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Abstract A battery pack is produced by connecting the cells in series and/or in parallel to provide the necessary power for electric vehicles (EVs). Those parameters affecting cost and reliability of the EVs, including cycle life, capacity, durability and warranty are highly dependent on the thermal management system. In this work, computational fluid dynamic analysis is performed to investigate the air cooling system for a 38,120 cell battery pack. The battery pack contained 24 pieces of 38,120 cells, copper bus bars, intake and exhaust plenum and holding plates with venting holes. Heat generated by the cell during charging is measured using an accelerating rate calorimeter. Thermal performances of the battery pack were analyzed with various mass flow rates of cooling air using steady state simulation. The correlation between Nu number and Re number were deduced from the numerical modeling results and compared with literature. Additionally, an experimental testing of the battery pack at different charging rates is conducted to validate the correlation. This method provides a simple way to estimate thermal performance of the battery pack when the battery pack is large and full transient simulation is not viable.

Abstract This paper presents the use of a Support Vector Machine load predictive energy management system to control the energy flow between a solar energy source, a supercapacitor-battery hybrid energy storage combination and the load. The supercapacitor-battery hybrid energy storage system is deployed in a solar energy system to improve the reliability of delivered power. The combination of batteries and supercapacitors makes use of complementary characteristic that allow the overlapping of a battery’s high energy density with a supercapacitors’ high power density. This hybrid system produces a straightforward benefit over either individual system, by taking advantage of each characteristic. When the supercapacitor caters for the instantaneous peak power which prolongs the battery lifespan, it also minimizes the system cost and ensures a greener system by reducing the number of batteries. The resulting performance is highly dependent on the energy controls implemented in the system to exploit the strengths of the energy storage devices and minimize its weaknesses. It is crucial to use energy from the supercapacitor and therefore minimize jeopardizing the power system reliability especially when there is a sudden peak power demand. This study has been divided into two stages. The first stage is to obtain the optimum SVM load prediction model, and the second stage carries out the performance comparison of the proposed SVM-load predictive energy management system with conventional sequential programming control (if-else condition). An optimized load prediction classification model is investigated and implemented. This C-Support Vector Classification yields classification accuracy of 100% using 17 support vectors in 0.004866 s of training time. The Polynomial kernel is the optimum kernel in our experiments where the C and g values are 2 and 0.25 respectively. However, for the load profile regression model which was implemented in the K-step ahead of load prediction, the radial basis function (RBF) kernel was chosen due to the highest squared correlation coefficient and the lowest mean squared error. Results obtained shows that the proposed SVM load predictive energy management system accurately identifies and predicts the load demand. This has been justified by the supercapacitor charging and leading the peak current demand by 200 ms for different load profiles with different optimized regression models. This methodology optimizes the cost of the system by reducing the amount of power electronics within the hybrid energy storage system, and also prolongs the batteries’ lifespan as previously mentioned.

Abstract Transesterification of waste cooking oil with heterogeneous (heteropoly acid) catalyst and methanol has been investigated. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to study the relationship between process variables and free fatty acid conversion and for predicting the optimal parameters. The highest conversion was 88.6% at optimum condition being 14 h, 65 °C, 70:1 and 10 wt% for reaction time, reaction temperature, methanol to oil molar ratio and catalyst loading, respectively. The RSM and ANN could accurately predict the experimental results, with R 2 = 0.9987 and R 2 = 0.985, respectively. Kinetics studies were investigated to describe the system. The reaction followed first-order kinetics with the calculated activation energy, Ea = 53.99 kJ/mol while the pre-exponential factor, A = 2.9 × 10 7 min −1 . These findings can help improve an environmentally friendly biodiesel process that conforms to ASTM D6751 standards.

Abstract Renewable energy has a more important role to play in China’s power sector, especially at the regional level. Renewable electricity in China has made great progress on the basis of national policies. However, the promotion of renewable electricity sector at the regional level is still hampered by local issues. China needs to solve some challenging barriers related to implementation of polices for the development of renewable electricity. These barriers stem largely from availability and reliability constraints. Systematic planning techniques are needed to fully utilize abundant renewable energy resources. In this work, an improved graphical pinch analysis-based approach is presented, which considers carbon-constrained regional electricity planning and supply chain synthesis of biomass energy at the regional level in rural China. The minimum renewable energy target is identified by Carbon Emissions Pinch Analysis (CEPA). Next, a demand-driven approach is applied to synthesize the biomass supply chain network to meet the established target in a given region. A detailed case study of Laixi County in China is used to demonstrate the applicability of the proposed approach for policy-making to promote utilization of renewable electricity at the regional level.

Abstract The energy penalties associated with the liquid amines carbon dioxide absorption are huge which could be minimised by using materials based carbon capture adsorption. A facile one-step approach for the preparation of activated porous carbon spheres through direct carbonization of d -glucose with a novel non-corrosive chemical, potassium acetate for carbon dioxide capture is presented here. The amount of potassium acetate is varied to control the chemical structure, morphology, porosity and textural features. The potassium acetate/ d -glucose impregnation ratio of 3 is optimum condition for obtaining activated porous carbon spheres with high specific surface area (1917 m2 g−1), spherical morphology, and specific pore volume (0.85 cm3 g−1). The activated porous carbon spheres prepared using different glucose to potassium acetate ratios are employed as carbon dioxide adsorbents. Among all, activated porous carbon spheres prepared with the potassium acetate/ d -glucose of 3 registers the best performance and exhibits carbon dioxide adsorption capacities of 1.96 and 6.62 mmol g−1 at 0 °C/0.15 bar and 0 °C/1 bar. It also shows impressive carbon dioxide adsorption at 0 °C/30 bar (20.08 mmol g−1) and 25 °C/30 bar (14.08 mmol g−1). This performance is attributed to highly developed porous structure of the optimized material. Low isosteric heat of adsorption (24.8–23.04 kJ mol−1) means physisorption which suggests lower energy penalties for material regeneration. A non-complicated synthesis and high carbon dioxide capture demonstrate the importance of this work. This synthesis strategy may be utilized to prepare porous carbons from other precursors which could find potential in energy-related applications.

Disposing of solid waste and demand of fossil fuel have become the great challenges in the 21st century. Malaysia as one of the top producers of palm oil and wooden furniture in the world is well positioned to take the challenge of the reuses of its enormous output of lignocellulosic biomass such as oil palm trunk, sawdust of rubberwood and sawdust of mixed hardwood generated from palm oil and furniture industries. Before these lignocellulosic biomasses can be used to produce fuel and major chemicals which are normally derived from petroleum, lignocellulosic materials have to be converted to glucose. Hence, it is a need to investigate the conversion efficiency and to determine the optimum conditions for the conversion of lignocellulosic materials to glucose. This present work is aimed to investigate the potential use of oil palm trunk, rubberwood sawdust and mixed hardwood sawdust as an alternative feedstock for lignocellulosic glucose production. This research also served to identify the optimum two-stage concentrated acid hydrolysis condition that can convert these three lignocellulosic biomasses to glucose efficiently. Two stages concentrated sulfuric acid hydrolysis process using different acid concentration and reaction time were performed on those lignocellulosic biomass samples. The optimum results for oil palm trunk, rubberwood and mixed hardwood sawdust were obtained by using 60% acid concentration reacted for 30 min during 1st stage hydrolysis and subsequently followed by another 60 min reaction time with 30% acid concentration during the 2nd stage hydrolysis. The results, showed that oil palm trunk has a higher glucose conversion yield than those of rubberwood sawdust and mixed hardwood sawdust.

Abstract In the quest for alternatives for fossil fuels, phase change materials (PCMs) have attracted considerable attention due to their ability to store renewable thermal energy. Compared to other storage systems, PCM systems are of low cost and capable of the storage of a high density of energy. However, few drawbacks hinder their practical application at an industrial scale. Among the drawbacks, supercooling problem affecting all types of PCMs is crucial. Supercooling as a shortcoming in PCM applications limits their practical applications. However, a comprehensive discussion or review articles have not been published. A PCM can exists in the liquid form below the phase change temperature or its freezing point, without fully freezing, due to supercooling. Thus, practical applications are limited by major problems such as the temperature variations and the increase of energy consumption. In this paper, most of the reported supercooling mitigation techniques for various types of PCMs and nanofluids are reviewed. These techniques are based mainly on adding nucleating agents (such as carbon nanotubes, fine salt particles, and nanoaditives), thickeners (such as carboxy methyl cellulose), and macroporous structures. The mitigation of phase separation and thermal cycling effects on supercooling are also discussed. The mitigation of supercooling in encapsulated organic PCMs, which is an important issue that is not very well understood, too is briefly addressed. Recommendations and future challenges to enhance the application of PCMs are discussed.

Abstract Butanol and ethanol are promising conventional fuel alternatives particularly when utilized in advanced combustion mode like homogeneous charge compression ignition (HCCI). This study investigates the performance and emission characteristics of HCCI engines fueled with oxygenated fuels (i.e. butanol and ethanol). The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. This study uses HCCI experimental data to characterize variations in seven engine performance metrics including indicated mean effective pressure (IMEP), thermal efficiency, in-cylinder pressure, net total heat released, nitrogen oxides (NOx), carbon monoxide (CO), and total hydrocarbon (THC) concentrations. Two types of ANNs including radial basis function (RBF) and feedforward (FF) are developed to predict the seven engine performance metrics. The experimental data at 123 HCCI operating points from two different engines are collected to validate the ANN models. The validation results indicate both RBF and FF models can predict HCCI engine performance metrics with less than 4% error for butanol and ethanol fueled engines. The results show that the FF neural network models are advantageous in terms of network simplicity with fewer required neurons but need twice as much training time compared to the RBF models.

Abstract Energy recovery within processes via heat exchanger network (HEN) to minimise the external utilities has been well established. Due to the close energy interactions between HEN and the utility system, it is important to synthesise these two elements simultaneously. In this work, a novel systematic approach for synthesis of HEN with utility systems is presented. Multiple cascades automated targeting is applied to determine minimum total operating cost of the trigeneration system, minimum hot and cold utility targets for heat integration prior to detailed design. Besides, the selection and allocation of utilities, distribution of steam, and potential power generation can also be determined. Meanwhile, the types of boiler fuel for the trigeneration utility system is also identified. An illustrative example with two scenarios is solved to illustrate the proposed approach.