamree Abd. Rahim , Abdellah El-hadj Abdellah , Mohd. Mustafa Al Bakri Abdullah , Marcin Nabiałek , Jerzy J. Wysłocki , Bartłomiej Jeż , Paweł Palutkiewicz , Rozyanty Abdul Rahman , Mohd. Nasir Mat Saad , Mohd Fathullah Ghazli@Ghazali

Many studies have been done using recycled waste materials to minimise environmental problems. It is a great opportunity to explore mechanical recycling and the use of recycled and virgin blend as a material to produce new products with minimum defects. In this study, appropriate processing parameters were considered to mould the front panel housing part using R0% (virgin), R30% (30% virgin: 70% recycled), R40% (40% virgin: 60% recycled) and R50% (50% virgin: 50% recycled) of Polycarbonate (PC). The manufacturing ability and quality during preliminary stage can be predicted through simulation analysis using Autodesk Moldflow Insight 2012 software. The recommended processing parameters and values of warpage in x and y directions can also be obtained using this software. No value of warpage was obtained from simulation studies for x direction on the front panel housing. Therefore, this study only focused on reducing the warpage in the y direction. Response Surface Methodology (RSM) and Genetic Algorithm (GA) optimisation methods were used to find the optimal processing parameters. As the results, the optimal ratio of recycled PC material was found to be R30%, followed by R40% and R50% materials using RSM and GA methods as compared to the average value of warpage on the moulded part using R0%. The most influential processing parameter that contributed to warpage defect was packing pressure for all materials used in this study.

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Effect of tool engagement on cutting force for different step over in milling aisi p20 tool steel

2021-01-01 , Roshaliza Hamidon , Mohamed N.I. , Saravanan R. , Azmi H. , Zailani Zainal Abidin , Mohd Fathullah Ghazli@Ghazali

In mold production, end milling with tool path strategies is required for the process known as pocket operation. Different step overs involve depending on the type of tool path strategy used. Thus, different engagement will occur and leads to fluctuation of cutting force due to different step over during the process. However, most of study before focused on the effect of cutting speed, feed rate and depth of cut only in machining AISI P20. Thus, in this study, step over will be considered as one of the factor to improve machining force. The objective of this study are to evaluate the effect of cutting parameters and step over on cutting force and to study the behavior of cutting force for different tool engagement. A series of milling operation was carried out by varying cutting speed and feed rate. However, the depth of cut was set to 0.25 mm for each run. Step over with 100%, 75% and 50% were selected in this study. L27 Taguchi and S/N ratio were used to determine the significant factors that influence the result. Within the range of cutting parameters selected, feed rate were found to be the most significant parameters that influence cutting force. The highest cutting force found for 100% step over compared to 75% and 50% step over. According to the result, cutting force increased as the step over increased. In can be concluded that, step over is one of the important cutting parameter that affected machining output.

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Optimisation of warpage on plastic injection moulding part using response surface methodology (RSM) and genetic algorithm method (GA)

2017-09-26 , Miza A.T.N.A. , Shayfull Zamree Abd. Rahim , Mohd. Nasir Mat Saad , Mohd Fathullah Ghazli@Ghazali , Mohd. Hazwan Mohd. Hanid

In this study, Computer Aided Engineering was used for injection moulding simulation. The method of Design of experiment (DOE) was utilize according to the Latin Square orthogonal array. The relationship between the injection moulding parameters and warpage were identify based on the experimental data that used. Response Surface Methodology (RSM) was used as to validate the model accuracy. Then, the RSM and GA method were combine as to examine the optimum injection moulding process parameter. Therefore the optimisation of injection moulding is largely improve and the result shown an increasing accuracy and also reliability. The propose method by combining RSM and GA method also contribute in minimising the warpage from occur.

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HIRARC assessment on a catering worker: A case study

2023-04-24 , Mohd Fathullah Ghazli@Ghazali , Osman M.S. , Darmawan V.E.B. , Shahrolnizam M.A.F. , Muslim M.M. , Wasa T.A.T.A. , Ramli Y.N.

The costs and effects of twenty workplace accidents are reported in this report. The research was commissioned by the Health and Safety Authority and conducted by an institution in 2007. The purpose of the scientific research was to explore the impact of workplace accidents using a case study approach. The results are intended to enrich the prior quantitative studies provided by the Health and Safety Authority and to inform the Authority of future promotional campaigns. The methodological objective was to detect the financial, physical and psychological consequences that accidents at work may have for employers and employees. The technique involved interviewing and presenting the results as case studies of the injured staff and their employers (or agents).

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Feasibility of Multilayer Perceptron (MLP) Network to Correlate Air Quality Index (AQI) and COVID-19 Daily Cases

2023-01-01 , Abd Maruzuki M.I.F. , Tengku Zahidi T.S.A. , Khairudin K. , Osman M.S. , Jasmy N.F. , Abdul Hadi B. , Akbar M.S. , Saufie A.Z.U. , Mohd Fathullah Ghazli@Ghazali , Nor Syamsudin D.S. , Mohd Nazeri N.B.

A movement control order (MCO) was implemented in Malaysia starting from March 18th, 2020, as a pandemic control strategy that restricted all movement and daily outdoor activities to curb the transmission of COVID-19 pandemic. The most populated area in Malaysia, Petaling Jaya, Selangor, was selected to investigate the relationship between the COVID-19 outbreak and air pollution. Multilayer perceptron (MLP) model was used in this study to correlate air quality index (AQI) with COVID-19-related cases/deaths. The underlying hypothesis is that a pre-determined particulate concentration can encourage COVID-19 airborne transmission and make the respiratory system more susceptible to this infection. The in-silico strategy employed an innovative machine learning (ML) methodology, specifically MLP network using AQI data from the Department of Environment (DOE), Malaysia as input data and number of COVID-19 cases from the Ministry of Health, Malaysia as target data. The MLP model was trained for 10,000 times. Based on the results obtained, the model starts to converge after 1000 epochs with a small mean absolute error (MAE) (173.0–118.9) from day 1 to day 14. However, there is no definitive correlation between predicted COVID-19 patients and the AQI with respect to day configuration.

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Optimisation of shrinkage and strength on thick plate part using recycled LDPE materials

2021 , Norshahira Roslan , Shayfull Zamree Abd. Rahim , Abdellah El-hadj Abdellah , Mohd. Mustafa Al Bakri Abdullah , Katarzyna Błoch , Paweł Pietrusiewicz , Marcin Nabiałek , Janusz Szmidla , Dariusz Kwiatkowski , Joel Oliveira Correia Vasco , Mohd. Nasir Mat Saad , Mohd Fathullah Ghazli@Ghazali

Achieving good quality of products from plastic injection moulding processes is very challenging, since the process comprises many affecting parameters. Common defects such as warpage are hard to avoid, and the defective parts will eventually go to waste, leading to unnecessary costs to the manufacturer. The use of recycled material from postindustrial waste has been studied by a few researchers. However, the application of an optimisation method by which to optimise processing parameters to mould parts using recycled materials remains lacking. In this study, Response Surface Methodology (RSM) and Particle Swarm Optimisation (PSO) methods were conducted on thick plate parts moulded using virgin and recycled low-density polyethylene (LDPE) materials (100:0, 70:30, 60:40 and 50:50; virgin to recycle material ratios) to find the optimal input parameters for each of the material ratios. Shrinkage in the x and y directions increased in correlation with the recycled ratio, compared to virgin material. Meanwhile, the tensile strength of the thick plate part continued to decrease when the recycled ratio increased. R30 (70:30) had the optimum shrinkage in the x direction with respect to R0 (100:0) material where the shrinkage increased by 24.49% (RSM) and 33.20% (PSO). On the other hand, the shrinkage in the y direction for R30 material increased by 4.48% (RSM) and decreased by 2.67% (PSO), while the tensile strength of R30 (70:30) material decreased by 0.51% (RSM) and 2.68% (PSO) as compared to R0 (100:0) material. Validation tests indicated that the optimal setting of processing parameter suggested by PSO and RSM for R0 (100:0), R30 (70:30), R40 (60:40) and R50 (50:50) was less than 10%.

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Product defect prediction model in food manufacturing production line using multiple regression analysis (MLR)

2021-07-21 , Illa I.N. , Tan Chan Sin , Fadzli R. , Safwati Ibrahim , Rosmaini Ahmad , Mohd Fathullah Ghazli@Ghazali

This paper aims to develop an improved general mathematical model by focusing on human factors variables that related to the product defect in the manufacturing production line. This is because many studies found that almost 40% of total defects resulted from the operator error and the defects are usually not obvious and neglected. The objective to have defect prediction mathematical model to satisfy as early quality indicator of the manufacturing flow production line and assist the quality control team in manufacturing industries. Thus, the human factor variables will be investigate thoroughly and final model can be used to predict product defect on the line to improve product quality. Product defects quantity are identified and analyzed to determine the potential predictors for developing the mathematical model. A case study is offered that illustrates in a spice packaging semi-automated production line the effect that complexity variables have on assembly quality. By using Minitab, Multiple Regression analysis is conducted to model the relationship between the input variables towards response variables. From the analysis, the predicted data showed reasonable correlation with the observed data improved with adjusted R-Sq from 2.6% to 7.9%. Hence, the regression equation obtain is selected to be the prediction mathematical model for defects based on human factor input variables.

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