Energy

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Characterisation and optimisation of flame-retarding palm oil-based polyurethane/montmorilloniteammonium polyphosphate foam
(Universiti Teknologi Malaysia, 2021) Dzulkifli, Mohd. Haziq
The organic chemical composition in polyurethane (PU) foam has adversely affected the flammability of the foam that causes high susceptibility to fire. Many researchers and industrialists have invested vast efforts to counter this issue, including incorporation of synthetically-produced flame retardants and fillers, and chemical modification on the constituting materials of PU foam. All these methods are not environmentally-friendly. Montmorillonite (MMT) has been known to have flameretarding properties in numerous polymer composite systems, however, its effects in PU foam application has not been investigated. The aim of this project was to develop and optimise palm oil-based PU foam reinforced with ammonium polyphosphate (APP) and MMT in the form of blended (PU/MMT/APP) and hybrid (PU/APP-MMT) systems. Palm oil-based PU foam was chosen as a matrix due to its renewable resources and sustainability. Two types of multi filler systems were prepared: PU/MMT/APP blend and PU/APP-MMT hybrid. The APP-MMT hybrid filler was prepared through an ion-exchanged surface-treatment method. PU foams were then fabricated at different MMT/APP and APP-MMT loadings, and were characterized for their compressive properties, fire retardancies, thermal stabilities, and morphologies. Hybrid PU/APP-MMT system had improved the fire retardancy where the limiting oxygen index (LOI) had reached 24.19 at 10 wt. % filler loading as opposed to 23.85 showed by blended PU/MMT/APP filler system. This probably indicates the synergistic effect between APP and MMT in fire-retarding mechanism where a more stable alumino-phosphate species was formed during the combustion, which enhanced the thermal-insulating and fire protection properties. However, the compressive modulus of hybrid APP-MMT showed the highest value (2.857 MPa) at 6 wt. %, where further filler inclusion beyond this point had deteriorated the strengths of the foams. Hindered intermolecular hydrogen-bonding was thought as the main contributor for the reduction. Response Surface Methodology was then used to find the optimised filler formulation for both multi-filler systems with compressive modulus and LOI as the responses. The optimisation yielded filler combination of 0.9 wt. % Na-MMT and 4.9 wt. % APP was the best for blended system, whereby the optimised hybrid APPMMT value was computed as 9.2 wt. %. The characterization of both optimised filler formulations portrayed some improvements, when compared to its pristine counterpart. The LOI values were improved up to 44 %, with compromising only 5 % of compressive modulus. From the results obtained, bio-based PU foam has a great potential to be used in structural panel-related applications that requires moderate loadbearing and flame-retardancy capabilities.
Characterization of carbon nanotube growth region in flame using wire-based macro-imaging method
(Universiti Teknologi Malaysia, 2019) Hamzah, Norikhwan
Carbon nanotube (CNT) synthesis in flame has enormous potential as an energy-efficient and economical production method compared to the conventional catalytic chemical vapor deposition (CCVD) synthesis process. However, synthesis control remains a great challenge for flame synthesis due to the limited understanding on the effect of flame inlet condition toward CNT growth region in a heterogeneous flame environment and premature catalyst surface encapsulation by the amorphous carbon layer. The present study formulates a simple, yet accurate method called wirebased macro image analysis (WMA) for thorough growth region identification. The WMA method is employed to investigate the effects of reactant composition and aerodynamics on the spatial distribution of CNT growth region. Besides that, bend wire method is developed to provide cross-sectional analysis of the CNT growth region with focus on the amorphous carbon layer thickness (ACLT) at variable reactant concentration including fuel from 50% to 100% and oxygen from 19% to 27%, with addition of water vapor up to 0.14 mg/sec mass flow rate within the fuel stream. The CNT is synthesized on a 0.4 mm diameter pure nickel wire within the methane diffusion flame with a stainless-steel wire mesh placed on top and water vapor is introduced in a fuel stream using a bubbler mechanism. The CNT growth region is confined within the flame sheet, gradually shifts from flame front to flame centreline as height above the burner increases. The growth region is more sensitive towards the change in the oxygen concentration compared to that of the fuel concentration due to the significant change of flame height caused by the former. A segregation of growth region temperature with temperature difference of 100 ? that is observed between the upstream and downstream growth region is governed by the proximity with respect to the flame sheet. The ACLT reduces in lean flame due to the reduction in excess carbon concentration and the addition of water vapor remarkably reduces ACLT by 17% on average in any combination of inlet conditions due to the water-induced etching and oxidation of amorphous carbon on the catalyst surface. Development of the WMA and bend wire method leads to deeper fundamental understanding of CNT flame synthesis and further enhance possibility of highly efficient and economical CNT production process in the future.
Coal combustion prediction analysis tool for ultra supercritical thermal power plant
(Universiti Teknologi Malaysia, 2021) Mat Zaid, Mohammad Zahari Sukimi
Coal remains a major source of energy in the power generation industry in Malaysia. However, coal usage results in serious ecological and environmental problems due to greenhouse gas (GHG) emissions. One of the main objectives of the coal combustion research is to develop techniques that may help power plant operators (PPO) to utilize coal cleanly and efficiently by adopting good coal blending practices. Currently, the emission mitigation and boiler cleanliness measures through the coal blending process are focusing more on laboratory-scale tests and not utilizing the actual plant data and behavior. This study aims to evaluate the effectiveness of the developed Coal Combustion Prediction Analysis Tool (CPAT) as a method to facilitate the PPO in predicting the impact of the individual or blended coal quality. It provides early predictions on the boiler combustion performance related to the coal quality and assists the PPO in preparing for the boiler process control optimization. The CPAT combustion model is related to the calculations of the boiler performance and emissions while the CPAT boiler cleanliness model is to compute the slagging and fouling indices. The former model was tested and validated using the actual plant data with the results showing that all the models have mean percentage errors of less than 1%, implying that the combustion model is accurate. The latter model was verified with the actual boiler process parameters and actual site observation for the slagging behaviour. The results show that it gives accurate indications of the slagging and fouling tendencies and helps the PPO to strategize the coal combustion plan. The effect of the coal blending ratios to the power plant performance and SOx emission is evaluated and the result shows that the CPAT is able to recommend the optimum blending ratio for optimum plant performance and SOx emission. Thus, the proposed CPAT is able to provide accurate predictions for the SOx emission to ensure SOx emissions of below 500 mg/Nm3 and reduce the overall auxiliary power consumption by 12 MWh, thereby improving the overall power plant efficiency and establishing the optimal operational regime. The optimization of coal blending helps to improve the power plant efficiency as well as reduce the GHG emissions for a boiler in a coal fired power plant (CFPP) in Malaysia.
Control of the photovoltaic emulator using fuzzy logic based resistance feedback and binary search
(Universiti Teknologi Malaysia, 2018) Ayop, Razman
Photovoltaic (PV) emulator is a power supply that produces similar currentvoltage (I-V) characteristics as the PV module. This device simplifies the testing phase of PV systems under various conditions. The essential part of the PV emulator (PVE) is the control strategy. Its main function is to determine the operating point based on the load of the PVE. The direct referencing method (DRM) is the widely used control strategy due to its simplicity. However, the main drawback of DRM is that the output voltage and current oscillate due to the inconsistent operating point under fixed load. This thesis proposes an improved and robust control strategy named resistance feedback method (RFM) that yields consistent operating point under fixed load, irradiance and temperature. The RFM uses the measured voltage and current to determine the load of the PVE in order to identify the accurate operating point instantaneously. The conventional PV models include the I-V and voltage-current PV model. These PV models are widely used in various control strategies of PVE. Nonetheless, the RFM requires a modified PV model, the current-resistance (I-R) PV model, where the mathematical equation is not available. The implementation of the I-R PV model using the look-up table (LUT) is feasible, but it requires a lot of memory to store the data. A mathematical equation based I-R PV model computed using the binary search method is proposed to overcome the drawback of the LUT. The RFM consists of the I-R PV model and the closed-loop buck converter. In this work, the RFM is investigated with two different controllers, namely the proportional-integral (PI) and fuzzy logic controllers. The RFM using the PI controller (RFMPI) and the RFM using the fuzzy logic controller (RFMF) are tested with resistive load and maximum power point tracking (MPPT) boost converter. The perturb and observe algorithm is selected for the MPPT boost converter. In order to properly design the boost converter for the MPPT application, the sizing of the passive components is proposed, derived and confirmed through simulation. This derivation allows adjustment on the output voltage and current ripple of the PVE when connected to the MPPT boost converter. The simulation results of the proposed control strategies are benchmarked with the conventional DRM. To validate the simulation results, all controllers are implemented using dSPACE ds1104 rapid prototyping hardware platform. The RFM computes an operating point of the PVE at 20% faster than the DRM. The generated output PVE voltage and current using RFMPI and the RFMF are up to 90% more accurate compared to the DRM. The efficiency of the PVE is beyond 90% when tested under locus of maximum power point. In transient analysis, the settling time of RFMF is faster than the RFMPI. In short, the proposed RFMF is robust, accurate, quick respond and compatible with the MPPT boost converter.
Design and fabrication of arc thermal plasma reactor for petroleum sludge treatment
(Universiti Teknologi Malaysia, 2019) Mohammed, Abubakar Ali
Over 130, 000 metric tonnes of toxic petroleum sludge are generated yearly in Malaysia. The traditional methods of disposing of petroleum sludge are short of providing the much-needed benign treatment. A more robust treatment technique is therefore desirable. The thermal plasma treatment technique is employed to bridge the gap. In this research, a 4.7 kW thermal plasma reactor was designed and fabricated. The output current and the plasma temperature range were 5 – 200 A and 356 – 1694 oC respectively. After the treatment, the morphology of the sludge transformed from jelly-like to crystalline solid. A mass reduction of 36.87 – 91.40% and a total organic compound reduction of 21.47 – 93.76% were achieved in a treatment period of 2 – 5 minutes. The leaching test indicates that the heavy metals were stabilized in the solid, and hence, the solid is safe for secure landfill. The product gas is a mixture of carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), water (H2O), methane (CH4), acetylene (C2H2) and ethylene (C2H4). The concentrations of the greenhouse gases, CH4 and CO2, were small. The lower heating value and the cold gas efficiency of the gas were 7.40 – 7.86 MJ/Nm3 and 25.22 – 51.90% respectively. The efficiency is within the range of the efficiency of gasification of petroleum sludge in an updraft gasifier. Based on the operating cost estimation, a profit margin of RM 3.11/kg of sludge was achieved. Two quadratic models, one for cold gas efficiency and the other for CO/CO2 ratio were developed. The developed models, using response surface methodology, showed a good fit with correlation coefficients of 99.32% and 99.66% for cold gas efficiency and CO/CO2 ratio respectively. The optimum operating conditions for the treatment were arc current = 188.15 A, plasma gas flow-rate = 31.54 L/min and treatment time = 1.89 min. The optimum responses obtained from the optimization of the reaction system were 52.59% and 1.80 for cold gas efficiency and CO/CO2 ratio respectively with the desirability of 1. Thermal plasma technique is, therefore, an alternative method for treating petroleum sludge.
Design and operation of renewable energy based distributed energy generation system
(Universiti Teknologi Malaysia, 2014) Ho, Wai Shin
Concerns over sustainability of fossil fuels, escalating petroleum prices and increasing awareness for the environment have encouraged countries all over the world to shift from the heavy reliance on fossil fuel to renewable energy (RE) resources for electricity generation. The distributed energy generation (DEG) system that operates within the distribution network that fits the criteria of future needs of smart and efficient energy system could therefore be the best platform to implement RE. However, in order to achieve an optimal DEG system in terms of cost and efficiency, the designing and scheduling process could be rather difficult and complex. Among the factors taken into consideration during the planning stage include; i) intermittency and operation of RE especially for solar energy systems which are weather oriented; ii) manipulation of supply load through integration of energy storage (ES) system for peak shaving; and iii) manipulation of demand load through load shifting (LS) for peak shaving. Taking these factors into account, new optimisation methodologies are introduced in this thesis, specifically, a targeting technique based on Pinch Analysis known as the Electric System Cascade Analysis (ESCA). The technique was then expanded in to a mathematical model for a more holistic investigation. In Malaysia, developments of RE and DEG are currently focused on Iskandar Malaysia (IM) which is set with the goal to be developed as the low carbon city. Compared to many other countries which focus on wind energy as their main RE for low carbon development, IM which lacks of wind energy instead considers other RE resources such as biomass, biogas (both from palm oil resources) and solar energy, as they are the most promising RE resources to simultaneously reduce the dependency on fossil fuels as well as providence of environmental benefit in the region of IM. Palm oil mills (POM), being a biomass and biogas collection point, having sufficient land area for solar energy systems, and located distances away from a centralised grid system could therefore gain much benefits from a DEG system. Thus it was taken as the case study for the optimisation techniques developed in this thesis. The case study on the POM includes optimal DEG system design and operation (electricity and heat) of the mill as well as the local residential community. The application of the model on an eco-community and industrial case study has demonstrated the applicability of the model to design an optimal cost competitive DEG system. Through this study, it shows that implementation of DEG system within IM is indeed feasible
Efficient framework for integrating distributed generation and capacitor banks considering simultaneous grid-connected and islanded distribution network operations
(Universiti Teknologi Malaysia, 2021) Leghari, Zohaib Hussain
In literature, for the planning problem of simultaneous distributed generation (DG) and shunt capacitor banks (SCB) allocation in radial distribution networks (RDNs), researchers have focused mainly on the real power loss reduction and ignored the benefits of reactive power loss minimization, which might not distribute DGs and SCBs at the desirable locations. In addition, a variety of metaheuristic optimization techniques have been employed in literature whose implementation involves either the number of phases or tuning of certain algorithm-specific parameters. In contrast, the Jaya algorithm (JA) is a simple and efficient single-phase optimization algorithm that is free from any parameter tuning. However, the JA also suffers from inadequacies of population diversity and premature convergence; therefore, require a mechanism to overcome these deficiencies. Furthermore, past studies conducted for the islanded networks have followed the approach of isolated operation and did not consider the power supply-demand imbalance condition, which will result allocation of oversized DGs and SCBs. Considering these facts, this research work proposes a two-stage planning approach for the efficient utilization of DGs and SCBs for the simultaneous grid-connected and islanded operations of the RDNs. The first stage determines the optimal installation locations and capacities of DGs and SCBs, and operating power factor of DGs using an improved variant of the JA (IJaya) to minimize the total power loss and voltage deviation during the gridconnected operation. For the proposed IJaya, a dynamic weight parameter based grid-search mechanism has been introduced to mitigate the problem of premature convergence and population diversity in JA. The performance of the IJaya was evaluated using the IEEE 33-bus and 69-bus RDNs. A comparative analysis with existing optimization methods reveals that the IJaya achieves up to 38.84% more reduction in power losses and 3.26% more voltage improvement. In the later part of the study, a methodology concerning the efficient and maximum utilization of the installed DG-SCB capacity in the islanded RDN under power imbalance conditions has been proposed. For that, a multiobjective minimization function incorporating the total power loss and under-utilization of available DG-SCB capacity has been established. To minimize the proposed function, an iterative analytical approach has been proposed to tune the source power factor. The results showed that the underutilization of available DG-SCB capacity varies up to 15.83% for the power factors ranging from 0.8 to 0.93. Expectedly, the proposed study will assist the utility companies to efficiently operate their distribution systems and to design effective energy management schemes for the customers.
Energy consumption behaviour assessment model for student accommodations in Malaysian public universities
(Universiti Teknologi Malaysia, 2015) Ishak, Mohd. Hafizal
In achieving towards sustainable campus of higher education institutions (HEIs), energy consumption behaviour assessment is one of the several issues that require attention by the facilities manager. Information on energy consumption behaviour is needed to determine potential energy savings. However, issues on the information of energy consumption behaviour such as 'direct' and 'indirect' data, pattern segregation, factors influence and modeling subsequently has inhibited the energy consumption behaviour assessment agenda. The purpose of this study is to assess energy consumption behaviour for student accommodations in Malaysian public universities. This study has two main objectives, first, to determine energy consumption patterns and analyse the factors that influence the pattern. Second, is to develop energy consumption behavioural models (ECBM) and assess the potential energy savings. The 'energy culture' framework consolidated with 'centrographic' approach and econometric analysis used to strengthen the development of ECBM. A self-administrated survey carried out involving 1,400 respondents in selected public HEIs. There are three types of energy use among students in public HEIs namely, 'high', 'low', and 'conserve'. The 'device', 'activities' and 'building regulation' are the influence factors on the pattern of energy use. The energy consumption behaviour model (ECBM) was developed at the final stage of the study. Through the model's application, there is a potential energy savings of 52 to 66 percent among the students. It is capable of assessing the energy consumption behaviour and potential energy savings
Framework for multistage pre-treatment of anaerobic digestion for maximizing electrical energy production
(Universiti Teknologi Malaysia, 2018) Abdur Raheem
Anaerobic digestion (AD) is a complex process involving several dependent variables. Among critical factors are pH value, temperature and type of pre-treatment of raw material. The change in these parameters affects the overall performance of the system in terms of biogas and methane yield, resulting into varying power output. Different pre-treatments of biomass have different impact on the kinetics of AD. Therefore, the overall electrical output power varies with varying the type of pretreatment and to which extent it is used. In this regard, most of the existing approaches focused only on the multistage reactor design and economic evaluations with single pre-treatment technique. They did not consider the effect of multistage pre-treatment techniques on electrical power output. This research proposes a novel methodology of multistage pre-treatment of organic matters which has the potential to increase the power output from AD to its maximum. The modelling of most common pre-treatment techniques (chemical, mechanical and thermal pre-treatments) of organic matters is presented to calculate the effect of these treatments on the electrical energy production. A framework is developed to evaluate the whole process from pre-treatment to the power output. Multistage pre-treatment is proposed in this research to enhance the electrical energy production from AD. The first order kinetic model of AD is used to calculate the biogas and methane yields and electrical energy as existing literature illustrates that this model is a good choice acceptably for the solution of chemical reactions involved in AD. Three different pre-treatment scenarios, AD with single pretreatment (Case 1), AD with two stage pre-treatment (Case 2) and AD with three stage pre-treatment (Case 3) are considered for the application of the proposed methods. The proposed scenarios are simulated to use different possible number of combinations in all three pre-treatment cases. The highest production of electrical energy achieved was 0.62 kWh, 0.75 kWh and 0.87 kWh for 1 kg of animal wastes for Case 1, Case 2 and Case 3 respectively. The results are compared with the experimental results of pilot scale plant and Anaerobic Digestion Model No. 1 (ADM1). This shows that biogas, methane yield and electrical energy output can be enhanced to approximately two fold by using multistage pre-treatment. The proposed technique is useful for the prediction of bioenergy yield for different organic matters as well as for other bioenergy conversion routes.
Households energy consumption and carbon dioxide emissions of Mahabad City, Iran
(Universiti Teknologi Malaysia, 2020) Soltani, Mohammad
This study seeks to find a method to identify the dominant pattern of energy choice and consumption in households, centring on demographic factors affecting the use of home appliances. To this aim, this research dealt with a variety of energy sources that were widely used by households, namely LPG, electricity, and kerosene for cooking, heating and cooling, lighting, and home appliances. Additionally, significant associations for household energy choice and consumption were identified for demographic variables, including household size, gender, age of household head, educational level, and income group. A binary logistic regression was performed to obtain quantitative data provided by a survey from 821 households across residential districts of urban and rural areas in Mahabad Region, northwest of Iran. Collected data were analyzed within a proposed three-energy dimensions model (3-ED). The results showed that if the other variables remain constant, income may lead to variation in LPG and electricity consumption. Unlike other independent variables, the household-head age failed to have a significant impact. The findings can contribute to a better understanding of effective factors on household energy choice and consumption in other cities and be useful for the support of policymakers in their consumption patterns. This research explores the impact of different household demographic characteristics on energy-saving behaviours and carbon dioxide (CO2) emissions in Mahabad city located in the northwest of Iran. The structural model adopted was composed of six variables, including household age, household size, educational qualification, income quintile, gender, and energy conservation behaviour concerning demographic features, energy sources, and consumptions. To compare the predictability power of these variables' effects on households' energy conservation and CO2 emissions, a crisp instruction on how to evolve a statistical technique for analyzing data was provided by Partial Least Squares Structural Equation Modelling (PLS-SEM). It was revealed that households consume approximately 89.71% on liquefied petroleum gas (LPG), 9.87% on electricity, and the rest 0.43% on kerosene, petrol, and diesel on a monthly basis. Eventually, the results of this research showed that age, family size, and carbon dioxide emissions, except education background and income level, are significantly correlated with energy-saving behaviour.
Improved antlion sizing optimization for vehicle-to-grid considering rule-based energy management schemes
(Universiti Teknologi Malaysia, 2023) Alsharif, Abdulgader H. Abdulgader
Renewable Energy Sources (RESs) integration with Electric Vehicles (EVs) and microgrids has become a popular system for providing an economic and green environment. In order to address power challenges, RESs such as solar and wind are exploited and integrated into a microgrid. EVs play a key role in reducing emissions and energy saving due to their free carbon nature, reducing fuel consumption, and can be used as storage or load. Tripoli-Libya (latitude 32.8872° N and longitude 13.1913° E) located in Northern Africa is one of the oils and natural gas producers that has been selected as the study area. However, the country is bedeviled with electric power problems. Microgrids are faced with planning issues, challenges associated with designing a proper model system, as well as stability which results in low power quality. The issue can be addressed by using metaheuristic algorithms combined with Energy Management Strategy (EMS). However, the conventional metaheuristic algorithms face premature convergence and acquire local optima quickly which needs to be improved. Thus, choosing suitable sizing metaheuristic algorithms is recommended to find the global optimum. Therefore, Improved Antlion Optimization (IALO) coupled with the Rule-Based Energy Management Strategy (RB-EMS) is proposed. An RB-EMS is used to control and monitor the flow of energy in the system using simple mathematical equations. Furthermore, in the literature review, rule-based is recommended due to the decision-making and providing the appropriate result. This study examines a grid-connected system aimed at addressing the current power challenges by integrating RESs into Electric Vehicle Charging Facility (EVCF) using Vehicle-to-Grid (V2G) technology. An objective function for the proposed grid-connected system mainly depends on measuring the per unit of generated electricity as Cost of Energy (COE), and reduction in Losses Power Supply Probability (LPSP) as means of stabilizing the system and maximizing the Renewable Energy Fraction (REF). Mathematical modeling for the Photovoltaic (PV), Wind Turbine (WT), EV, inverter, and Battery (BT) as the microgrid components for the case study (Tripoli-Libya) is adopted. The acquired result has been validated with other algorithms Antlion Optimization (ALO), Particle Swarm Optimization (PSO), and Cuckoo Search Algorithm (CSA). The obtained simulation result indicates that the proposed method IALO contributed lower COE ($0.0936 /kWh), and high REF (99.40%) as compared to the counterpart algorithms. The IALO coupled with RB-EMS fills the gap in sizing and planning a cost-effective system to address the sizing limitations. The results affirm the low-cost nature of the proposed model of a grid-connected microgrid system using V2G technology. A further economic assessment is made using the Stochastic Monte Carlo Method (SMCM) used to estimate the load impact by integrating various numbers of EVs and the payback period. Sensitivity analysis was utilized to demonstrate the impact performance of the proposed components under various scenarios.
Improved control strategies for three-phase grid-connected photovoltaic systems under grid-fault conditions
(Universiti Teknologi Malaysia, 2020) Kamil, Haval Sardar
During grid fault conditions, a distributed generation should remain connected for a pre-determined amount of time, and also provide reactive power to support the grid voltage. This is called low-voltage ride through (LVRT). LVRT control method for wind power generation systems under unbalanced and harmonic conditions is a well-developed research topic. However, too little attention has been paid to the LVRT control method for three-phase grid-connected photovoltaic (PV) systems under grid fault conditions. This thesis proposes improved control methods for a three-phase three-leg and a three-phase four-leg PV power converter under grid fault conditions. For a three-phase three-leg PV system, an improved positive-negative-sequence control scheme and an instantaneous active-reactive power control strategy are suggested. These schemes are used to cancel the double grid frequency oscillations in the active power and reactive power of a three-phase grid-connected PV during unbalanced grid condition. These methods are also effective to reduce the oscillations of Direct Current (DC)-link voltage that can be detrimental for DC-link capacitor. In order to track the desired unbalanced or harmonic reference current, enhanced proportional resonant (PR) current controllers with harmonic compensator have been designed using Bode frequency analysis. This study also suggests enhanced control method for a three-phase four-leg grid-connected PV system under unbalanced fault conditions using the combination of proportional integral (PI) and enhanced PR controllers using symmetrical components. Enhanced synchronization method for a three-phase four-leg grid-connected PV power converter operating in a three-phase four-wire system under unbalanced grid fault conditions using the magnitude and the phase angle of the positive, negative and zero sequence components is also presented. The proposed control strategy for the three-phase three-wire PV has the ability to cancel the double grid frequency oscillations in the active power, reactive power and also up to 55.5% reduction in the amplitude of the voltage oscillations under unbalanced grid fault conditions. The enhanced scheme for three-phase four-leg PV power converter operating in a three-phase four-wire system under unbalanced grid fault conditions has also the ability to cancel the oscillation of both the active and the reactive powers simultaneously.
Integrated framework for synthesising energy-efficient distillation column sequence
(Universiti Teknologi Malaysia, 2020) Zubir, Muhammad Afiq
This thesis presents and describes the development and application of an integrated framework for the synthesis of energy-efficient distillation column sequences. The framework is generic and applicable to various types of distillation columns. It is unique in the sense that it integrates distillation column sequencing, selection and design with the graphical representation of the driving force method. The existing driving force method was improved to include the effect of feed composition and also several concepts from existing methods, which can improve the capability of the method in finding optimal solutions that are feasible, economical, energy-efficient and material-efficient. The framework consists of five stages: 1) energy analysis of the existing sequence, 2) determination of the driving force sequence, 3) design of the driving force sequence, 4) feasibility, energy intensity and material intensity analyses and 5) economic analysis. In Stage 1, an existing distillation column sequence was simulated using the Aspen HYSYS process simulator to obtain its energy usage. In Stage 2, the graph of the improved driving force method was used to determine an energy-efficient distillation column sequence, which was also simulated to obtain energy usage. Then, by using a similar graph, suitable unit operations (flash columns, ordinary distillation columns, or extractive distillation columns) for the sequence were selected and designed in Stage 3. This post-design driving force sequence was also simulated for the same purpose as in Stage 1. The analyses began in Stage 4, where the feasibility, energy intensity and material intensity of the distillation column sequences obtained in Stages 1, 2 and 3 were compared. Feasibility was determined based on the reflux ratio range, distillation column height and product purity whilst energy and material intensities were based on mass, water and energy indexes. Finally, in Stage 5, an economic comparison based on capital, operation and total annual costs was employed. The framework was successfully tested on five different case studies with different objectives to test and verify the methodologies used in the framework. The application of the overall framework showed that energy savings of up to 32.94% could be achieved whilst operating within the feasible range. The energy and material intensities were also reduced by up to 59.31%, indicating lesser amount of energy and material used for the framework’s sequence. The capital and operation costs were also reduced, as much as 35.05% and 30.88%, respectively, which led to 31.71% lower total annual cost, compared with the sequences obtained by previous studies.
Integrated spatio-temporal techno-economic approach for modeling multi-sectoral bioenergy deployment
(Universiti Teknologi Malaysia, 2021) Mohd Idris, Muhammad Nurariffudin
Although aspects of long-term planning are commonly taken into account in current analyses of bioenergy policy scenarios, spatial representations of the bioenergy supply chain are often overlooked. Multiple questions such as where, when, and how bioenergy is deployed thus have not been sufficiently addressed within a single modeling framework. Moreover, techno-economic models that can capture the dependencies of bioenergy supply chain variables among end-use sectors still need to be explored. This thesis presents a spatially and temporally explicit techno-economic supply chain optimization model that allows the assessment of bioenergy deployment at a higher system level from a multi-sectoral perspective. This thesis also presents applications of the model in the context of developing low-carbon pathways for a developing country having an economy reliant on fossil fuels and agriculture, with Malaysia serving as a case study. The model was developed in the generic algebraic modeling system, with ArcGIS applied for spatial processing and Python applied for database management. The first part of the thesis presents the model application for assessing long-term cross-cutting impact of implementing bioenergy in multiple energy sectors up to 2050. The findings suggest that integrating substantial capacity of bioenergy in Malaysia's energy sectors could help save up to 37% of the annual emission avoidance cost of meeting the long-term emission target. The findings also suggest that the renewable energy policies could deliver more emission reductions than the decarbonization policies, but would require 30% more cumulative investment. The second part of the thesis discusses more detailed strategies on how biomass co-firing with coal can contribute to meeting short-term emission target up to 2030, which is related to multi-scale production of solid biofuels from palm oil biomass to scale up co-firing. The findings show that densified biomass feedstock could substitute significant shares of coal capacities to deliver up to 29 Mt/year of greenhouse gas reduction. Nevertheless, this would cause a surge in the electricity system cost by up to 2 billion USD/year due to the substitution of up to 40% of the coal-fired plant capacities. The third part of the thesis presents the model application to analyze the impact of the co-deployment of co-firing and dedicated biomass technologies in contributing to the bioenergy cost reduction under the impact of incremental decarbonization targets and supply chain cost parameter variations. The findings suggest that the multi-sectoral deployment of bioenergy in energy systems is key to meeting decarbonization targets at the national scale. By also considering biomass co-firing with coal in the biomass technological pathway, up to 27% of bioenergy cost reduction could be enabled in the main case. All the findings from this thesis are expected to inform the ongoing policies and initiatives regarding greenhouse gas reduction, renewable energy production, and resource efficiency improvement for managing environmental sustainability.
Kinetic studies and mathematical modelling of imperata cylindrica flash pyrolysis
(Universiti Teknologi Malaysia, 2017) Oladokun, Olagoke Abimbola
Biomass pyrolysis product offers great potentials in facilitating energy and environmental challenges. This is, however, yet to be realized due to some technological barriers that limit its economic potential. In this thesis, a flash pyrolysis of Imperata cylindrica in a transported bed reactor is investigated, aiming at improving its overall performances from both operation and design perspectives using a mathematical modelling approach. A macroscopic model of the process was used in estimating the kinetic parameters of I. cylindrica and in determining the optimal operating conditions of the reactor. A microscopic model using Computational Fluid Dynamics (CFD) was applied to study the reactor’s hydrodynamics and to determine optimal values for key design parameters, i.e., solid inlet positions, gas inlet position and height-width ratio. To facilitate more detailed analyses, a new algorithm was developed for determining cellulose, hemicellulose and lignin compositions from biomass devolatilization kinetic study. The results obtained confirmed that I. cylindrica has good fuel properties and decomposes easily in the presence of heat, thus making it a suitable feedstock for biofuel production in thermochemical processes. However, the laboratory scaled transported bed reactor was found inefficient and requires very high operating temperature in maximizing biooil yield. Based on the CFD study, the efficiency can be improved if the biomass and hot-sand inlets were positioned closer to the reactor wall and at opposite end. The results also indicated that a good hydrogen gas yield could be obtained from steam reforming of I. cylindrica biooil. In conclusion, the mathematical modelling approach carried out in this study has highlighted the potential of the proposed process and the use of I. cylindrica as a good biomass source for energy
Mathematical modelling optimisation of centralised sewage treatment plant for electricity generation
(Universiti Teknologi Malaysia, 2022) Tarmizi, Muhammad Saufi
Electricity has become one of the most basic requirements of human life. Fossil fuel is the world's leading source of gross electricity production. Alternative fuels have been offered the chance to lessen reliance on fossil fuels while also lowering greenhouse gas emissions. Malaysia's government recently announced a plan to seek green energy alternatives and technology for sustainable development. One of the most promising alternative fuels is biogas, which is produced through anaerobic digestion of sewage sludge in treatment plants. The existing decentralised small-scale sewage treatment plant is currently facing a problem due to a lack of input substrate for a biogas facility of sufficient scale. Furthermore, it necessitates a large plot of land and is difficult to manage due to its scattered placement. As a result, a centralised sewage treatment plant (CSTP) is a viable solution to this issue. The main objective of this research is to develop a model for optimal CSTP planning in generating electricity. The general methods can be divided into 5 stages; data gathering, problem formulation and superstructure construction, model development, general algebraic modeling system software coding and result analysis. The first phase includes creating a single-period model with the primary purpose of lowering capital costs. The second phase is the development of a multi-objective model that maximises economic performance while reducing environmental effect. The third phase is to develop a multi-period model for planning till 2035. Biogas from anaerobic digestion is estimated to be capable of producing 2,140 GWh of power in Peninsular Malaysia in 2018. In terms of economics, electricity generation, and sewage management, the model produced significant improvements over current practise. According to the first model, the total cost of building CSTP is RM175,014,755, which will serve around 400,000 population equivalent and produce 5,767 MWh of electricity per year. The second model calculated the total cost as RM250,880,000 with a multi-objective factor, λ, of 0.56 and a capacity of 7,699 MWh electricity per year. The final model concluded that co-digestion is the best solution for increasing biogas production. Two CSTPs were proposed to meet electricity demand and available sewage at a total cost of RM1,217,416,734 and to generate 56,943 MWh of electricity per year. This research can be used as a preliminary study for CSTP by policymakers and government bodies, allowing them to draw conclusions about the technical and environmental aspects of the transition from existing decentralised to centralised systems. This model can be further developed into usable software that assists relevant authorities in making decisions.
Microwave enhanced transesterification of biodiesel from non-edible feedstocks using waste mollusc shells as heterogeneous catalyst
(Universiti Teknologi Malaysia, 2022) Mohd. Zamberi , Mahanum
Transesterification method is widely used to produce biodiesel at low volume production, which employs a homogenous catalyst and is found effective for processing virgin or highly refined vegetable oils. Hot plate and water bath are the common heat source methods being used to assist in expediting the reaction process. These methods are not only consumed time but they also wasted a lot of water for washing which makes the whole process less environmental friendly and costly. Microwave technology has been reported able to provide rapid heating but the report on its application in the transesterification process is somehow very limited especially involving the catalyst from mollusc shells. Until today, the waste mollusc shells application as the primary heterogeneous catalyst implemented in bulk size in converting high free fatty acid (FFA) feedstock into green and potential biodiesel with the microwave irradiation method is rarely reported. This research aimed to optimise biodiesel production and to identify significant parameters affecting the non-edible biodiesel yield via microwave-assisted transesterification. Waste mollusk shells oxides derived from Corbicula fluminea, Anadara granosa, and Perna viridis as heterogeneous catalysts were utilised to assist the microwave irradiation transesterification process. High FFA of rubber seed oil (RSO), and Jatropha Curcas oil (JCO) were employed as raw feedstock. The mollusc shells were sieved to different particles sizes ranging from 1 mm to 2 mm and were calcined at 900oC for 4 hours at a 10oC/min heating rate. Microwave power and reaction time were varied from 350 W to 450 W and 5 to 9 min respectively. The catalyst characterizations were carried out using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscope (SEM) and Brunauer- Emmett Teller (BET). A two-step transesterification process was utilised to perform the production. The acid esterification process used sulfuric acid and methanol to reduce the FFA percentage for RSO and JCO. The calcined catalysts mediated the transesterification reaction with feedstock and methanol via domestic microwave heating. Optimisation process was conducted using the Taguchi method of L27(35) orthogonal arrays. According to the analysis of the signal-to-noise ratio and ANOVA, the effect of catalyst loading was the most significant parameter with 45.1% and 47.5% contribution on the biodiesel yield from JCO and RSO, followed by the reaction time and molar ratio of methanol to oil. RSO biodiesel recorded the highest yield conversion of 96.6%, followed by JCO around 95.9% under the optimum parameter of 400 W microwave power and 7 minutes reaction time. It can be concluded that the calcium oxide catalyst derived from waste mollusc shells has a high potential to be used as biodiesel production catalysts in the transesterification of low quality feedstock that fulfill the ASTM D 6751 standard requirement.
Mitigating the effect of load shedding in electrical grid using hybrid renewable energy system approach
(Universiti Teknologi Malaysia, 2022) Bakht, Muhamad Paend
Load shedding is an operating condition whereby the electrical grid is temporarily disconnected or suspended from the load. The idea is to minimize the deficit between generation capacity and load demand, while ensuring a fair level of supply availability for all consumers. Load shedding is a prominent problem for many developing countries and thus, this thesis investigates the prospects of hybrid renewable energy system (HRES) to mitigate its effect at the distribution level. The proposed HRES in this work is configured using the photovoltaic (PV) array, wind turbine (WT), energy storage unit (ESU) and diesel generator (Gen). Despite the substantial amount of literatures on HRES, limited work is directly related to load shedding mitigation in grid-connected system. Furthermore, it is unclear what would be the cost of installing HRES and under what operating conditions the system would perform optimally. Thus, the main design objective of the proposed system is to ensure supply availability with minimum levelized cost of electricity (LCOE) and payback period (PBP). A small residential locality in Quetta, Pakistan is selected as a case study to test the system. The proposed HRES is equipped with the energy management scheme (EMS), which is designed in MATLAB/Stateflow. The sizes of HRES components (i.e., PV, WT and ESU) are optimized by the grasshopper optimization algorithm (GOA) and the results are verified with particle swarm optimization algorithm (PSO). The objective function of the optimization is characterized by three variables: LCOE, PBP and the loss of power supply probability (LPSP). Scenario-based simulations are performed in MATLAB to validate the functionality of the EMS and the behaviour of optimized HRES for various load shedding and meteorological conditions. In addition, it is compared with the conventional solutions for load shedding, namely the diesel generator (only), uninterruptable power supply (UPS), and the combination of both. The results based on one-year climatic data shows that the LCOE for the HRES is 6.64 cents/kWh, with PBP of 7.4 years. The LCOE of HRES is 77.6% cheaper than the LCOE for generator (only), 49.8% for the UPS, and 66.7% for the combined solution. Accordingly, the PBP is also shorter compared to diesel generator (12.9 years), UPS (9.8 years) and the combined system (11.3 years). Furthermore, the integration of HRES alleviates the annual grid burden by 32.9, 47.2 and 42.3%, respectively. These results confirm the superiority of the HRES over the conventional solutions. Finally, sensitivity analysis is performed to observe the changes in the LCOE and PBP with respect to the variation in the components prices, feed-in-tariff rate, metrological conditions and load demand. It can be concluded that a well-designed and optimized HRES has the potential to effectively mitigate the problem of load shedding with reasonable cost.
Monte-carlo based robust analytical method for optimal sizing and reliability of hybrid renewable energy system
(Universiti Teknologi Malaysia, 2020) Mudasiru, Mustapha
The need for a more reliable power from the utility grid and ever-increasing concerns on Greenhouse Gas (GHG) emission effect has globally promoting Renewable Energy Sources (RES). RES is increasingly being adopted in complementing traditional fossil fuels in the energy power supplies. Hybrid Renewable Energy (HRE) systems incorporating wind and solar sources offers lower costs, higher reliability, reduced investment risks, fuel diversification etc. However, wind speed and solar radiation are characterized by their limitations of inherent intermittency and variability. These limitations have led to the concept of optimal sizing and reliability assessments to maintain a balance between generated power and the system loads. Nonetheless, RES reliability assessment studies are site-specific, but existing studies are inexhaustive given the capacity availability and reliability requirements of various sites as well as their performance evaluations. This thesis presents the optimal sizing and reliability assessment of a hybrid solar and wind energy systems for a selected location. Weibull statistical method and air temperature amplitude based statistical models are adopted for wind and solar energy potential assessments of the selected site. The Weibull parameters were estimated using standard deviation method for wind energy potential assessment. Moreover, the air temperature based models of Hargreaves and Samani; Allen; Samani; and Bristow-Campbell models were used for solar energy potential assessment. Simulation of the uncertainty in the wind speed and its probability distribution is performed by using Auto-Regressive Moving Average (ARMA) model to improve wind speed normal distribution. In this approach, the best normal distribution for the simulated wind speed for the reliability analysis is chosen. To improve the performance of the Photovoltaic (PV) module, a single diode six parameter model is developed. First, the P-V and I-V curves were used to generate the required constraints. These constraints were then used to obtain the solution vector of the six parameters using MATLAB and System Advisor Model (SAM). Also, the system’s capacity availability and reliability was assessed using Monte Carlo (MC) simulation. Finally, the result of the MC reliability assessment is later served as Loss of Power Supply Probability (LPSP) constraints to Artificial Bee Colony (ABC) algorithm for the system’s optimal sizing and enhanced reliability assessment. Results from the study show that both wind and solar energy potential of the selected site is high and can generate power at utility level. The ARMA simulated wind speed shows an improvement of 21.8% in standard deviation over the measured wind speed. The adoption of the negative components in the ARMA model transformation resulted in least error of 23.34% in the final wind simulation. Results obtained based on the six parameter solution vector gives improved performance of the PV module. Using the developed MC technique, capacity availability of 100% and LPSP of zero is achieved. The developed ABC algorithm resulted in system reliability improvement of 98.92% when the MC results are constraint into the ABC for the optimal sizing. Various results were validated at appropriate sections and finally, the optimal sizing results of PV/battery RES power system is found to give the best reliability. Such a system has great reliability and can be implemented in facilities requiring constant power supplies such as critical infrastructure.
Multi-omics and taxonomic analyses of empty fruit bunch adapted mangrove microbial communities with lignocellulolytic abilities
(Universiti Teknologi Malaysia, 2020) Lam, Ming Quan
Current demand for energy drives the rapid progress of second-generation biofuel development. Use of lignocellulosic biomass, such as oil palm empty fruit bunch (EFB) in second-generation biofuels production resolved the limitation of firstgeneration biofuels which compete with food source. Lignocellulosic pre-treatment and saccharification are two crucial steps in second-generation biofuel production. These steps require synergistic action of lignocellulolytic enzymes. The use of large volume of freshwater in biofuel industry is a major concern as it creates competition between biofuel industry and human consumption. Seawater, which cover 96.5% of the biosphere could be an alternative to freshwater in biological pre-treatment and saccharification of lignocellulosic biomass. Therefore, the discovery of novel salttolerant microorganisms and their halophilic enzymes is an important aspect of lignocellulosic waste deconstruction using seawater. In this study, halophilic microbial community was collected from mangrove soil at Tanjung Piai National Park, Johor. Their ability to degrade lignocellulose was explored using culture independent and culture dependent approaches. The mangrove soil was used as inoculum and incubated with EFB in artificial seawater medium for 10 weeks. Total DNA, RNA and proteins were extracted (culture independent). 16S rRNA and 18S rRNA gene fragments were amplified from total DNA and composition of microbial community was analyzed based on amplicon metagenome sequencing. Taxonomic analysis showed that phyla Proteobacteria and Bacteroidetes were predominant prokaryotic population. Metatranscriptomic analysis revealed a total of 9,953 open reading frames (ORFs) related to lignocellulose degradation: 3,867 glycosyl hydrolases (GHs), 2,485 carbohydrate binding modules (CBMs), 2,156 carbohydrate esterases (CEs), 947 auxiliary activities (AAs) and 498 polysaccharide lyases (PLs). The highly expressed enzyme families were GH74, CE1, GH5, AA2, GH43, CE3, GH3, CE15, GH10 and GH6. Metaproteomic analysis identified a total of 87 lignocellulolytic enzymes in bound fraction of EFB and culture supernatant. Synergistic action of different lignocellulolytic enzymes from diverse microbial origin was observed with mostly affiliated to phyla Proteobacteria and Bacteroidetes. In addition, bacteria from the mangrove microbial community were isolated and their lignocellulolytic abilities were assessed (culture dependent). Two halophilic bacteria from the phylum Bacteroidetes, namely Meridianimaribacter sp. CL38 and Robertkochia sp. CL23 were selected for genomic analyses. A total of 30 and 89 lignocellulolytic enzymes were encoded in the genomes of strain CL38 and CL23, respectively. Furthermore, both strains demonstrated their abilities to degrade EFB. Genomic analyses of these two strains are the first genomic information from their respective genera. Due to the low similarity of 16S rRNA gene with closely related member, strain CL23 was further taxonomically characterized via polyphasic approach. Based on phenotypic, chemotaxonomic and genomic evidences, the strain CL23 is proposed as a new species with the name Robertkochia solimangrovi sp. nov. Multi-omics and taxonomic analyses in this study identified new halophilic microorganisms from mangrove with a wide array of new lignocellulolytic enzymes that are able to degrade EFB. These enzymes could be further investigated for development of enzyme cocktails which will be useful for seawater based lignocellulosic biorefining.

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