Chittagong University of Engineering & Technology

About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Renewable energy & Dielectric. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.

Production of biogas and performance evaluation of existing treatment processes in palm oil mill effluent (POME)

Abstract: Palm oil is an important edible oil in the global fats and oil market and its industry is also one of the prominent global agricultural industries. The production of crude palm oil reached 62.34 million tonnes in 2014. However, enormous volumes of production has subsequently discharged large volumes of a palm oil mill effluent (POME). POME is a remarkably contaminating effluent due to its high amount of COD, BOD and colour concentrations, which can affect the environment, especially water resources. However, it was recognized as a prospective source of renewable biogas such as biomethane and biohydrogen. Nowadays, with the global emphasis on sustainability, if we simultaneously operate wastewater treatment and produced renewable bio energy in the palm oil industry, then this industry can be environmentally sound, with cleaner production and greater sustainability. The aim of this review is to discuss various existing treatment processes (mainly anaerobic and aerobic digestion, physicochemical treatment and membrane separation) and factors that influence the treatment methods and conversion of POME to renewable biogas such as biomethane and biohydrogen on a commercial scale.

Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

Abstract: Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the device logs. Modeling and predicting individual’s smartphone usage based on contexts, such as temporal, spatial, or social information, can be used to build various context-aware personalized systems. In order to intelligently assist them, a machine learning classifier based usage prediction model for individual users’ is the key. Thus, we aim to analyze the effectiveness of various machine learning classification models for predicting personalized usage utilizing individual’s phone log data. In our context-aware analysis, we first employ ten classic and well-known machine learning classification techniques, such as ZeroR, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Adaptive Boosting, Repeated Incremental Pruning to Produce Error Reduction, Ripple Down Rule Learner, and Logistic Regression classifiers. We also present the empirical evaluations of Artificial Neural Network based classification model, which is frequently used in deep learning and make comparative analysis in our context-aware study. The effectiveness of these classifier based context-aware models is examined by conducting a range of experiments on the real mobile phone datasets collected from individual users. The overall experimental results and discussions can help both the researchers and applications developers to design and build intelligent context-aware systems for smartphone users.

Studies of ionizing radiation shielding effectiveness of silica-based commercial glasses used in Bangladeshi dwellings

Abstract: Following the rapid growing economy, the Bangladeshi dwellers are replacing their traditional (mud-, bamboo-, and wood-based) houses to modern multistoried buildings, where different types of glasses are being used as decorative as well as structural materials due to their various advantageous properties. In this study, we inquire the protective and dosimetric capability of commercial glasses for ionizing radiation. Four branded glass samples (PHP-Bangladesh, Osmania-Bangladesh, Nasir-Bangladesh, and Rider-China) of same thickness and color but different elemental weight fractions were analyzed for shielding and dosimetric properties. The chemical composition of the studied material was evaluated by EDX technique. A well-shielded HPGe γ-ray spectrometer combined with associated electronics was used to evaluate the attenuation coefficients of the studied materials for 59 keV, 661 keV, 1173 keV and 1332 keV photon energies. A number of shielding parameters- half value layer (HVL), radiation protection efficiency (RPE) and effective atomic number (Zeff) were also evaluated. The data were compared with the available literature (where applicable) to understand its shielding capability relative to the standard materials such as lead. Among the studied brands, Rider (China) shows relatively better indices to be used as ionizing radiation shielding material. The obtained, Zeff of the studied glass samples showed comparable values to the TLD-200 dosimeter, thus considered suitable for environmental radiation monitoring purposes.