http://umt-ir.umt.edu.my:8080/handle/123456789/3209 2026-04-05T17:05:10Z http://umt-ir.umt.edu.my:8080/handle/123456789/23390 Title: PREDICTIVE MODELLING FOR NITROGEN OXIDES EMISSION OF DIESEL ENGINES OPERATED WITH ALTERNATIVE PLASTIC FUEL Authors: MOHAMMAD NOR KHASBI BIN JARKONI, QD 281 .P9 M64 2024 2024-08-01T00:00:00Z http://umt-ir.umt.edu.my:8080/handle/123456789/22749 Title: GENETIC ALGORITHM-ARTIFICIAL NEURAL NETWORK (GA-ANN) AND GIS-BASED WIND MAPPING FOR WIND ENERGY EXPLOITATION: CASE STUDY IN MALAYSIA Authors: YONG KIM HWANG Abstract: Wind maps are required to determine wind resource over a given areas and they are an important component of wind energy exploration and exploitation. The intermittency of wind, geographical, and temporal variability, as well as the complex relationship between wind and their nature, have made accurate spatial wind speed modelling more difficult. The aim of this study was to contribute a novel and original solution to the problem of developing wind maps for wind energy exploitation in Malaysia. The main inputs of this study were 37 Malaysian Meteorological Department stations’ wind data and 3 installed wind masts’ data. The Genetic Algorithm-Artificial Neural Network model was applied in the Measure-Correlate- Predict method to substitute and fill missing data. Spatial modelling was conducted to establish wind maps by interpolating point sources of wind data and extrapolating the wind flow at 10-m and 50-m heights. The Genetic Algorithm-Artificial Neural Network model was also applied to training spatial modelling and to generate a nonlinear wind map. The results revealed that nonlinear wind map had addressed the overprediction issue of the wind maps in mountainous areas at the Cameron Highlands site, where the root mean squared error, and the mean absolute error decreased by 60.39% and 64.01% respectively. Overall, the nonlinear wind map improved simulated wind data by increasing accuracy and decreasing errors, up to 18.39% and 31.42% respectively. In conclusion, the results clearly prove that addressing the complex nonlinear relationship between the input parameters and output wind map decrease errors in the simulation of wind speed. 2025-01-01T00:00:00Z http://umt-ir.umt.edu.my:8080/handle/123456789/22529 Title: INVESTIGATION OF CO-PYROLYSIS OF FISH WASTE AND EMPTY FRUIT BUNCH: KINETIC AND THERMODYNAMIC PARAMETER ESTIMATION, PRODUCT YIELDS, AND BIOCHAR CHARACTERISATION Authors: NURUL IFFAH FARHAH MOHD YUSOF Abstract: This study explores the feasibility of utilising fish waste (FW) and empty fruit bunches (EFB) as feedstocks in co-pyrolysis. The research aims to optimize copyrolysis by estimating kinetic and thermodynamic parameters, evaluating product yields at various temperatures, and characterising biochar properties. Thermogravimetric analysis was employed at heating rates of 10, 20, and 30 °C/min, while kinetic and thermodynamic parameters were estimated using the Kissinger- Akahira-Sunose and Flynn-Wall-Ozawa methods. Reaction mechanisms were inferred using Criado plots. Product yields were measured in a tube furnace at 500 - 600 °C temperatures. The biochar was characterised using Fourier-transform infrared spectroscopy and scanning electron microscopy. Results revealed that activation energies (𝐸!) ranged from 25.56 to 170.76 kJ/mol, with 75FW:25EFB exhibiting the lowest 𝐸!. The complexity of co-pyrolysis reactions was evidenced by frequency factors with average values spanning from 1.65 x 108 to 5.66 x 1011 s-1. Reaction models identified included Power law, Avrami-Erofeev, Reaction-order, Geometrical contraction, and Diffusion models. The changes in enthalpy and entropy analyses highlighted the energy-intensive and thermodynamic equilibrium tendencies of the process, with the change in Gibbs free energy confirming the feasibility of copyrolysis. FW-rich mixtures produced higher biochar yields, attributed to their ash content, while EFB-rich mixtures favored biogas production due to higher volatile content. At 500 °C, 75FW:25EFB achieved 47 % biochar yield, which decreased to 42 % at 600 °C. EFB demonstrated superior gas production potential, reaching 67 % biogas yield at 600 °C. Co-pyrolysis resulted in improved liquid fuel and gas yields compared to individual feedstocks. Biochar analysis revealed that FW-rich biochar contained nitrogenous and aromatic functional groups, while lignocellulosic structures characterized EFB-rich biochar. Co-pyrolyzed biochar also showed enhanced carbonization, porosity, and functionality, making it ideal for soil amendment, water filtration, and renewable energy uses. The study highlights the advantages of optimizing FW and EFB ratios, with 75FW:25EFB and 50FW:50EFB emerging as the most effective blends for balanced product yields and biochar quality. This research demonstrates the potential of FW and EFB co-pyrolysis as a sustainable approach to optimize biochar production and convert waste into valuable resources. 2024-06-01T00:00:00Z http://umt-ir.umt.edu.my:8080/handle/123456789/22528 Title: DRIVING CYCLE DEVELOPMENT USING K-SHAPE AND CONVOLUTIONAL NEURAL NETWORK FOR ENERGY CONSUMPTION AND EMISSIONS ANALYSIS Authors: ARUNKUMAR A/L SUBRAMANIAM Abstract: One of the key challenges in the automotive industry is enhancing fuel efficiency and reducing emissions while meeting regulatory requirements. Common issues include the lack of context-specific driving cycles, limitations of conventional clustering methods in capturing non-linear driving behaviours, inefficiencies in real-time system integration with Siemens Totally Integrated Automation (TIA) Portal, and delays from traditional data exchange methods, leading to inaccuracies in fuel consumption and emission analysis. This research focuses on integrating MATLAB scripts for fuel consumption and emission analysis with real-time execution in the Siemens TIA Portal. A major focus is on seamless integration strategies between MATLAB and Siemens environments during execution to enhance analysis efficiency. The study involves collecting driving cycle data for Ipoh City using MATLAB Mobile and DC-TRAD and constructing the Ipoh City driving cycle using the K-shape clustering technique, which identifies complex patterns more accurately than conventional clustering methods. Additionally, a convolutional neural network (CNN) algorithm is applied for effective and precise driving cycle development. The research includes a detailed analysis of execution cycle time, fuel consumption, and emissions across both MATLAB and Siemens environments. A significant improvement in execution performance is achieved, with model cycle times in the Siemens environment reduced by over 90%, reaching a maximum of 100 milliseconds compared to 45 seconds in MATLAB. This substantial reduction in cycle time is accomplished without compromising accuracy, as the results from MATLAB are successfully replicated in the Siemens environment, leading to the selection of route 6 as the optimized route for the Ipoh City driving cycle. Addressing challenges related to computational power and system integration for real-time processing, this research outlines strategies to optimize MATLAB scripts for real-time deployment within Siemens systems. Ultimately, this integration aims to provide efficient and accurate solutions for analysing energy consumption and emissions in automotive applications, contributing valuable advancements to the field. 2025-06-01T00:00:00Z