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Browsing Energy by Subject "Biomass energy—Research"
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- ItemFramework for multistage pre-treatment of anaerobic digestion for maximizing electrical energy production(Universiti Teknologi Malaysia, 2018) Abdur RaheemAnaerobic 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.
- ItemIntegrated spatio-temporal techno-economic approach for modeling multi-sectoral bioenergy deployment(Universiti Teknologi Malaysia, 2021) Mohd Idris, Muhammad NurariffudinAlthough 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.
- ItemSuperstructure optimization and forecasting of decentralized energy generation based on palm oil biomass(Universiti Teknologi Malaysia, 2013) Bazmi, Aqeel AhmedMalaysia realizes the importance of addressing the concern of energy security to accomplish the nation’s policy objectives by mitigating the issues of security, energy efficiency and environmental impacts. To meet the rising demand for energy and incorporation of Green Technology in the national policy, Malaysian government during the last three decades has developed several strategies and policies. National Green Technology Policy was an initiative, which marked the firm determination of the government to incorporate Green Technology in the nation’s economy policy. Malaysia has abundant biomass resources, especially oil palm residues with power generation potential of about 2400 MW, which is promising for decentralized electricity generation (DEG). The aim of this study is to determine the best location to install appropriate biomass electricity generation plant in Johor and forecasting the electricity market (i.e. electricity demand) in order to provide a strategic assessment of measures for the local energy planners of Malaysia, as an optimization bottom-up model. A superstructure was developed and optimized to represent DEG system. The problem was formulated as Mixed Integer Nonlinear Programming (MINLP) and implemented in General Algebraic Modeling System (GAMS). Electricity demand was modeled using Adaptive Neuro Fuzzy Inference System (ANFIS). Based on GAMS and ANFIS models, palm oil biomass based DEG system and distribution network scenarios for current as well as next ten, twenty and thirty years have been proposed for State of Johor, Malaysia. Biomass from sixty six Palm Oil Mills (POMs) would be collected and transported to eight selected locations. Empirical findings of this study suggested that total production cost is minimized by placing biomass gasification based integrated combine cycle (BIGCC) power plant of 50MW at all eight locations. For 2020 Scenario, no additional infrastructure will be required. For 2030 Scenario, additional units of BIGCC of 50MW will be required at five out of eight locations. While for 2040 Scenario, again no additional infrastructure development will be needed. Total minimum cost varied from 6.31 M$/yr for current scenario to 22.63 M$/yr for 2040 scenario.