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| 1 | Conversion of Malaysian low-rank coal to mesoporous activated carbon:Structure characterization and adsorption properties显示文摘Malaysian Selantik low-rank coal(SC)was used as a precursor to prepare a form of mesoporous activated carbon(SC-AC)with greater surface area(SA)via a microwave induced KOH-activation method.The characteristics of the SC and SC-AC were evaluated by the iodine number,ash content,bulk density,and moisture content The structure and surface characterization was carried out using pore structure analysis(BET),scanning electron microscopy with energy dispersive X-ray spectroscopy(SEM-EDX),X-ray diffraction(XRD),Fourier Transform Infrared(FTIR),elemental analysis(CHNS),thermogravimetric analysis(TGA),and determination of the point of zero charge(pH(PZC)).These results signify a mesoporous structure of SC-AC with an increase of ca.1160 times(BET SA=1094.3 m^2·g^-1)as compared with raw SC without activation(BET SA=1.23 m^2·g^-1).The adsorptive properties of the SC-AC with methylene blue(MB)was carried out at variable adsorbent dose(0.2-1.6 g·L^-1),solution pH(2-12),initial MB concentrations(25-400 mg·L^-1),and contact time(0-290 min)using batch mode operation.The kinetic profiles follow pseudo-second order kinetics and the equilibrium uptake of MB conforms to the Langmuir model with a maximum monolayer adsorption capacity of 491.7 mg g^-1 at 303 K.Thermodynamic functions revealed a spontaneous endothermic adsorption process.The mechanism of adsorption included mainly electrostatic attractions,hydrogen bonding interaction,andπ-πstacking interaction.This work shows that Malaysian Selantik low-rank coal is a promising precursor for the production of low-cost and efficient mesoporous activated carbon with substantive surface area. | Ali H.Jawad Khudzir Ismail Mohd Azlan Mohd Ishak Lee D. Wilson | 2019 | Chinese Journal of Chemical Engineering2019，27，7： | 7 |
| 2 | A Preliminary Assessment of Load Consumption and Solar Power Potential at Kota Belud,Sabah显示文摘This paper presents a detailed preliminary assessment of load consumption and solar power potential at the Eco-Tourism Centre of Liogu Ku Silou-Silou(EPLISSI),Kota Belud,Sabah.This initial investigation assessed the feasibility of an off-grid solar PV system at EPLISSI with a suitable solar panel system for project installation and commissioning purposes.Due to the absence of an electrical grid and power supply,no pre-existing electrical appliances could be found in EPLISSI.Hence,an excel-based software,the ESCoBox,was used to produce the load profiles.The input data for this software came from a list of required electrical appliances(LED lights,fans,and phone chargers)and the historical frequency of visitors to EPLISSI.Meanwhile,to assess the solar power potential at EPLISSI,an online simulator known as Global Solar Atlas version 2.3 or GSA 2.3 was used.As an input for the GSA 2.3,the initial solar panel system capacity was set for 0.5 kWp,and then an increment of 0.1 kWp was entered until specific criteria were met.The selection of the suitable size is made when the system can satisfy the daily total average load demand and a specific load fulfillment demand.As a result,it was found that the site requires a total average demand and a total peak demand of 4.60 and 11.87 kWh/day,respectively.From the GSA 2.3 generated report,an off-grid solar PV system with the capacity of 2.50 kWp solar PV can satisfy the daily total average load demand of this area,where the average PV energy output is within the range of between 7.74–9.80 kWh/day or an average of 8.72 kWh/day.In conclusion,this preliminary assessment indicates that installing an off-grid solar PV system in this area is possible. | Maryon Eliza Matius Mohd Azlan Ismail Adriana Erica Amaludin Yan Yan Farm Mohd Adzrie Radzali Ahmad Fazlizan Wan Khairul Muzammil | 2022 | Energy Engineering2022，119，4： | 1 |
| 3 | Application of response surface methodology (RSM) for optimization of cassava starch grafted polyDADMAC synthesis for cationic properties显示文摘 | Mohd A. A. Razali Nurhasikin Sanusi Hanafi Ismail Nadras Othman Azlan Ariffin | 2012 | Starch/St?rke2012，，12： | 1 |
| 4 | One-step process for production of biodiesel from Jatropha curcas L. seeds: Optimization using Response Surface Methodology (RSM)显示文摘 | Asnida Yanti Ani Mohd Fauzi Abdullah Mohd Azlan Mohd Ishak Khudzir Ismail | 2009 | Journal of Chemistry and Chemical Engineering2009，3，12： | 0 |
| 5 | Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction显示文摘Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)algorithms.This paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas industry.The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level indicator.The accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to zero.The performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and F-measure.Results showed that the sensitivity of all classifiers increases as the imbalance rate increases.SVM with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN. | Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain | 2023 | Computers, Materials & Continua2023，，6： | 0 |
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