Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

Effect of blending ratio and catalyst loading on co-gasification of wood chips and coconut waste

Abstract: Catalytic co-gasification is an important tar reforming technique, which may appreciably improve the quality of syngas through tar reforming reaction. In this study, wood chips (WC) were co-gasified with two coconut wastes, namely coconut shells (CS) and coconut fronds (CF), in a downdraft gasifier. The dolomite and limestone were used as tar reforming mediums. The effect of the blending ratio, catalyst type, biomass type and catalyst to biomass loading on gas composition and heating value of the syngas was investigated for different WC/CS and WC/CF blends. The results revealed that the WC/CS blending ratio of 70:30 produces the highest H2 amount (11.70 vol.%), which was 31% higher than the H2 amount of the other blends. The HHVsyngas of 70:30 blend was measured about 4.96 MJ/Nm3, which was also higher among all the tested blends. The co-gasification of 70:30 blend of WC/CS, when compared with same blending ratio WC/CF, produced two times higher CO, 60% higher H2 and 75% higher HHVsyngas. During catalytic co-gasification of WC/CS blends with dolomite and limestone, the dolomite yielded 24%, 13.8% and 25.6% increment in CO, H2, and CH4, respectively. It is concluded that the coconut wastes can be substituted or co-gasified with wood after carrying out some major changes in a gasifier geometry.

A quantitative study of tuning ROS gmapping parameters and their effect on performing indoor 2D SLAM

Abstract: Simultaneous localization and mapping (SLAM) complexity reduction is a fast progressing research area. Its attraction is owed to the potential commercial benefits of developing low cost yet highly effective SLAM based robotic applications. ROS gmapping package offers a lightweight incorporation of FastSLAM 2.0. The package has been used with different ROS supported robotic platforms and showed remarkable success. However, the effect of the package mapping parameters seem not to be fully exploited, especially with low cost robotic platform with no full ROS support such as Hercules platform. This paper presents a full implementation and performance quantitative evaluation on the gmapping package running on both standard PC and Raspberry Pi processors. We study the effects of tuning the number of particles, the displacement update and the resampling threshold by separately varying each of these parameters to several incremental values and running the algorithm on a recorded dataset. For each run, a grid map was constructed and the performance was evaluated based on mapping accuracy, CPU load and memory consumption. We are then able to propose a tuning guidelines to enlighten the gmapping execution while maintaining high performance.