Climate, Environment and Biodiversity

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Active vibration control of flexible beam incorporating recursive least square and neural network algorithms.
(Universiti Teknologi Malaysia, 2017) Abd. Jalil, Nurhanafifi
In recent years, active vibration control (AVC) has emerged as an important area of scient ific study especially for vibrat ion suppression of flexible structures. Flexible structures offer great advantages in contrast to the conventional structures, but necessary action must be taken for cancelling the unwanted vibration. In this research, a simulation algorithm represent ing flexible beam with specific condit ions was derived from Euler Bernoulli beam theory. The proposed finite difference (FD) algorithm was developed in such way that it allows the disturbance excitat ion at various points. The predicted resonance frequencies were recorded and validated with theoretical and experimental values. Subsequent ly, flexible beam test rig was developed for collecting data to be used in system ident ificat ion (SI) and controller development. The experimental rig was also utilised for implementation and validat ion of controllers. In this research, parametric and nonparametric SI approaches were used for characterising the dynamic behaviour of a lightweight flexible beam using input - output data collected experimentally. Tradit ional recursive least square (RLS) method and several artificial neural network (ANN) architectures were utilised in emulat ing this highly nonlinear dynamic system here. Once the model of the system was obtained, it was validated through a number of validation tests and compared in terms of their performance in represent ing a real beam. Next, the development of several convent ional and intelligent control schemes with collocated and non-collocated actuator sensor configurat ion for flexible beam vibrat ion attenuation was carried out. The invest igat ion involves design of convent ional proportional-integral-derivat ive (PID) based, Inverse recursive least square active vibrat ion control (RLS-AVC), Inverse neuro active vibration control (Neuro-AVC), Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain controllers. All the developed controllers were tested, verified and validated experimentally. A comprehensive comparat ive performance to highlight the advantages and drawbacks of each technique was invest igated analyt ically and experimentally. Experimental results obtained revealed the superiorit y of Inverse RLS-AVC with gain controller over convent ional method in reducing the crucial modes of vibration of flexible beam structure. Vibration attenuation achieved using proportional (P), proportional-integral (PI), Inverse RLS-AVC, Inverse Neuro- AVC, Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain control strategies are 9.840 dB, 6.840 dB, 9.380 dB, 8.590 dB, 17.240 dB and 5.770 dB, respectively.
Agro-ecological evaluation of sustainable area for citrus crop production in Ramsar District, Iran
(Universiti Teknologi Malaysia, 2017-07) Zabihi, Hasan
Citrus growing is regarded as an important cash crop in Ramsar, Iran. Ramsar District has a temperate climate zone, while citrus is a sub-tropical fruit. Few studies on citrus crop in terms of negative environmental factors have been carried out by researchers around the world. This study aims to integrate Geographical Information System (GIS) and Analytical Network Process (ANP) model for determination of citrus suitability zones. This study evaluates the agro-ecological suitability, determine potentials and constraints of the region based on effective criteria using ANP model. ANP model was used to determine suitable, moderate and unsuitable areas based on (i) socio-economic, morphometry and hydro-climate factors using 15 layers based on experts’ opinion; (ii) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image of the year 2003 with 98.45% overall accuracy, and (iii) developed Multiple Linear Regression (MLR) model for citrus prediction. Thereby, weighted overlay of 15 factors was obtained using GIS. In this study, the citrus orchards map of 2003 and the new map of the citrus areas of 2014 namely Citrus State Development Program (CSDP) of the study area were compared. The results of this study demonstrated: (i) suitable areas (free risk areas) based on negative environmental factors and areas which are susceptible to citrus plantation; (ii) high-risk areas which are unsuitable for citrus plantation, and (iii) the high weights derived by ANP model were assigned to altitude, frost and minimum temperature. The MLR model was successfully developed to predict citrus yield in Ramsar District by 10% error. The MLR model would propose optimum citrus crop production areas. As conclusion, the main outcome of this study could help growers and decision makers to enhance the current citrus management activities for current and future citrus planning
An optimized volume determination method based on aerial photogrammetry approach for sustainable environment
(Universiti Teknologi Malaysia, 2022) Yusoff, Ahmad Razali
Beach erosion occurs continuously along the shoreline due to the interaction of natural processes. The beach volume aspect is critical to represent the entire profile of beach evolution. Most advanced survey techniques are costly, requiring arduous control survey setup effort to measure the beach volume. Unmanned Aerial Vehicle (UAV) systems have recently attracted interest in the mapping community, which provides similar products to aircraft systems and comes with Structure from Motion Multi View Stereo (SfM-MVS) technology at a lower cost. The UAV photogrammetric beach volume mappings were mostly conducted by using nonoptimal methods such as low altitude mapping, bundle amount of Ground Control Points (GCPs) distribution, and uncalibrated Ground Sample Distance (GSD). Hence, this study aims to invent an optimized volume determination method accurately using Unmanned Aerial Vehicle (UAV) photogrammetry mapping to minimize work time and perform less laborious beach mapping work for sustainable environment study through several objevtives, firstly, to investigate the camera calibrations, UAV altitudes, and GCPs distribution for optimized beach volume UAV mapping method, secondly, to modify the SfM-MVS photogrammetric volume formula for the development of accurate and optimized beach volume mapping method, and thirdly, to analyze the optimized beach volume mapping method produced. The study was conducted at Desaru Beach, Universiti Teknologi Malaysia (UTM), Irama Beach 1, and Irama Beach 2. Various GCP distributions (3, 4, 5, 6, 8, 10, 15, and 20 GCPs) and UAV mapping altitude differences at 10m, 20m, 30m, 40m, 50m, 60m, 80m, and 100m were studied. This study utilized robust statistical analysis to investigate the beach volume measurement trend and UAV mapping behaviour from various GCP distributions and different UAV altitude mapping. The study results indicated that 4GCP and 100m altitude UAV mapping were the optimal methods for measuring beach volume using UAV with minimal mapping work. Based on the modified photogrammetric volume calculation methods in determining the beach volume, it is evident that the beach volume value is significant. In conclusion, this study shows that the modified photogrammetric volume formula provides better accuracy than the existing volume formula.
An optimum closed loop supply chain network model in a stochastic product life cycle context.
(Universiti Teknologi Malaysia, 2016) Madadi, Najmeh
Nowadays, closed loop supply chain network (CLSCN) receives considerable attention due to the growing awareness of the environmental destruction and depletion of natural resources. The establishment of a CLSCN is considered as a strategic decision that requires a lot of effort and intensive capital resources. Therefore, it is very crucial to make CLSCN design decisions taking into account multiple facets of uncertainties. Literature reviews to date reveals that uncertainties in product life cycle (PLC) or what has been called “product diffusion” have been vastly ignored. Particularly, the deterministic nature of the proposed diffusion models is a severe defect that can hinder the involvement of real-world uncertainties in design of a CLSCN. This study is an attempt to fill this gap by developing a costefficient CLSCN model for a product with dynamic and stochastic diffusion into the market that leads to an optimum design of the targeted CLSCN. Firstly, a geometric Brownian motion (GBM)-based diffusion forecast method was proposed and validated using a conventional approach namely, Holt’s method. Then, a two-stage stochastic programming mathematical model for optimum design of the targeted CLSCN was developed. The developed stochastic CLSCN model provides the optimum design of the targeted CLSCN utilizing the values predicted for the product diffusion through the PLC based on the proposed forecast method. The developed mathematical model addresses two types of decisions namely, “here and now” and “wait and see” decisions within the PLC. The “here and now” decisions were made in the first stage. The results show optimum values for decisions concerning configuration of the CLSCN as well as dynamic capacity allocation and expansion decisions through the PLC. However, the “wait and see” decisions are made in the second stage within the frame provided by the first-stage solutions. Here, the results portray optimum values for decisions concerning with the flow quantities between the CLSCN facilities, backorder and inventory levels, and recovery of returns through the PLC. In order to test the applicability of the developed CLSCN model, the mathematical model was coded by CPLEX software and solved for secondary data from the case study from previous case study in literature. Finally, a sensitivity analysis was performed to investigate the effect of diffusion uncertainty on the total cost of the CLSCN, its configuration, and production capacity allocations and expansions. The results of the sensitivity analysis revealed that, for the higher levels of diffusion uncertainty, the total cost imposed to the supply chain increases due to the increase in the allocated production capacity as well as the increase in the number of involved facilities.
Assessment of emerging pollutants in Skudai river and its treatability at downstream water treatment plant
(Universiti Teknologi Malaysia, 2021) Talib @ Harun, Juhaizah
Emerging Pollutants (EPs) are synthetic or naturally occurring compounds currently detected in water environment. These chemicals such as surfactants, pharmaceuticals, personal care products (PCPs) and pesticides could cause adverse ecological and human health effects which include alteration of the normal function of endocrine systems of human and animals. With variation of potential sources, determination of their presence is also a difficult and costly. Different treatment technologies to remove the EPs for drinking water have been studied, which include adsorption, chemical oxidation and membrane filtration. Nevertheless, these technologies are relatively costly in terms of capital, operation and maintenance. This study was carried out to identify the best technique in extracting the EPs from water for screening purposes, to assess their presence in River Skudai and to determine the ability of downstream water treatment plant (WTP) to remove the EPs. Identification approach and solvent was carried out through extensive literature review and trial tests. Samples were taken from eight sampling points in Skudai River and five points in the WTP. Samples were pretreated using solid phase extraction (SPE) method and were analysed using Liquid Chromatography-Mass Spectrometry Detection (LCMSQTOF) for the river water sample and using Gas Chromatography-Mass Spectrometry (GCMS) for the treatment plant water sample. It was surmised that the extraction of EPs is largely based on polarity. The acetonitrile and methanol are highly polar solvents that can achieve high yields of EPs. EPs detected in Skudai River can be categorized into three groups, namely pharmaceutical (decylamine, hexadecyl isocyanate, methotrexate, butirosin A, tridodecylamine and 4-vinylcyclohexene), PCPs (tetradecylamine, limonene, oleylamine, and diethanolamine) and EDCs (styrene, ethylbenzene, phthalic and alfa-methyl styrene). The concentration of styrene ranged from 45 µg/L to 203 µg/L with an increasing trend towards downstream of the river. All the EPs detected are classified as toxic and carcinogenic compounds. As for the WTP, the coagulation process successfully removed endosulfan, chlorothalonil, and ethylbenzene while sedimentation removed 50% of benzene, 50% of triazine, along with 100% of ibuprofen and bisphenol A (BPA). Filtration and chlorination process did not remove styrene or triazine. Trihalomethanes (THMs) which are classified as EDCs were formed after chlorination process. Using polynomial multivariate, the removal rate of triazine in the water treatment plant was modelled. A nonlinear regression design was successfully applied to model the response as a polynomial function based on selected independent. Polynomial multivariate was further used to conduct and evaluate the effectiveness of coagulation and sedimentation process. The findings of this study indicate that different types of EPs can be found in Skudai River. While many can be successfully removed in the conventional water treatment plant, more efforts are needed to ensure that the environment and human health are protected from the hazardous EPs.
Assessment of urban air quality in Makassar South Sulawesi Indonesia
(Universiti Teknologi Malaysia, 2013) Sattar
An assessment of the urban air quality in Makassar area, which covers SO2, CO, NO2, O3, Pb, and TSP sampled over a period of eleven years (i.e 2001 to 2011), with PM10 monitored for six years (2006-2011) are discussed and presented in this thesis. The air quality data were obtained from secondary measurements made by the Office of Ministry of Environment Sulawesi, Maluku and Papua, the Environment Board of the Province of South Sulawesi, and the Environmental Agency of Makassar City. In addition, the primary data of airborne PM10 concentration sampled on a weekly basis for a period of one year (i.e February 2012 to January 2013) at one Makassar site are also reported. PM10 was sampled using a standard size selective high volume air sampler and analyzed for its elemental, black carbon and ionic species constituents. Results showed that the overall average concentrations of SO2, CO, NO2, O3, Pb, TSP and PM10 measured at eight monitoring sites of Makassar was 74.9 µg/m3, 1007 µg/m3, 42.5 µg/m3, 53.7 µg/m3, 0.70 µg/m3, 179 µg/m3, 53.9 µg/m3, respectively. The concentration of the particulate matter found in the study area was typically influenced by the dry and wet season experienced in the region. A total of nineteen elemental components (i.e Ag, Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Ti and Zn), four ionic species (i.e Cl-, NO3 -, SO4 2-, NH4 +) and black carbon, together constituted a mere 28.8% of the PM10 mass concentration, while the remaining 71.2% are yet to be explained. However, the use of a more rigorous source apportionment model based on positive matrix factorization (PMF) successfully identified six major sources of air pollution in the area, which include marine, motor vehicle, road dust, soil dust, industry and biomass burning, each contributing 25%, 24%, 16%, 13%, 12%, and 10% of the particulate mass concentration, respectively
Atmospheric PM2.5 and particle number concentration in semi-urban industrial-residential airshed
(Universiti Teknologi Malaysia, 2020) Dahari, Nadhira
Air pollution is one of the crucial factors that cause premature death and health problems. Fine particulate matter (PM2.5) has a high association with adverse health effects due to its capability to penetrate deep into the human respiratory system. The deterioration of air quality in Malaysia, especially Johor Bahru city, is worrying due to the swift industrial, transportation as well as housing expansion. Air pollution has a closer relationship with the particle number concentration (PNC) rather than the particle mass concentration. However, measurement of the PM2.5 is normally reported in particle mass concentration. Due to the light-weighted small particle sizes that dominate the PNC, they are accounted for only a few percent of the total particle mass concentration. Thus, these small particles could be neglected if the toxicological effects are determined primarily by the mass concentration rather than the PNC. This study aims to investigate the 24 h mean PM2.5 mass concentrations, meteorological parameters and PNC, besides determining the concentrations of the trace metals and water-soluble inorganic ions of the PM2.5 pollutant collected at the industrial-residential airshed of Skudai, Johor Bahru. This research analysed the source apportionment of the PM2.5 composition and the relationship of the PM2.5 mass concentrations with PNC. The meteorological variables, PNC data and PM2.5 samples were collected from August 2017 until January 2018. The source apportionment of the PM2.5 composition were determined using Positive Matrix Factorisation (PMF). This study found that the highest 24 h PM2.5 mass concentration is 44.6 µgm-3, with a mean value of 21.85 µgm-3 throughout the SW through the NE monsoon. 43.33% of the daily PM2.5 mass exceeded the 24 h World Health Organization Guideline, while 8.33% of the concentration exceeded the 24 h Malaysia Ambient Air Quality Standard. The ambient temperature throughout the monsoon seasons shows a significant positive correlation (p < 0.05) with PM2.5 mass (r2 = 0.43 to r2 = 0.54), while the wind speed (r2 = -0.23 to r2 = -0.01) and the relative humidity (r2 = -0.47 to r2 = -0.27) show negative correlations. The rainfall on the other hand shows weak correlation towards PM2.5 mass. The accumulation mode particles (0.27 µm < Dp < 1.0 µm) corresponded to 94~98% of the total particle number concentration, with highest hourly mean of 372.20 #cm-3 during the SW monsoon. The accumulation mode has the highest correlation value of r2 = 0.8701 among the other particle size bins. The major trace elements identified were Fe (279.2 ± 69.2 ngm-3), Ba (200.1 ± 57.2 ngm-3), Zn (133.2 ± 67.6 ngm-3), Mg (116.3 ± 43.8 ngm-3) and Al (104.1 ± 30.6 ngm-3). For inorganic ions, the secondary inorganic aerosols (SIA) were highly contributed by NO3- (639.9 ± 138.1 ngm-3), SO42- (556.9 ± 203.0 ngm-3) and NH4+ (424.1 ± 106.1 ngm-3). Despite the anthropogenic activities as the sources of particulates, a minor fraction of pollutants may also due to the regional transboundary transport. The PMF analysis shows that non-combustion traffic source is the main contributor to the ambient PM2.5 (25.4 %). The six predominant sources identified were (1) mineral dust pollution (4.2 %), (2) source of mixed road dust and biomass burning (18.1%), (3) mixed secondary inorganic aerosol and road dust emission (18.1%), (4) emission of the non-combustion traffic source (25.4%), (5) industrial emission (18.1 %) and (6) undefined (16.1 %). The comprehensive findings of this study may support the need to control the PM2.5 sources.
Biodiesel from palm waste cooking using immobilized lipase in modified PVA-alginate sulfate beads
(Universiti Teknologi Malaysia, 2021) Hasan, Nor Badzilah
Malaysia is currently the world’s second largest palm oil producer, which accounts for 39% of world palm oil production. About 0.5 million tonnes of waste cooking oil is generated annually in Malaysia. Improper disposal of waste cooking oil leads to the environmental pollution particularly in land and water. To overcome these problems, this study aimed to use palm waste cooking oil as a feedstock to produce biodiesel as an alternative to the limited and non-renewable sources of conventional petroleum. Apart from that, utilization of lipase as biocatalyst to produce biodiesel has advantages over chemical catalyst as the reaction can be performed under mild conditions and simple separation process. This study investigated the production of biodiesel from palm waste cooking oil using immobilized Candida rugosa lipase (CRL) in Polyvinyl Alcohol (PVA) alginate sulfate beads. The One-factor-at-a-time (OFAT) method was used in order to select a suitable range of variables before statistical analysis was performed. The Design-Expert software was used as a statistical tool to operate Central Composite Design (CCD) for optimization of significant factors. The statistical analysis was used in order to achieve maximum biodiesel production and evaluate the effect of each variable and their interaction of biodiesel yield. Four main parameters responsible for the yield of transesterification were analyzed; waste cooking oil (WCO) to methanol ratio, temperature, water content and enzyme content. The experimental results showed that the highest conversion was 85.14% under condition oil to methanol ratio 7:1, 10% of water content, 40% wt of enzyme loading and temperature 37.5ºC. The regression model of the ANOVA was found to be significant with p<0.001 and R2= 0.9737. As a conclusion, the results proved that the immobilization method of C. rugosa lipase in PVA-alginate-sulfate beads is reliable and can enhance conversion of palm waste cooking oil to biodiesel.
Blade fault diagnosis using artificial intelligence technique
(Universiti Teknologi Malaysia, 2016) Ngui, Wai Keng
Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis.
Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data
(Universiti Teknologi Malaysia, 2014-12) Rasib, Abd. Wahid
Various measurements methods have been used to determine the validity of the information produced for carbon sequestration especially in tropical rainforests. Generally, these methods can be divided into two major categories which are the micrometeorological and biometric approaches. The former uses remote sensing and tower flux and the latter refers to field direct measurement of biomass. Presently, use of a single measurement approach has sometimes caused uncertainty in the accuracy of carbon sequestration in terms of the source or sink of carbon in these forests. Thus, this study proposed and developed a new model for carbon sequestration generated from the integration of remote sensing and biometric approach. This study was carried out in Pasoh Forest Reserve and the model was used for up-scaling to estimate the carbon concentration of the entire forest. Data for remote sensing were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and the biometric approach was based on tree census and litterfall observations. The results for the years 2000 until 2009 based on the new model showed that the carbon sequestration was a carbon source with increments ranging between -1.421 t ha-1yr-1 to -16.573 t ha-1yr-1, a mean value of -8.526 t ha-1yr-1 and Root Mean Square Error (RMSE) 2.916. The use of the new model revealed that there is a 6% accuracy improvement in the results as compared to a single-based remote sensing model. As a conclusion, the integration of approaches for a new model for carbon sequestration is more efficient than the use of a single approach. Furthermore, the new model is suitable for validating and calibrating global automatic climate products
Cementation factor and carbonate formation properties correlation from well logs data for nasiriya field
(Universiti Teknologi Malaysia, 2016) Kadhim, Fadhil Sarhan
The cementation factor has specific effects on petrophysical and elastic properties of porous media. A comprehensive investigation of carbonate rock properties which have an interlock with the cementation factor was done through core analysis and well log data. Five wells in Nasiriya oilfield, which is one of the giant fields consists of the carbonate reservoirs in the Middle East were used in this study. The study was made across the Mishrif and Yamamma carbonate formations in the Nasiriya oilfield. Neurolog software (V5, 2008) was used to digitize the scanned copies of available logs while Interactive Petrophysics software (IP V3.5, 2008) was used to determine the properties of studied formations. The average cementation factor values were calculated from the F-PHI plot and Gomez methods and compared with Pickett method. Petrophysical and dynamic elastic properties were determined from well logs. In this study, a new approach was introduced to obtain correlations of cementation factor to petrophysical and dynamic elastic properties of Mishrif and Yamamma formations. An artificial neural network platform was used to determine these correlations depending on the determined properties of studied formations. The neural network model used two different training algorithms; Gradient Descent with Momentum and Levenberg–Marquardt. Results show that the plot of average core data and calculated data from IP software of porosity and permeability gave a good correlations coefficient of R2 = 0.86034 to 0.94303. Generally, cementation factor values obtained from all methods are found to be less than two. In addition, cementation factor values also increased with increasing depth of the studied formations. An efficient performance and excellent prediction of cementation factor have been obtained with less than 10-4 and 10-8 mean square error from both artificial neural network models. Three saturation models were used to estimate water saturation of carbonate formations, which are simple Archie equation, dual water model and Indonesian model. The Indonesian water saturation model recorded the lowest percentage error in comparison with water saturation of core samples, and the water saturation in Yamamma formation was higher than in the Mishrif formation. The accurate determination of a cementation factor gives reliable saturation results.
Combustion performance of various syngas compositions in swirl combustor.
(Universiti Teknologi Malaysia, 2017) Samiran, Nor Afzanizam
The challenge of using syngas in combustion system is the composition variability and low calorific value. Syngas mainly consists of H2 and CO and other sub component such as N2, CO2 and H2O. High H2-enriched syngas would result in high NOx production for some combustion cases. Whereas high CO concentration is posed with stability issues. The presence of sub-component as a diluent improves the emission characteristic but slows down the chemical reaction rate and calorific values. The variability in syngas strongly depends on the type of gasification technique, feedstock and oxidation agent. The present study therefore aims to investigate the combustion performance of different configuration in composition of syngas using premixed swirl mode technique. Various simulated syngases of CO and H2-dominant syngas or CO-rich and H2-rich syngas were used as fuels to evaluate the performance of emissions, diluent effects, lean blowout limit and flame structure. Further investigation on combustion of syngas was fundamentally conducted using numerical approach in which a comparative study on flame structure and reaction zone species were evaluated between those syngas fuels. Measurement by gas analyser was used to evaluate the performance of combustion emission and direct photography was used to analyse the flame appearance. Lean blowout test was performed by gradually reducing the fuel flowrate until flame blowout occur. For numerical method, two different combustion models namely flamelet generated manifold (FGM) and chemical equilibrium (CE) models were implemented to predict the combustion characteristic of syngas and the result obtained was then validated with experimental results. The results indicate that high CO-rich syngas shows evidently less NOx and CO emissions as compared to the other dominant CO fuel. Higher fraction of CO2 dilution results in reduction of NOx emissions, with pronounced impact on fuel-rich cases. There was minimal effect on CO emissions with increased dilution of CO2. The lean blowout limit test shows that higher CO content results in blowout at higher equivalence ratio. Addition of hydrocarbon fuel such as CH4 or hydrogen extends the blowout limit as the flammability limit is stretched to ultra-lean region. Dilution of unreactive CO2 in syngases results in higher lean blowout limit. Higher fraction of H2 in syngas produces both lower NOx emission and lean blowout limits. The optimum characteristic of high H2-rich syngas is also validated by numerical approach using FGM method. The numerical computation found that the increasing content of H2 in syngas results in lower flame temperature, subsequently leading to reduced flame height and lower NO emissions.
Comparative model for classification of forest degradation
(Universiti Teknologi Malaysia, 2015-03) Mehdawi, Ahmed A.
The challenges of forest degradation together with its related effects have attracted research from diverse disciplines, resulting in different definitions of the concept. However, according to a number of researchers, the central element of this issue is human intrusion that destroys the state of the environment. Therefore, the focus of this research is to develop a comparative model using a large amount of multi-spectral remote sensing data, such as IKONOS, QUICKBIRD, SPOT, WORLDVIEW-1, Terra-SARX, and fused data to detect forest degradation in Cameron Highlands. The output of this method in line with the performance measurement model. In order to identify the best data, fused data and technique to be employed. Eleven techniques have been used to develop a Comparative technique by applying them on fifteen sets of data. The output of the Comparative technique was used to feed the performance measurement model in order to enhance the accuracy of each classification technique. Moreover, a Performance Measurement Model has been used to verify the results of the Comparative technique; and, these outputs have been validated using the reflectance library. In addition, the conceptual hybrid model proposed in this research will give the opportunity for researchers to establish a fully automatic intelligent model for future work. The results of this research have demonstrated the Neural Network (NN) to be the best Intelligent Technique (IT) with a 0.912 of the Kappa coefficient and 96% of the overall accuracy, Mahalanobis had a 0.795 of the Kappa coefficient and 88% of the overall accuracy and the Maximum likelihood (ML) had a 0.598 of the Kappa coefficient and 72% of the overall accuracy from the best fused image used in this research, which was represented by fusing the IKONOS image with the QUICKBIRD image as finally employed in the Comparative technique for improving the detectability of forest change
Context-aware collaboration tool for precast supply chain management
(Universiti Teknologi Malaysia, 2016) Mohammad Abedi
Precast concrete supply chain management is associated with numerous activities, a wide range of stakeholders, and various processes. Poor information management, lack of collaboration, poor levels of communication, and lack of integration are the major issues identified which could result in adverse consequences for the completion of precast construction projects. For effective collaboration and communication, industry requires a system that is able to deliver up-to-date information to the stakeholder. The purpose of this research is to develop a context-aware collaboration system for precast concrete supply chain management. Context-aware computing is capable of identifying the various users’ needs related to their roles, activities, location, and the time required to deliver specific information and services. The user-requirements study with the precast stakeholders in Malaysia was conducted through a pilot and detailed semi-structured interviews to understand the user needs in the precast concrete supply chain phases. The information obtained from these studies led to the formulation of the system design goals, functional specifications, and system-architecture development and, eventually, to the development of a prototype context-aware collaboration system. The prototype was evaluated, and validated by precast stakeholders. Findings showed that 80% of respondents agreed that the proposed concept would be able to provide the precast stakeholders with appropriate and up-to-date information for them to improve their decision-making processes. This research concludes that the concept of a context-aware collaboration system is feasible and able to provide a progressive collaborative process and intelligent information-management tool which will enhance collaboration and improve communication for precast stakeholders
Cooling effect of urban river reserve vegetation structure in outdoor environment
(Universiti Teknologi Malaysia, 2019) Omar, Siti Rahmah
Urban river reserve or urban riparian is recognised as urban green space in Malaysia. According to the Department of Irrigation and Drainage (DID), this space is necessary for the stabilisation of riverbank, biodiversity, and water quality. However, there is no study conducted on the advanced functions of urban river reserve such as thermal reduction in a hot and humid climate. It is believed that vegetated urban river reserves can promote thermal reduction and improve microclimate conditions in urban areas. In Malaysia, the urban river reserve design is spatially constrained, which creates severe limitations for the thermal reduction strategy. The absence of a cohesive governmental guideline within different agencies further worsens the problem. This study has investigated the extensiveness of urban river reserve indicator as an urban green space and its associated thermal reduction properties. The cooling effect of urban river reserve and factors affecting the distribution of cooling effect were also determined. A total of 31 different zones of urban river reserves were formed to assess physical and vegetation compositions. Five representations were then selected for detailed field measurements. A surface-plant-air microclimate model and wind tunnel simulation were used to predict the impact of modification on measured parameters. The results showed that none of the surveyed urban river reserves was categorised as green infrastructure aquatic system typologies, and most of the areas were neglected spaces due to incivilities aspects. Most of the existing riverside urban green spaces were merely vegetative walls that divided the river and their adjacent area that cannot provide significant thermal reduction function. The urban green spaces which are vegetative wall experienced higher average diurnal temperature of 31.9°C compared to 30.6°C for urban river reserve which has been fully developed into a green park. The built-up areas adjacent to the urban river reserves with more than 80% of non-vegetated surface experienced higher average temperature with a difference of 2.7°C (±SD=1.6). The thermal reduction effect is substantial within the radius of 30m from the river edge. The results showed that the cooling effect differs based on green area coverage, type of vegetation coverage, vegetation formation, specific green volume and vertical clearance height. The minimum vertical clearance height of 2m between tree canopy and understory growth was necessary to ensure sufficient convection over evaporative surface for increased ventilation. Therefore, vegetation structure and detail specification are crucial in extending thermal cooling effect on areas straddling the river. This study is useful for future use of urban development policies and design. The application of these parameters could improve the living conditions for future river developments.
Critical success factors structural model for energy management of Malaysia public universities
(Universiti Teknologi Malaysia, 2016-08) Abdullah Saleh, Alia
Universities are increasingly consuming energy to support various activities. A large population of staff and students in Malaysian universities has led to excessive energy consumption which directly gives an impact to the environment. Thus, Malaysian Ministry of Higher Education insisted all parties involved to take the initiatives to reduce energy consumption. Frequent commentaries in the literature have stated by identifying Critical Success Factors (CSFs) and continually measured using Key Performance Indicators (KPIs) will ensure successful effective performance for the organization. However, formal relationship between them have not been defined and established. This study intended to identify the CSFs group and CSFs for energy management (EM) towards a sustainable university and to determine the relationship between the identified CSFs group for EM with the KPIs towards a sustainable university, simultaneously developing the CSFs structural model. The first objective is achieved through a thoroughly review of literature, followed by experts interview to verify the CSFs group and CSFs gained from literature review, then questionnaire survey which involve five research universities in Malaysia. 650 questionnaires were distributed with only 222 sets were completed. The first objective was analyzed by Statistical Package for Social Science 19 (SPSS 19) which examine internal consistency reliability including Cronbach’s Alpha, construct validity, factor analysis and lastly mean and standard deviation to see the importance of each CSFs. According to the results, five CSFs group with 23 CSFs for EM were identified which are; top management support, comprehensive EM team, awareness, strategic maintenance management and good relationship among stakeholders. Then, in order to achieve objective two, findings obtained from first objective was used. The Partial Least Square- Structural Equation Modeling 3.0 (PLS-SEM 3.0) was applied to analyze the data. The analysis was carried out in two stages which are the measurement model analysis and the structural model analysis. The assessment of measurement model has gone through the process of reliability and validity resulted in the removal of several items due to the indicator loading value below than the suggested value. In addition, several items from KPIs were removed due to high collinearity issues such as perfect correlation of items. The second step of the model assessment is evaluating the structural model, which demonstrated explanatory and predictive power. The result of the assessment support the hypotheses that only two CSFs group namely strategic maintenance management and top management support have significant relationship with KPIs towards a sustainable university. Lastly the findings have been confirmed with experts. In aspect of acceptance level of the findings in structural model, most of the experts agreed that the findings obtained are in-line with current scenarios in Malaysian universities
Detection and mapping of small-scale and slow-moving landslides from very high resolution optical satellite data
(Universiti Teknologi Malaysia, 2020) Eyo, Etim Efiong
Small slope failures are often ignored because of their perceived less severe impact. Although they may have small velocity, small slope failures can cause damages to facilities such roads and pipelines. The main objective of this research is to utilise very high resolution Pleiades-1 data to extract surface features and identify surface deformations susceptible to small slope failures. An algorithm was developed using object-based image analysis (OBIA), Pleiades-1 imagery, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) and Real Time Kinematic-Global Positioning System (RTK-GPS) data. Using the OBIA algorithm four different object attribute parameters namely spectral, textural, spatial and topographic characteristics were applied in a rule-based classification, for semi-automated detection of small translational landslides. The developed OBIA algorithm was further modified by using Pleiades-1 imagery, Nearest Neighbors (k-NN) and Support Vector Machine (SVM) techniques in example-based classification for the detection of small landslides, with focus on the effects of the training samples size and type on the results of the classification. The horizontal displacement of the landslides was investigated based on sub-pixel image correlation method using Pleiades-1 images and Shuttle Radar Topographic Mission (SRTM). Kalman filtering method and RTK-GPS observations from TUSAGA-Aktif Global Navigation Satellite System (GNSS) Network in Turkey were utilised to formulate kinematic analysis model for the landslides. The developed algorithms were validated in Kutlugün test site in Northeastern Turkey. In the rule-based classification results, a total of 123 small landslides covering a total area of approximately 413.332 m2 were detected. The size of landslides detected varied between 0.747 and 7.469 m2. The detected landslides yielded user’s accuracy of 81.8%, producer’s accuracy of 80.6%, quality percentage of 82% and computed kappa index of 0.87. In the small landslides detection using the example-based classification, the SVM method had higher producer accuracy (85.9%), user accuracy (89.4%) and kappa index (0.82) compared to the k-NN algorithm that had producer accuracy (83.1%), user accuracy (86.0%) and kappa index (0.80). A total of 128 small landslides were detected using k-NN algorithm, while a total of 134 landslides were detected using SVM algorithm. The displacement results from RTK-GPS measurements varied from 2.77 mm to 24.87 mm in 6 months, while the velocities varied from 0.80 mm to 8.28 mm/6 month. The displacements from optical image correlation agreed well with RTK-GPS results and provided a more uniform movement pattern than could be derived solely using the RTK-GPS measurements. The landslide movements are dominantly toward the north direction. These trends agree with the results of previous study in the area. The main contributions of this research include – development of a comprehensive metrics to quantify the attribute parameters of small landslides, derivation of susceptibility and inventory maps for small landslides, and the design of an early warning system for small slope failures on highway infrastructures. The results of this research will add to the increasing applications of Pleiades-1 image in landslide investigations.
Determinants of mobile phone waste recycling and end-of-life management in Johor
(Universiti Teknologi Malaysia, 2017) Shah, Gautam Lalit
Growing development in the telecommunications industry, along with frequent purchases, upgrading and increased ownership of mobile phones (MPs) have indirectly contributed to the global increase in e-waste generation, along with future pile-ups of used MP units and accessories. An improper end-of-life (EOL) management of MPs further exacerbated environmental degradation associated with their hazardous waste components. The increasing number of new MP purchases and service subscriptions, especially in Johor had made it relevant to study how the MP usage trend and its EOL management amongst consumers and sellers could affect future stockpiling and e-waste disposal. This study also analyzed the urban and non-urban respondents‘ willingness to pay (WTP) for a green MP or participate (WTPar) in a recycling program as well as their opinions on MP-related policy and recycling facilities. It involved a randomly selected sample of 1200 MP users and 110 sellers around urban Johor Bahru and nonurban areas (i.e., Muar and Kota Tinggi). Mean comparison or analysis of variance (ANOVA), bivariate analysis, and linear regression were used to determine associations between socio-economic background and purchasing activity as well as willingness-toparticipate in a recycling program between the groups. Results indicated that on average, urban consumers chose price in making purchases, owned more MP units and kept them as spares, thus implying the stockpiling problem. Based on Kendall's tau coefficient, willingness to participate in a recycling program and pay more for a green MP differed according to socio-economic and locational factors (i.e., p < 0.01 or significant at 99% confident level). Majority, especially the non-urban respondents, were highly supportive of incentives and rebates, along with improved accessibilities and increased number of recycling facilities in promoting a more sustainable EOL management of MPs. The study provides a new insight in integrating locational and socio-economic factors, as well as MP usage, pricing, purchasing behavior, and convenience with current and future MP‘s EOL management system and policy framework.
Developing a sustainable tax efficiency model to reduce property tax non-compliance
(Universiti Teknologi Malaysia, 2016) Ashmat, Ismail
Property tax is one of the most important factors in contributing to the sustainability and function of local authorities. The revenue collected is vital in providing sufficient funding to accommodate the demand for services and facilities of population in the city. However, the effectiveness of tax administration practices in Malaysia is questionable due to the impact of non-compliance of tax payment. Various opinions and arguments in the literature have pointed out that the root cause of this problem lies in the weaknesses of current tax administration. The weaknesses identified from the literature can be categorised as are the taxation procedures, preparation of Valuation List, and governance and legislation. Therefore, the main objective of this research is to develop a sustainable tax efficiency model in tax administration to reduce the property tax non-compliance within the local authorities in Malaysia. In order to explore basic understanding about standard practices of tax administration to reduce property tax non-compliance at the preliminary stage, the interviews have been conducted with the experts in property taxation. This followed by comprehensive data collection through distribution of structured close-ended questionnaires to the valuation officers in Malaysian local authorities that classified as an expert. Delphi Method used for data collection is to obtain the expert’s consensus on relevant questions asked in 3 Rounds. There are 47 experts that have responded the questionnaires in the Round 1 from 149 Malaysian local authorities that been circulated. The sample has been scaled-down into 14 experts in Round 2 and 3, due to the accuracy reason of the feedbacks in Round 1. The responds were analysed descriptively based on Cronbach’s Alpha to test the level of consistency and reliability of the indicators, and Factor Analysis to cut-off the indicators into the most preferred by the experts. This followed by benchmarking approach for the experts to determine where their responses ranked compared to other expert’s responses. The findings have exposed the efficiency indicators to reduce the tax non-compliance. Three major findings in this research are firstly, revenue collection from property tax maintain as the main source of income, secondly, the revenue collection has contributed to the strong financial tool to the local authorities and thirdly, with strong financial tool, local authorities will be sufficient and efficient in providing the services and facilities to the taxpayers. The sustainable tax efficiency model has produced the economic principle o f efficiency indicators than based on social or environmental. In general, Malaysian local authorities have to strategize the valuation procedure and preparation of Valuation List effectively in order to strengthen governance and legislation to reduce the tax non-compliance. Hence, the sustainable tax efficiency model developed in this research can be implemented to reduce property tax non-compliance at local government level
Development of air pollutants dispersion algorithm in urban industrial park for air pollution simulation
(Universiti Teknologi Malaysia, 2019) Selamat, Ubaidullah
Environmental Impact Assessment analysis is generally restricted to neighbourhood scale air pollution simulation using the Gaussian Plume model (GPM). This approach expected to enhance the resolution of ground level concentration in the conventional GPM based software up to building scale by using Computational Fluid Dynamics (CFD) model alongside the GPM. The aim of this study was to develop an air pollution prediction algorithm for air pollutants release from industrial stacks. It was used to estimate, simulate and control air pollution in urban industrial park using integrated GPM and CFD model. The GPM was used for regional air pollutant level prediction to find high pollutant concentration zones. Whereas, CFD model was used for detailed simulation on respective polluted areas. In order to achieve this, a building detection algorithm from satellite image based on building footprint detection and height estimation from shadow thickness has been proposed to reduce pre-processing effort of the present CFD solver. The present CFD algorithm were based on Fractional Step Method for efficient steady state solver and Prandtl Mixing Length turbulence model for low cost turbulence calculation. The accuracy of the CFD algorithm has been tested and verified against benchmark problems (less than 3% error for lid-driven cavity problem, less than 8% for flow over isolated cube). It was discovered that CFD algorithm developed in this study is sufficiently accurate as other wind flow models with slight over prediction in wind speed by 1.04 m/s (15.6% are below 10% error) and able to predict the wind direction correctly within 60° angle (37.5% are within 15° angle) compared to measurement data. Air pollutant release from major stacks in Pasir Gudang Industrial Park was studied using GPM and high NO2 concentration zone (1800 μg/m3) was found in Taman Air Biru. Results suggest that 24-hour averaged SO2 and PM10 maximum ground level concentration are well within Ambient Air Quality Standard (AAQS) limits with 8.9 μg/m3 (8.4%) and 11.4 μg/m3 (7.6%) respectively. Meanwhile, 24-hour averaged NO2 concentration exceed AAQS limit with 270.3 μg/m3 (360%). The detailed CFD simulation of wind distribution and pollutant dispersion process within the area was presented. Present CFD model (1800 μg/m3) over predicted 1-hour averaged NO2 ground concentrations by a factor of 3 compared to the present GPM (700 μg/m3) but it provides more information on wind distribution as well as pollutant dispersion process. A new atmospheric dispersion solver has been developed that is able to simulate pollutant dispersion on both regional scale using GPM and building scale using CFD model.

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