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.

A critical review on the influence of process parameters in catalytic co-gasification: Current performance and challenges for a future prospectus

Abstract: Gasification has emerged as a prominent technique to convert biomass, coal, plastic, and municipals wastes sludge (generated from agriculture, industrial, and domestics, urban centers) into energy in the form of gaseous products. However, co-gasification of these materials has many advantages, such as desired product yield and uninterrupted feedstock supply as well as the sustainable utilization and disposal of these wastes. Numerous reviews have been documented based on the gasification of individual materials of biomass and coal, nevertheless, very few reviews have been reported on the process of co-gasification. In co-gasification, the effect of parameters becomes very important when dealing with the co-gasification of different mixed materials. The objective of this review to study the effect of temperature, blending ratio, and equivalence ratio (ER) on catalytic co-gasification of biomass-coal, biomass-plastic, biomass-sewage sludge, and mixed plastic blends. In addition, the effects of these parameters on gaseous products, heating values, tar formation, and gasification performance have been analyzed. It is also important to specify the ranges of parameters for the feed combinations in catalytic co-gasification that will provide a guideline for researchers and commercial enterprises to investigate co-gasification. For temperature from 650 to 750 °C found good for hydrogen rich syngas production. Whereas, the ratio of biomass 50% or above and ER of 0.20 and 0.25 were found good for higher hydrogen and lower CO2 and tar production. Moreover, the current issues are related to technology, operational problems, policy requirements and route map for commercial success of co-gasification technology have been highlighted.

Liquid-Liquid Equilibrium of Poly(ethylene glycol) 2000 + Potassium Citrate + Water at (25, 35, and 45) °C

Abstract: Liquid−liquid equilibrium for an aqueous two-phase system containing poly(ethylene glycol) 2000 + potassium citrate + water was studied at three different temperatures of (25, 35, and 45) °C. The binodal curve was fitted to an empirical equation relating the concentrations of PEG 2000 and potassium citrate, and the coefficients were estimated for the respective temperatures. Tie line compositions were correlated using Othmer−Tobias and Bancroft equations, and the parameters are also reported.

Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm

Abstract: The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint in filtering the training dataset to yield training data that are most relevant to the intended or the targeted test dataset/file. It has to scan through all the training datasets categories to classify the intended/targeted data. As such, traditional k-NN is considered not intelligent and consequently is suffering poor accuracy performance with high computational complexity. A Semantic-kNN (Sk-NN) algorithm for ML is thus proposed in this paper to address the limitations in the traditional k-NN. The proposed Sk-NN deploys a process by leveraging on the semantic itemization and bigram model to filter the training dataset in accordance with the relevant information engaged in the test dataset. It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. Ultimately, Sk-NN is to elevate the ML performance in pattern extraction and labeling in the big data context.