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A Supported Titanium and Copper Based Catalyst for Desulphurization Reaction

Junaidi Mohamad Nasir, Wan Azelee Wan Abu Bakar* and Mohd Yusuf Othman*

Department of Chemistry, Faculty of Science, Universiti Tenologi Malaysia,

81310 UTM Skudai, Johor, Malaysia.

Natural gas is used as one alternative way to generate the largest scale of energy especially for electricity in the world. Hence, many oil and gas companies try to explore a new crude natural gas well for maximizing production of LNG. Malaysian crude natural gas contains various gases components including methane (40-50 %), ethane (5-10 %) and propane (1-5 %). However, this crude natural gas also contains toxic and acidic gases components such as H2S (1–5 %) and CO2 (20-30 %) which has the ability to corrode carbon steel used in the natural gas pipeline system and material in the processing plant. Fe3+/Zn2+/Cu2+/Al2O3 and Fe3+/Zn2+/Cu2+/Ti4+/Al2O3 with the ratio 0.05:0.2:1 and 0.1:0.1:0.8:1 respectively, were found to be the best catalysts for reaction of H2S desulphurization. These catalysts were prepared via modified sol-gel and impregnation methods and calcined at 400oC for 5 hours. The results of catalytic activity testing for the Fe3+/Zn2+/Cu2+/Al2O3 and Fe3+/Zn2+/Cu2+/Ti4+/Al2O3 showed 100% H2S desulphurization reaction occurred at 100oC of reaction temperature. Both supported catalysts of Fe3+/Zn2+/Cu2+/Al2O3 and Fe3+/Zn2+/Cu2+/Ti4+/Al2O3 also had the lowest H2S adsorption with 3.7 % and 1.9 % respectively, ranging from room temperature to 40oC. Furthermore, both catalysts could oxidize the highly concentration of H2S with 90.6 % and 94.3 % respectively, even at the light-off temperature of 40oC. Importantly, the both catalysts could be regenerated via heating at 200oC for 3 hours under compressed air flow at the rate of 100mLmin-1. The XRD analysis only showed the present of three peaks due to cubic phase of γ-Al2O3. The Ti, Cu, Zn and Fe elements that were present in both catalysts matrix system were presumably dispersed on the surface of alumina support and were detected through EDX analysis. The SEM micrograph showed that the supported catalysts had agglomerated in undefined shape with various size particles.




Fadhali M. A.*, Zainal J., Munajat Y., Jalil A. and Rahman R
Institute of advanced Photonic Sciences,

Faculty of Science

University Technologi Malaysia

81310 Skudai, Johor,Malaysia

In this paper we present some investigations and analysis of various parameters that contribute for increasing the coupling efficiency of laser diode to single mode fiber coupling using Ball lens coupling and butt coupling schemes. The fiber attachment process and the fixing of various coupling components have been performed in what is so called active alignment process, where the system continues measuring the coupled power during the process of coupling and welding of (lens holder, fiber ferrule, and welding clips). Nd: YAG laser welding system (LW4000S from Newport) has been used for the alignment and welding of the coupling components. Results of optimizing laser beam parameters to get good welds with small heat affected zones (HAZ) such as (variation of weld dimensions with changing of laser beam parameters are also presented. We also studied the weldability of different materials to determine the suitability of using those materials as the base material and welding tools for different types of photonic devices packaging.


A 2-D Analysis of the Stability and Convergence of a Nonlinear Optimal Control Algorithm
Rohanin Ahmad & Mohd Ismail bin Abdul Aziz
Department of Mathematics, Faculty of Science,

Universiti Teknologi Malaysia,

81310 Skudai, Johor, Malaysia.

A multipass process is one that possesses two distinct properties; repetitive operation and interaction between the state and/or output functions generated during successive cycles of operation. A repetitive process has strong structural links to two-dimensional systems, which propagate information in two separate directions that are considered as two distinct dimensions. An algorithm is a repetitive process that falls naturally into the area of 2-D systems where one dimensions is the time horizon of the system under investigation and the other is the progress of the iterations. In this paper we used the 2-D system theory techniques based on the theory of unit memory repetitive processes to analyze the stability and convergence behavior of an algotihm developed for solving nonlinear optimal control problems.


Using Genetic Algorithm (GA) for Solving Integer Linear Programming Problem (ILpp)
Shamsollah Ghanbari
Asstian Azad University, Ashtian, Iran.

Linear programming (Lp) is a branch of applied mathematics. Generally, linear programming problem (Lpp) solving is done by simplex method. A kind of linear programming, named Integer Linear Programming (ILpp) is being necessited to integer solution. There exist different methods for solving integer linear programming problem (ILpp) for example: Gomory’s-all – integer Algorithm, Dantzig – cut method, Branch and bound method, etc. These methods have time, space and computational complexities. It is intended to try to solve ILpp with GA in this paper. This method has loss time, space and computational complexities.


Look Ahead Heuristics for Modeling Solid Waste Collection Problems
Zuhaimy Ismail, Irhamah & L.S.Lee
Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia,

81310 Skudai, Johor, Malaysia.

The collection of solid waste in major cities is intrinsically complex, because it involves various relative factors, which are often in conflict. It normally involved the multi-criteria urban management issues that require multi-criteria analysis. This is categorized as an NP-hard problem where most of these problems are solved using heuristic method. This paper presents the Look Ahead Heuristic (LAH) algorithm developed for solving the scheduling problems of solid waste collection problems with the inclusion of environmental issues such as the smell. Initially the problem is modeled as the capacitated arc routing problem where the minimum deadheading cycles through all the required edges are determined. The inconveniences due to smell was included which enables large quantity of garbage to be removed as soon as possible. Results achieved from this multi-objective routing problem put emphasis on both the cost and the smell. Based on the LAH strategy, we developed solutions to optimize the routing problem for local waste management authority.


The PSB-SD’s Method for the Unconstrained Optimization Problem
1Mustafa bin Mamat, 2Yosza bin Dasril, 3Ismail bin Mohd
1,3Department of Mathematics,

Faculty of Science and Technology

Universiti Malaysia Terengganu

21030 Kuala Terengganu
2Department of Electronic Engineering,

Universiti Teknikal Malaysia Melaka.

A Quasi-Newton especially PSB method is a popular iterative method and had been used in many real problems such as in engineering (Papadrakakis, 1993). The convergence of this method is also quite faster because the computational problems in Hessian matrix can be avoided. The convergence rate of the steepest descent (SD) is more slowly but it has a global convergence. In this paper, we try to explain an algorithm that combined the PSB and SD search direction in determine the solution of unconstrained optimization problem. Then, at the end we also explain about the convergence for this algorithm.


Optimization of Crude Palm Oil Transportation for Northern Peninsular Malaysia
1Shamsudin Ibrahim, 2F.M. Abbas Al-Karkhi & 3Omar A. Kadir
1,2Fakulti Sains Kuantitatif,, Universiti Utara Malaysia,

06010 Sintok,

Kedah Darul Aman.
3School of Industrial Technology, Universiti Sains Malaysia,

11800 Pulau Pinang.

Transportation problem is a special class of linear programming problem that deals with shipping a commodity from sources to destinations. This paper presents a method for finding the optimum solution of a crude palm oil transportation problem with the objective of distance minimization. This commodity originates at the mills and sent to the refineries using a single capacity tanker trucks. A number of mills in the Northern part of Peninsular Malaysia are selected as the sources and a number of refineries as the destinations. This is also an unbalanced transportation problem where demand exceeded supply. An integer programming model was developed and run using the Ilog software. The results indicate that this method performs well in terms of the solution exhibited the best mill-to-refinery assignment. The study was further extended to see the effect on total distance when the refineries were relocated at different towns in this part of the country. At one of these locations we found that the total distance was reduced to almost half compared to the original optimal solution.


A Review on Ant Colony Optimization Algorithm for Solving Facility Layout Problems formulated as Quadratic Assignment Problems
Phen Chiak See & Kuan Yew Wong
Department of Manufacturing & Industrial Engineering, Faculty of Mechanical Engineering,

Universiti Teknologi Malaysia,81310 Skudai, Johor, Malaysia.

Since the first formulation of Facility Layout Problems (FLPs) as Quadratic Assignment Problems (QAPs) by Koopmans and Beckman (1957), many initiatives have been taken to solve them through various mathematical ways. However, due to the NP-Hardness of QAPs, solutions for large problem instances () could be computationally intractable. To date, researchers seek for various approximate methods including various local search and metaheuristics approaches to find optimal solution for the problems in a reasonable computational time. One of the metaheuristics that is gaining momentum for solving QAPs is Ant Colony Optimization (ACO). This paper aims to review the underlying concepts of ACO as well as its associated algorithm or variants. Based on the review, it is found that existing ACO variants still possess certain limitations or drawbacks which could be further improved. Hence, this call for a need to derive a more robust ACO variant for solving QAPs. The paper culminates with conclusions and some future research directions.


A Genetic Algorithm for Solving Vehicle Routing Problem with Stochastic Demands
Zuhaimy Ismail & Irhamah
Department of Mathematics, Faculty of Science,

Universiti Teknologi Malaysia,

81310 Skudai, Johor, Malaysia.

Vehicle Routine Problem (VRP) consists in finding the optimal route through a number of customers from one or several depots to a set of geographically scattered points, such every point is visited once by exactly one vehicle, all routes start and end at the depot, and the total demands of all points on one particular route must not exceed the vehicle capacity. The objective to be minimized is usually a function of the number of vehicles in the solution, the distance driven and the service provided to the customers. One of important variation of VRP is in the demand structure where the demand of each location is unknown when the route is designed, but it follows certain probability distribution. This is known as the VRP with Stochastic Demand (VRPSD). The Algorithms for stochastic VRP such as this are rather complex than deterministic and the computational intricacy is very demanding. Various formulations and algorithms have been proposed and investigated but the work on the application of Genetic Algorithm (GA) in VRPSD is lacking in the literature. GA is an effective search and optimization method that simulates the process of natural selection or survival of the fittest. It has seen widespread use amongst modern metaheuristics, and several applications to NP-hard problems. This approach provides satisfactory results for optimization problems that are hard to solve using exhaustive techniques. This paper presents the GA heuristic approach on VRPSD for single vehicle and single depot. The chromosome representation of the problem is based on order/permutation representation with the inclusion of the initial solution using constructive and insertion heuristic. The GA is used to find the order in which the customers are initially visited, and a local search is applied subsequently to detect possible improvement. The approach is tested on a set of randomly generated problems following some discrete probability distributions and compared with existing heuristic procedure. The problem data are inspired by real case of VRPSD in waste collection. The results show that GA, although requiring slightly longer computational times, is better than previous algorithm in terms of solution quality.


Mixed Integer Programming Model for the Portfolio Selection with Minimum Transaction Lots
Lucy K. Basar, Fajriana, Maryana, Putra B.J. Bangun, Rustam Sinaga, Zainal Azis & Herman Mawengkang
Department of Mathematics

University of Sumatera Utara

The mathematical model of portfolio optimization has been largely written in terms of minimizing the risk, given the return. The difficulty in dealing with the quadratic programming model due to Markowitz has been overcome by the recent progress in algorithmic research, the introduction of linear risk function has given rise to the interest in solving portfolio selection problems with real constraints. This paper deals with the portfolio selection problem with minimum transaction lots. A heuristic of neighborhood search algorithm is proposed to solve the mixed integer programming model. The algorithm starts from the solution of the relaxed problem to find a solution which is close to the continuous solution.


Statistical Modeling of the Incidence of Breast Cancer in NWFP, Pakistan
1Salahuddin Khan & 2Arifullah
1Department of Statistics,

University of Peshawar,

NWFP, Pakistan.
2Lecturer in Statistics,

Higher Education Department,

Peshawar, NWFP, Pakistan.

Breast cancer is the most common form of cancer that affects women. It is life threatening disease and commonest malignancy in women through out the world. In this study an effort has been made to determine the most likely risk factors of breast cancer and to select a parsimonious model of the incidence of breast cancer in women patients of the age 50 years and above in the population of NWFP, Pakistan. The data were collected from a total of 3000 women patients, arriving at Institute of Radiotherapy and Nuclear Medicine Peshawar, NWFP, Pakistan. Logistic regression model was estimated, for breast cancer patients, through backward elimination procedure. Brown tests were applied to provide an initial model for backward elimination procedure. The logistic regression model, selected through backward elimination procedure contain the factors Menopausal status (M), Reproductive status (R), and the joint effect of Diet and family history (D*H). We conclude that menopausal status, reproductive status and the joint effect of diet and family history were the important risk factors for the breast cancer. Separate models were then fitted for married and unmarried breast cancer patients. The best selected model for married females is of factors Feeding (F), R, M, (D*H), whereas the best selected model for unmarried females has only one main factor Menopausal status. We conclude that breast feeding, reproductive status, menopausal status and the joint effect of diet and family history were the important risk factors of breast cancer in married women and the menopausal status was the important risk factor of breast cancer in unmarried women.


An Overview of Evaluation Criteria in Logistic Regression Models
1Hussain Jassim N., 2Low Heng Chin, & 3F.M. Abbas Alkarkhi
1,2School of Mathematical Sciences,

Universiti Sains Malaysia, 11800 Pulau Penang
3School of Industrial Technology,

Universiti Sains Malaysia, 11800 Pulau Penang

Survival regression models are used in many disciplines such as Social, Medical, Biological, and Engineering Sciences. Choosing a model that represents adequately the data depends on a number of criteria suggested by statisticians, but so far there is no agreement on any criterion as the best one for evaluating the survival regression models. The present paper is an overview of more than fourteen criteria used in evaluating logistic regression models, which is one of the survival regression models. The criteria are divided into four groups. The first group consists of six criteria and related to goodness of fit, the second group for evaluating the model coefficients using three criteria, the third group is the criterion of testing association between the response probability variable and the linear combination of explanatory variables in the model (link function), while the last group is for comparison of logistic models and has three criteria. Two types of logistic models are considered nested and not nested. The advantages, disadvantages, and the use of these criteria in evaluating the logistic models are studied in this paper. The main conclusion is that although there are numerous criteria, some of them are preferred and used more than others.


Projection Pursuit Regression A Method of Statistical Downscaling
A.H. Wigena and Aunuddin
Department of Statistics,

Bogor Agricultural University,

Jakarta 10640.

In climatology statistical downscaling techniques have been used for predicting local rainfall from GCM (Global Circulation Model) output. Since the characteristics of GCM output are nonlinear and do not follow any standard statistical distribution, the use of parametric technique will not be appropriate. Projection pursuit regression (PPR) is one of nonparametric methods which can be used to model the data that have such characteristics. The result of analysis shows that PPR performs better than the common parametric method, i.e. principal component regression (PCR). In respective of the length of data, the correlations of the predicted values of the PPR model with the observed data are much higher (between 0.71 and 0.84) than those of PCR model (between 0.60 and 0.66).


The Determinants of Breast Feeding: Quantiles Regression Approach
Mahdiyah Mokhtar1, Wan Norsiah Mohamed2 & Kamarulzaman Ibrahim3
1Department of Home Economics, Faculty of Technology,Universitas Negeri Jakarta,13220 Rawamangun, Jakarta Timur.

2,3School of Mathematical sciences, Faculty of Science and Technology,Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor.

Breast milk is known to be very essential for the baby. Many researches have been carried out to determine factors which influence the period of breastfeeding. Among the statistical tools treat are often used for analyzing data regarding breastfeeding include logistic regression and multiple linear regression. In this paper, quantiles regression approached and analyzed the data of Malaysian Family Life Survey (MFLS), to identify the determinants of breastfeeding among mothers in Malaysia. It is known that the classical linear regression methods based on minimizing sums of squared residuals, but quantiles regression use a mechanism based on estimating models for the conditional median function and the full range of other conditional quantile functions. An implementation of this method is available with R software in the quantreg package. It is found that the period of breastfeeding is significantly related to place of living, religion and totals number of children in the family.

Keywords: Breastfeeding; Quantiles Regression; R-Program.


The Use of Logistic Regression Model to Indentify the Risk Factor of H5N1 Avian Influenza Virus at Native Chicken in Sumatera and Kalimantan Island, Indonesia
1Etih Sudarnika, 2Asep Saefuddin , 3Abdul Zahid and 4Chaerul Basri
1,3,4Laboratory of Epidemiology,

Faculty of Veterinary Medicine, IPB, 16680, Darmaga, Bogor, Indonesia.
2Department of Statistics,

Faculty of Mathematics and Science, IPB, Bogor, Indonesia.

The cross sectional study had been carried out in December 2005 at Kalimantan and Sumatera Island, Indonesia. The objective of this study was to apply the logistic regression model to identify the risk factor of H5N1 avian influenza virus at native chicken. 12,713 serum samples of chicken from 498 farmers were collected. The H5N1 virus was tested by Haemagglutination Inhibition (HI) test from serum samples and the information of risk factor was obtained from questionnaire. The questionnaire involved farmer's characteristic and farm management. Logistic regression Model showed that an association with disease risk at a 5% significance level was found for cage hygiene (OR: 1.64, 95%CI 1.21-2.23), feed equipment hygiene (OR: 1.53, 95%CI 1.12-2.09), drink equipment hygiene (OR: 1.57, 95%CI 1.16-2.12), cage environment hygiene (OR: 1.60, 95%CI 1.13-2.21), the quarantine actions (OR: 2.69, 95%CI 1.61-4.50) and movement control of poultry, vehicles and humans (OR: 1.75, 95%CI1.03-2.99).

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