C.W. Mohd Noor

Bio: C.W. Mohd Noor is an academic researcher from Universiti Malaysia Terengganu. The author has contributed to research in topics: Diesel engine & Brake specific fuel consumption. The author has an hindex of 4, co-authored 6 publications receiving 200 citations. Previous affiliations of C.W. Mohd Noor include Universiti Malaysia Pahang.

Biodiesel as alternative fuel for marine diesel engine applications: a review.

Abstract: Transportation and shipping activities are major contributor to air pollution at sea where most of it occurs as a result of exhaust emissions from ships. Stringent emission limitations enforced by the International Maritime Organization have hastened the need to find a new alternative fuel for marine diesel engines. Thus, biodiesel fuel was chosen as one of the environmentally friendly alternative energy that can reduce ship toxic gas emissions and at the same time reduces dependence on petroleum-based fuels. Therefore, the purpose of this paper is to provide a comprehensive review of biodiesel as an alternative fuel for marine diesel engine applications. This review covers the biodiesel fuel background, engine performance, history, recent progress, engine warranty, issues, challenges, and possible solutions on using biodiesel for marine applications. A significant number of literatures from indexed journals were cited accordingly. The results of previous studies had shown that the use of biodiesel would mostly increase the amount of brake specific fuel consumption and nitrogen oxide gas while conversely reducing other toxic gas emissions. Although a number of issues and challenges arise, most marine engine manufacturers give conditional warranty against the use of biodiesel in the engines. The study concluded that biodiesel and its blends have a bright future in the marine sector, provided some of the highlighted issues can be solved.

Prediction of Marine Diesel Engine Performance by Using Artificial Neural Network Model

Abstract: This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various engine speeds and loads. An ANN prediction model was developed based on a standard back-propagation Levenberg–Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data and output from the mathematical model. The results showed that the ANN model provided good agreement with the experimental data with a coefficient of determination (R2) of 0.99. The prediction error of the ANN model is lower than the mathematical model. The present study reveals that the artificial neural network approach can be used to predict the performance of a marine diesel engine with high accuracy

RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)

Abstract: Welding processes are considered as an essential component in most of industrial manufacturing and for structural applications. Among the most widely used welding processes is the shielded metal arc welding (SMAW) due to its versatility and simplicity. In fact, the welding process is predominant procedure in the maintenance and repair industry, construction of steel structures and also industrial fabrication. The most important physical characteristics of the weldment are the bead geometry which includes bead height and width and the penetration. Different methods and approaches have been developed to achieve the acceptable values of bead geometry parameters. This study presents artificial intelligence techniques (AIT): For example, radial basis function neural network (RBF-NN) and multilayer perceptron neural network (MLP-NN) models were developed to predict the weld bead geometry. A number of 33 plates of mild steel specimens that have undergone SMAW process are analyzed for their weld bead geometry. The input parameters of the SMAW consist of welding current (A), arc length (mm), welding speed (mm/min), diameter of electrode (mm) and welding gap (mm). The outputs of the AIT models include property parameters, namely penetration, bead width and reinforcement. The results showed outstanding level of accuracy utilizing RBF-NN in simulating the weld geometry and very satisfactorily to predict all parameters in comparison with the MLP-NN model.

Application of artificial neural network for prediction of marine diesel engine performance

Abstract: This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

A Study of Software Approach for Predicting Weld Bead Geometry in Shielded Metal Arc Welding (SMAW) Process

Abstract: The prediction of the optimal weld bead width is an important aspect in shielded metal arc welding (SMAW) process as it is related to the strength of the weld. This paper focuses on investigation of the development of the simple and accurate model for prediction of weld bead geometry. The experiment used welding current, arc length, welding speed, welding gap and electrode diameter as input parameters. While output parameters are bead width, depth of penetration and weld reinforcement. A number of 33 mild steel plate specimens had undergone the SMAW welding process. The experimental data was used to develop mathematical models using SPSS software. The actual and predicted values of the weld bead geometry are compared. The proposed models shows positive correlation to the real process.

(Energy, SCI-Q1, Top 10%) - Engine performance and emissions using Jatropha curcas, Ceiba pentandra and Calophyllum inophyllum biodiesel in a CI diesel engine

Abstract: Biodiesel is a recognized replacement for diesel fuel in compressed ignition engines due to its significant environmental benefits. The purpose of this study is to investigate the engine performance and emissions produced from Jatropha curcas, Ceiba pentandra and Calophyllum inophyllum biodiesel in compressed ignition engine. The biodiesel production process and properties are discussed and a comparison of the three biodiesels as well as diesel fuel is undertaken. After that, engine performance and emissions testing was conducted using biodiesel blends 10%, 20%, 30% and 50% in a diesel engine at full throttle load. The engine performance shows that those biodiesel blends are suitable for use in diesel engines. A 10% biodiesel blend shows the best engine performance in terms of engine torque, engine power, fuel consumption and brake thermal efficiency among the all blending ratios for the three biodiesel blends. Biodiesel blends have also shown a significant reduction in CO2, CO and smoke opacity with a slight increase in NOx emissions.

Decarbonising ships, planes and trucks: An analysis of suitable low-carbon fuels for the maritime, aviation and haulage sectors

Abstract: The high environmental impacts of transport mean that there is an increasing interest in utilising low-carbon alternative energy carriers and powertrains within the sector. While electricity has been mooted as the energy carrier of choice for passenger vehicles, as the mass and range of the vehicle increases, electrification becomes more difficult. This paper reviews the shipping, aviation and haulage sectors, and a range of low-carbon energy carriers (electricity, biofuels, hydrogen, and electrofuels) that can be used to decarbonise them. Energy carriers were assessed based on their energy density, specific energy, cost, lifecycle greenhouse gas emissions, and land-use. In terms of haulage, current battery electric vehicles may be technically feasible, however the specific energy of current battery technology reduces the payload capacity and range when compared to diesel. To alleviate these issues, biomethane represents a mature technology with potential co-benefits, while hydrogen is close to competitiveness but requires significant infrastructure. Energy density issues preclude the use of batteries in shipping which requires energy dense liquids or compressed gaseous fuels that allow for retrofits/current hull designs, with methanol being particularly appropriate here. Future shipping may be achieved with ammonia or hydrogen, but hull design will need to be changed significantly. Regulations and aircraft design mean that commercial aviation is dependant on drop-in jet fuels for the foreseeable future, with power-to-liquid fuels being deemed the most suitable option due to the scales required. Fuel costs and a lack of refuelling infrastructure were identified as key barriers facing the uptake of alternatives, with policy and financial incentives required to encourage the uptake of low-carbon fuels.