Strategic Research Partnership on Automotive Powertrain & Fuels in Universiti Malaysia Pahang, Universiti Teknikal Malaysia Melaka & Universiti Tun Hussein Onn Malaysia

Malaysia has been known as one of the competitive automotive manufacturers and consumers in the ASEAN region. This is as a result of the government initiative of promoting the automotive industry as one of the key industries in Malaysia. However, depleting fuel sources, unstable oil price (Yr 2008, USD50/barrel – Yr 2011, USD100/barrel) and the increasing climate problems have prompt major research and development works on optimum automotive powertrain technologies with reduced emissions. Among the universities in the Malaysia Technical University Network (MTUN), three universities - Universiti Malaysia Pahang (UMP), Universiti Teknikal Malaysia (UTeM) & Universiti Tun Hussein Onn Malaysia (UTHM) - have take up the challenge in championing R & D works in producing efficient powertrain and emissions control systems. In this paper, existing research efforts and a proposed strategic partnership are outlined. This initiative will focus on strengthening the research and consultancy capabilities that include utilization of research facilities and expertise among the universities. It is also expected that the outcomes from the partnership will foster better engagement between automotive related industries and universities, at the same time resolving the related R & D issues

Emissions of a Single Cylinder Diesel Engine Operating with Ethanol

The conventional software of GT-Power is used to simulate a single cylinder diesel engine. The diesel engine is simulated to study the engine emission when the engine is operating with ethanol as alternative fuel. The simulation results were compared with the data from the diesel engine operating with mineral diesel The simulations are conducted at full load condition for the engine operating with ethanol and mineral diesel. It is found that the emission of diesel engine operating with ethanol is higher as compared to mineral diesel

Seaweed farming : A perspectives of genetic engineering and nano-technology application

In order to meet the growing demand for resources, there is a rising interest in macroalgae cultivation worldwide due to their potential as a source of food, fuel, and bio-products. However, large-scale and sustainable seaweed cultivation has been a persistent challenge. Specific fundamental issues need to be addressed to maximize the benefits of seaweed production. This article reviews a plan for transitioning to an environmentally sustainable aquaculture system incorporating non-toxic nanoparticles. It also provides an overview of genetic enhancement techniques for macroalgae species to realize their potential fully. Additionally, the article discusses the need for advanced tools and concepts to overcome the challenges in seaweed identification and cultivation and emphasizes the importance of a coordinated effort in fundamental and applied research using emerging technologies to ensure long-term practicality

Experimental Investigation of Al2O3 - Water Ethylene Glycol Mixture Nanofluid Thermal Behaviour in a Single Cooling Plate for PEM Fuel Cell Application

AbstractThermal enhancement through application of nanofluid coolant in a single cooling plate of Proton Exchange Membrane (PEM) fuel cell was experimentally investigated in this paper. The study focuses on low concentration of Al2O3 dispersed in Water - Ethylene Glycol mixtures as coolant in a carbon graphite PEM fuel cell cooling plate. The study was conducted in a cooling plate size of 220mm x 300mm with 22 parallel mini channels and large fluid distributors. The mini channel dimensions are 100mm x 1mm x 5mm. A constant heat load of 100W was applied by a heater pad that represents the artificial heat load of a single cell. Al2O3 nanoparticle used was 0.1 and 0.5 vol % concentration which was then dispersed in 50:50 (water: Ethylene Glycol) mixture. The effect of different flow rates to heat transfer enhancement and fluid flow represented in Re number range of 20 to 120 was observed. Heat transfer was improved up to 13.87% for 0.5 vol % Al2O3 as compared to the base fluid. However the pressure drop also increase which result in pumping power increment up to 0.02W. The positive thermal results implied that Al2O3 nanofluid is a potential candidate for future applications in PEM fuel cell thermal management

Thermogravimetric Analysis of Marine Macroalgae Waste Biomass as Bio-Renewable Fuel

Macroalgae are considered as the 3rd generation of biofuels and a future feedstock for biorefinery. This research aims to provide simple and dependable analytical techniques for measuring the thermal characteristics of dried seaweed. The main objective was to investigate the thermal characteristics of four seaweed species utilizing a thermogravimetric analyzer. The seaweeds Gracilaria fisheri, Caulerpa lentillifera, Ceramium rubrum, and Eucheuma cottonii were collected from the Pahang state of Peninsular Malaysia. The calorific value of the samples was revealed by using a calorimeter. Ceramium rubrum showed the highest calorific value, while Gracilaria fisheri had the most negligible calorific value among the selected samples. The thermogravimetric analysis (TGA) data revealed that the most significant weight loss for this biomass occurred between 160 and 300° for the selected species. Gracilaria fisheri has shown the highest decomposition with the minor residue at 30.26%, whereas Caulerpa lentillifera has a slow weight loss rate in the mentioned range. SEM analysis has been used to perform the morphology of samples, which shows differences in the concentration of epiphytic diatoms with different structural shapes. Based on the results, macroalgae is a promising sustainable biomass feedstock for biofuel application

Prediction of lithium-ion battery temperature in different operating conditions equipped with passive battery thermal management system by artificial neural networks

Lithium-ion batteries generate an enormous amount of heat during constant operation or rapid charge and discharge, which can result in a substantial increase in temperature, affecting the battery performance, reducing its cycle life, and potentially posing a safety issue. As a result, phase change materials (PCMs) based battery thermal management system (BTMS) can be used to control temperature of the battery and improve its performance. Moreover, with the increasing usage of artificial intelligence in a variety of disciplines, it appears to be worthwhile to investigate artificial intelligence approaches to evaluate various types of battery thermal management systems. The main aim of this study is to develop an artificial neural network (ANN) model for prediction of lithium-ion battery temperature equipped with a BTMS. The inputs of the model are discharge rate (1,2 ,3 and 4C), PCM thicknesses (0, 3, 6, 9, and 12 mm), Time (s) and PCM (with and without paraffin/ graphene PCM composite). The output of the model is temperature of the battery (C). Totally, 2012 data points were used to train, validation and test the model. The results of the study revealed capability of ANN to predict battery temperature in various operating conditions of BTMS. The R2, MSE, MAD and MAPE of the model were 0.99, 0.0173, 3.84 and 0.331, respectively. The results of the study have approved suitability of the ANN to predict performance of the passive BTMS

Latent heat prediction of nano enhanced phase change material by ann method

Thermal characteristics of phase change material (PCM) are important in design and utilization of thermal energy storage or other applications. PCMs have great latent heat but suffer from low thermal conductivity. Then, in recent years, nano particles have been added to PCM to improve their thermophysical properties such as thermal conductivity. Effect of this nano particles on thermophysical properties of PCM has been a question and many experimental and numerical studies have been done to investigate them. Artificial intelligence-based approach can be a good candidate to predict thermophysical properties of nano enhance PCM (NEPCM). Then, in this study an artificial neural network (ANN) has been developed to predict the latent heat of the NEPCM. A comprehensive literature search was conducted to acquire thermal characteristics data from various NEPCM to train and test this artificial neural network model. Twenty different types of Nano particle and paraffin based PCMs were used in ANN development. The most important properties which are used as the input for the developed ANN model are NP size, density of NP, latent heat of PCM, density of PCM, concentration and latent heat of NEPCM in the range of 1–60 nm, 100–8960 kg/m3, 89.69–311 kJ/kg, 760 to 1520 kg/m3, 0.02–20 wt% and 60.72–338.6 kJ/kg, respectively. The output variable was latent heat of NEPCM. The result indicates that the ANN model can be applied to predict the latent heat of nano enhanced PCM satisfactory. The correlation coefficient of the created model was 0.97. This result shows ability of ANN to predict the latent heat of NEPCM

Biocrude potential assessment of macroalgae for sustainable biofuel production

Biofuel are commonly regarded as sustainable renewable fuels and are offers a feasible solution for social and economic development. Within the biofuel source, macroalgae are quickly becoming a typical contender as a possible third-generation fuel source due to ease of cultivation and fast growth rates. It is the most excellent bioenergy alternative that overcomes the downsides of first- and second-generation biofuels. Macroalgae based biocrude oil through Hydrothermal liquefaction (HTL) is a promising pathway for sustainable biofuel production. This study aims to compare the biochemical composition of 15 different species collected from various literature sources. The biocrude potential from different species are estimated and compared. The results suggested that green seaweed U. luctuca (16.81%) contain the highest percentage of biocrude content. This indicates the macroalgae as a promising feedstock for biofuels, bio-chemicals and other bio-products

Macroalgae farming for sustainable future : Navigating opportunities and driving innovation

Seaweed cultivation has garnered significant interest, driven by its wide range of biomass benefits. However, comprehensive assessments from various perspectives are imperative to ensure the sustainable cultivation of seaweed. Biotic and Abiotic factors can significantly impact seaweed yield in complex commercial farming. Biotic factors include bacteria, fungi, viruses, and other algae, while abiotic factors include environmental conditions such as temperature, salinity, light intensity, and nutrient availability. Additionally, the susceptibility of seaweeds to pests and diseases further compounds the issue, leading to potential crop losses. This study endeavours to shed light on the immense potential of macroalgae cultivation and underscores the pressing need for scientific advancements in this field. The comprehensive review clearly explains the latest developments in seaweed cultivation and highlights significant advances from diverse seaweed research. Moreover, it provides insightful glimpses into possible future developments that could shape the trajectory of this promising industry

Thermal Conductivity Enhancement of Al2O3 Nanofluid in Ethylene Glycol and Water Mixture

AbstractThe ability of nanofluids that exhibits enhanced thermal performance is acknowledged by researchers through studies since decades ago. However, the observation of thermal properties for nanofluids in water and ethylene glycol based is not fully explored yet. Hence, this paper presents the thermal conductivity of water and ethylene glycol (EG) based Al2O3 nanofluid. The 13 nm sized Al2O3 nanoparticles were dispersed into three different volume ratio of water: EG such as 40:60, 50:50 and 60:40 using a two-step method. The measurement of thermal conductivity was performed using KD2 Pro Thermal Properties Analyzer at working temperatures of 30 to 70 ̊C for volume concentration of 0.5 to 2.0%. The results indicate that the thermal conductivity increases with the increase of nanofluid concentration and temperature. While the percentage of ethylene glycol increase, the range of thermal conductivity decreases due to ethylene glycol properties. The measurement data of the nanofluids give maximum enhancement of thermal conductivity at condition 2.0% volume concentration, temperature of 70 ̊C and for all base fluid