Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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The plastic-derived product, nowadays, becomes an indispensable commodity for different purposes. A huge amount of used plastic causes environmental hazards that turn in danger for marine life, reduces the fertility of soil, and contamination of ground water. Management of this enormous plastic waste is challenging in particular for developing countries like Bangladesh. Lack of facilities, infrastructure development, and insufficient budget for waste management are some of the prime causes of improper plastic management in Bangladesh. In this study, the route of plastic waste production and current plastic waste management system in Bangladesh have been reviewed extensively. It emerges that no technical and improved methods are adapted in the plastic management system. A set of the sustainable plastic management system has been proposed along with the challenges that would emerge during the implementation these strategies. Successful execution of the proposed systems would enhance the quality of plastic waste management in Bangladesh and offers enormous energy from waste.

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Focusing on the application of carbon slurry electrodes in advanced electrochemical power and energy storage systems, the electrical conductivity of such electrodes is thoroughly investigated experimentally. A slurry electrode made from steam-activated Norit is analyzed to estimate its electronic and ionic conductivities separately. A single-pass rectangular flow channel with three different widths of 4.1, 3.6, and 3.1 cm is used to investigate the effect of the flow channel geometry on slurry electrode conductivity. Three different slurry concentrations of 5, 10, and 15 wt % are investigated, while electronic and ionic conductivities are separately measured using distilled water and sulfuric acid as electrolytes. The charge conduction improvement due to the availability of more charge-carrying particles in the slurry is quantified, and it is shown that up to about 220 and 120% increase in electronic conductivities can be achieved by increasing carbon loading from 5 to 10 and 15 wt %, respectively. Analysis of slurry conductivity variations from a static condition to a flow rate of 280 mL min–1 with different channel widths and concentrations shows that the slurry conductivity reaches a maximum value at an intermediate flow rate and is then gradually decreased. The optimum working condition of a slurry electrode is finally discussed.

Performance and emission comparison of Karanja (pongamia pinnata), Pithraj (aphanamixis polystachya), Neem (azadira chtaindica) and Mahua (madhuca longofolia) seed oil as a potential feedstock for biodiesel production in Bangladesh ABSTRACTThis paper investigates the production of biodiesel (BD) from karanja (Pongamia pinnata), pithraj (Aphanamixis polystachya), neem (Azadira chtaindica) and mahua (Madhuca longofolia) seed oil through acid esterification, followed by the investigation on the transesterification process and physicochemical properties of oils. This study also includes their effects on engine performance and emission on a direct ignition (DI) diesel engine. A maximum 9 of 6% by volume methyl ester (biodiesel) was obtained from mahua oil at methanol concentration of 22vol%, catalyst concentration of 0.5wt% and a temperature of 55°C and at the same condition 94%, 92% and 91% biodiesel extraction was experienced for neem, pithraj and karanja seed oil respectively. The diesel-biodiesel blend (B10) has been used during the test run and it was found that all of the fuels showed performance closer to the neat diesel. Among all the biodiesels, karanja showed better performance compared to the other three. On the other hand, high oxygen content of biodiesel causes less CO and NOx emission. It was experimentally found that mahua emits the least amount of CO and NOx which were 44.44% and 38.3% respectively compared to the neat diesel. Results indicate that these oils are potential biodiesel feedstock and can be used as an alternative to the diesel fuel in the near future. Desirable engine performance and tail pipe emissions are also observed during the experimental investigation.

The present world is now facing the challenge of proper management and resource recovery of the enormous amount of plastic waste. Lack of technical skills for managing hazardous waste, insufficient infrastructure development for recycling and recovery, and above all, lack of awareness of the rules and regulations are the key factors behind this massive pile of plastic waste. The severity of plastic pollution exerts an adverse effect on the environment and total ecosystem. In this study, a comprehensive analysis of plastic waste generation, as well as its effect on the human being and ecological system, is discussed in terms of source identification with respect to developed and developing countries. A detailed review of the existing waste to energy and product conversion strategies is presented in this study. Moreover, this study sheds light on sustainable waste management procedures and identifies the key challenges to adopting effective measures to minimise the negative impact of plastic waste.

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