Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Critical success factors of Lean Six Sigma for the Malaysian automotive industry

Abstract: Purpose – The objective of this paper is to explore the critical success factors (CSFs) for Lean Six Sigma (LSS) in the Malaysian automotive industry.Design/methodology/approach – Structural equation modeling (SEM) was employed to test the model drawing on a sample of 252 Malaysian automotive organisations. Exploratory factor analyses (EFA), confirmatory factor analysis (CFA), and reliability analysis empirically verified and validated the underlying items of CSFs of LSS.Findings – The results of EFA, CFA, and reliability analysis show that two items for supplier relationship are recommended to be excluded from the analysis. The result indicates that LSS has identified 40 items as compared to the original questionnaire which had 42 items. Based on the survey of empirical data, the two factors of leadership and customer focus have been shown to be the extremely important factors for LSS implementation in the Malaysian automotive industry.Research limitations/implications – Firstly, this survey is based onl...

Glycerol for renewable acrolein production by catalytic dehydration

Abstract: The significant surge in biodiesel production by transesterification of edible or non-edible oils have caused surplus of glycerol in the market. With its characteristics, unique structure, renewability, and bio-availability, glycerol has tremendous potential to be transformed to higher value-added chemicals. This article provides a comprehensive and critical review of glycerol dehydration to acrolein in both petroleum-and bio-based processes. Acrolein has enormous industrial applications as a significant chemical intermediate for acrylic acid, dl-Methionine and superabsorbent polymer production. The current development of several precursors on suitable support such as heteropoly acids, zeolites, mixed metal oxides, and pyrophosphates in creating superior catalytic properties for both liquid- and gas-phase processes has been discussed. The acidity and textural properties of various catalysts, as significant variables affecting acrolein yield and selectivity, are evaluated separately. Techno-economical evaluation on dehydration of petroleum- and bio-based glycerol to acrolein proved that the bio-based processes are more feasible compared to the conventional petroleum-based process. In addition, various proposed mechanisms for catalytic dehydration of glycerol to acrolein have been examined. Particularly, catalyst coking and few crude glycerol applications have been identified as the main drawbacks for immediate industrialization and commercialization of glycerol dehydration to acrolein.

Hybrid recommendation approaches for multi-criteria collaborative filtering

Abstract: Recommender systems are software tools and techniques for suggesting items in an automated fashion to users tailored their preferences. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user's needs from the neighborhoods of like-minded people, are becoming increasingly popular as ways to overcome the information overload. The multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and several methods have been proposed for improving the accuracy of these systems. However, the problem of multi-criteria recommendations with a single and overall rating is still considered an optimization problem. In addition, increasing the accuracy in predicting the appropriate items tailored to the users' preferences is on of the main challenges in these systems. Hence, in this research new recommendation methods using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Self-Organizing Map (SOM) clustering are proposed to improve predictive accuracy of criteria CF. In this research, SOM enables us to generate high quality clusters of dataset and ANFIS is used for discovering knowledge (fuzzy rules) from users' ratings in multi-criteria dataset, generating appropriate membership functions (MFs), overall rating prediction and input selection. Using exhaustive search method for input selection, the effective inputs are determined to build the ANFIS models in all generated clusters. Furthermore, new fuzzy-based algorithms, Weighted Fuzzy MC-CF (WFuMC-CF), Fuzzy Euclidean MC-CF (FuEucMC-CF) and Fuzzy Average MC-CF (FuAvgMC-CF), are presented for prediction task in multi-criteria CF. FuEucMC-CF and FuAvgMC-CF algorithms uses the fuzzy-based Euclidian distance and fuzzy-based average similarity, respectively, the WFuMC-CF algorithm uses fuzzy-based user- and item-based prediction in a weighted approach. Experimental results on real-world dataset demonstrate that the proposed hybrid methods remarkably improve the accuracy of multi-criteria CF in relation to the previous methods based on multi-criteria ratings.