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Improved antlion sizing optimization for vehicle-to-grid considering rule-based energy management schemes
No full textRenewable 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
Effects of palm oil mill effluent anaerobic sludge pretreatment temperature on biohydrogen production and the inoculum microbial community
Full text linkBiohydrogen production yield from dark fermentation could be improved by inhibiting interspecies hydrogen transfer to methanogens, homoacetogens and suppress non-biohydrogen producers as metabolic competitors. Heat pre-treatment has been extensively used for this purpose. In this study, the effects of heat pre-treatment temperatures on the performance of mesophilic biohydrogen dark fermentation system and the inoculum microbial community were evaluated, using POME anaerobic sludge as inoculum. Heat pre-treatment of the anaerobic sludges were conducted at 50°C, 65°C, 80°C and 100°C for 30 minutes. Biohydrogen production was evaluated under batch fermentation (30°C, initial pH 5.5-6.0, 180 rpm). Shotgun metagenomics analysis was carried out on the raw, untreated and treated sludge inoculum after fermentation. The results showed that pre-treatment of the inoculum at 65°C produced the highest biohydrogen yield of 0.67 mol H2/mol hexose followed by pre-treatment at 80°C which produced 0.62 mol H2/mol hexose. Untreated inoculum yielded 0.30 mol H2/mol hexose while 50°C and 100°C pre-treated inoculum produced less than untreated anaerobic sludge. Methane was not detected in any of the fermentation reactions. Shotgun metagenomics revealed that inoculum heat pre-treatment temperatures influenced the microbial communities in biohydrogen production fermentation. Inoculum pre-treated at 65°C and 80°C were enriched with the most biohydrogen-producing taxa, mainly spore-forming Clostridia which generate biohydrogen via butyrate-type fermentation pathways, producing butyric and acetic acids. Untreated anaerobic sludge fermentation was not significantly enriched with biohydrogen-producing microbial taxa. Heat pre-treated inoculum at a lower temperature of 50°C was enriched with non-spore-forming biohydrogen producers from Klebsiella, Escherichia and Citrobacter genera. These genera produced low biohydrogen yield by shifting the fermentation pathway to mixed-acid fermentation, producing a mixture of acetic and butyric acids and ethanol. The presence of non-biohydrogen producers, such as lactic acid bacteria also decreased biohydrogen production of the 50°C pre-treated inoculum, due to the lactic acid and ethanol fermentations. Heat pre-treatment at higher temperature of 100°C selectively enriched spore-forming microbial taxa. Among the species are biohydrogen producers B. coagulans, homoacetogens C. magnum, and non-biohydrogen producers from Bacillus species. These species re-direct the fermentation pathway to mixed-acid fermentation and produced high concentration of acetic acids. Methanogens, e.g. M. soehngenii present in the raw anaerobic sludge were suppressed in all the fermentation reactions. Metabolic functions analyses from the metagenome data showed that biohydrogen production potentially upregulate functions related to cellular processes (prokaryotic cell spore formation), carbohydrate metabolisms (sugar alcohols, monosaccharides, sugar acids and carboxylic acids metabolisms) and energy (fermentation and methanogenesis). In conclusion, inoculum heat pre-treatment is essential to enhance biohydrogen production using POME anaerobic sludge as heat pre-treatment enriched the inoculum with biohydrogen producers and suppress activities of methanogens, non-biohydrogen producers and homoacetogens
Synthesis and characterization of lead-free perovskite thin film deposited using spin coating technique
Full text linkPerovskite solar cell (PSC) technologies are viewed as promising innovation and have sparked a lot of interests due to their efficiency, ease of manufacture, and low energy and environmental impacts. The performance of PSC has improved quickly in recent years, with the current record of 25.6 % power conversion efficiency (PCE). The purpose of this thesis is to fabricate methylammonium tin triiodide, CH3NH3SnI3 perovskite thin film using spin coating technique. By manipulating spin rate and the precursor concentration, the structural and optical properties of microscope perovskite thin film were investigated and characterised using scanning electron -SEM, atomic force microscope-AFM, X-ray diffraction-XRD, and Ultraviolet-Visible spectroscopy respectively. The XRD result showed that CH3NH3SnI3 perovskite material exhibits intense peaks corresponding to the plane (100) and (200) at the angles of 14º and 28º. The results from AFM revealed that perovskite film fabricated at 2000 rpm with 1.0 M of precursor concentration showed a uniform and consistent surface structure with a root mean square roughness (Rrms) of 20.70 nm. From SEM analysis, perovskite thin film fabricated at 2000 rpm with precursor concentration of 1.0 M showed a significant homogeneous and uniform layer. From UV-Vis data, the highest absorption spectra was demonstrated by the perovskite film with 1.0 M concentration and fabricated at 2000 rpm spin rate. By controlling the spin rate and precursor concentration during the fabrication process, it was shown that 2000 rpm and 1.0 M are the best parameters for good perovskite thin film quality for solar cell application
Chiller energy savings by waste cold recovery from liquid nitrogen bulk
Full text linkWaste cold energy generated from the liquid nitrogen vaporization is usually abandoned when the gas supply system is pre-designed without cold integration. The purpose of this study is to investigate the potential for energy savings in a chiller system through the integration of waste cold recovery with thermal energy storage. The waste cold recovery system captures the waste cold generated during the operation of the liquid bulk nitrogen system and transfers it to a thermal energy storage system. This stored energy can then be utilized to supplement the chiller system during the targeted periods of peak demand, reducing its load and increasing its efficiency. The minimum of thermal energy storage capacity is determined by targeting the maximum peak shaving load through Cold Energy Storage Cascade Analysis (CESCA). The study analyses the feasibility of the waste cold recovery method in terms of energy savings, payback period and cost-effectiveness with peak load shaving of air-cooled and water-cooled chiller. Case study of 500 kg/h of liquid nitrogen revealed the significant energy savings at 124 MWh annually and avoided of 83 tCO2/year with minimum thermal energy storage capacity at seven tones of refrigeration. The simple payback period on air-cooled chiller is slightly better than water-cooled chiller which ranged from four to five years. The study concludes that the integration of waste cold recovery with thermal energy storage is a promising solution for liquid bulk nitrogen users looking for energy savings efforts and reduce their carbon footprint
Simulation of performance and optimization for diesel engine fueled with higher biodiesel blend
Full text linkWith global energy demand that keeps on increasing by 1.2% every year, CO2 was predicted to increase as well. A short-term solution to reduce CO2 is to switch to carbon-neutral fuels and one of them is biodiesel. However, biodiesel’s higher viscosity and lower calorific value compared to pure petroleum diesel lead to higher brake specific fuel consumption (BSFC) especially for higher biodiesel blend. Even though there are many ways to reduce the BSFC, to quantify how much these ways manage to reduce it requires experiments that are costly and time consuming. At the same time, a limited simulation model can be used as the alternative to the experiments. Therefore, the objective of this research is to develop a model based on the Yanmar L70N6 engine using GT-Suite simulation software, to predict engine performance, combustion, and emissions when using biodiesel. The engine model was then used to simulate a high biodiesel blend with a variation of injection timing (IT), injection pressure (IP), and preheat biodiesel fuel (PF). In this research, experimental work was conducted to obtain baseline data for validating the simulation study based on the manufacturer’s default setting of IP (206 bar), IT (14 ºbTDC), and ambient temperature for fuel which is around 30°C. In the experiment, B10 and B30 were tested at four different speeds (1500, 2000, 2500, and 3000 rpm) with five different loads (3, 5, 7.5, 10, and 11.5 Nm) at each speed. Then, B30, B50, B70, and B100 were simulated with variations of IP (206, 220, 240, 260, 280, and 300 bars), IT (10, 12, 14, 16, 18, 20, 22, and 24 ºbTDC) and PF (30, 40, 50, 60, 70, 80, and 100°C). For model validation, the engine speed was simulated at 2000 rpm with five different loads and comparison between the simulation and the experimental results showed less than 10% differences in the BSFC of B10 (8.8%) and B30 (5.1%). The results showed that by increasing IP to 300 bar, retarding IT to 12ºbTDC, and PF to 100ºC, reduction of the BSFC was recorded from 2.1% to 5.4% meanwhile CO2 emission reduction was recorded from 3.79% to 10.7% and by combining three optimized parameters, it helps reducing BSFC, and CO2 for all blends. Among all biofuels, B100 has the lowest BSFC (8.8%) and CO2 (22.3%) at 3000 rpm and 3 Nm load. In conclusion, the objective of the research, which is to develop a reliable simulation model and improve the performance of a high biodiesel blend, has been achieved successfully
Effect of nanoparticles in cuttings transport perrformance of water based muds at eccentric drill pipe
Full text linkOne of the major issues for drilling operations is achieving effective cuttings transport, particularly in extended-reach drillings (ERD) with horizontal and highly deviated sections. The main objective of the present study is to investigate and compare the application of different nanoparticles (NPs), such as nanosilica (SiO2), aluminium oxide (Al2O3), magnesium oxide (MgO), and copper oxide (CuO) for improvement in cuttings transport in a full wellbore section at both eccentric and concentric drill pipes. Water-based mud (WBM) was mixed with 0.13 and 0.26 wt.% of each of the NPs to create NP drilling fluids, which were then tested for rheological and filtration characteristics. The flow loop is 20 feet long, 2.4 inches wide (2.4-in. ID), and 1.4 inches thick (1.4-in OD). By circulating the tested fluid samples into the test section vertically to horizontally while controlling the flow rates (1.9, 2.15, 2.4 L/s), cuttings sizes (1.10–1.4 mm; 1.5–1.7 mm; 1.8–2.0 mm), hole angles (0, 30, 60, and 90o), and drill pipe eccentricity (e = 0; e = 1.0), the cuttings transport experiments were carried out. Simulating actual field circumstances is the aim of such a change in the operating parameters. The parameter used to assess hole cleaning is known as the "cuttings transport ratio (CTR)," which is calculated as the weight of recovered cuttings divided by the weight of injected cuttings. According to the findings, conventional WBMs' rheological properties are successfully improved by NPs, which improves borehole cleaning and drilled cutting suspension. With a higher NP concentration, the WBM's filtration capabilities were enhanced. The ideal concentration of NPs for rheological characteristics is 0.13 wt.%, whereas the ideal concentration for filtration control properties is 0.26 wt.%. In contrast, MgO yielded the lowest CTR, followed by SiO2, Al2O3, and drilling muds containing CuO mud samples produced the highest CTR. Their unique morphologies and various interactions with bentonite in the fluid system were linked to these variations in CTR. The cuttings are best transported at 0°, then 30°, next 90°, with 60° being the least-cleaning hole angle. Cutting behaviour is heavily influenced by the slope and geometry of the hole. At various flow rates, the concentric annulus provided a greater CTR than the eccentric drill pipe. However, flowrate is a major factor in eccentricity, and flow rates greater than 2.4 L/s may result in higher CTE pipe eccentricity. This research is the first effort to assess the use of various NP additions to improve the capacity of drilling fluids to circulate and move drilled cuttings out of the wellbore. With the help of NPs, the cuttings transport performance of WBM can be reasonably improved, and the project risks may thus be reduced. Thus, the study is expected to open new directions in developing NPs material as potential cuttings transport agents
Investigation of silica nanoparticle-polymer hybrid stability under high temperature and salinity for oil displacement application
Full text linkPolymer flooding is one of the most often utilised enhanced oil recovery (EOR) techniques because it provides excellent recovery. Polymer flooding enhances sweep efficiency and reduce viscous fingering severity by increasing fluid and oil mobility. Due to excellent viscosifying nature, and well-known physiochemical properties, partially hydrolyzed polyacrylamide (HPAM) is the polymer most often utilised for the application. However, high temperatures restrict its application because polymer will acts as shear-thinning, such it undergoes shear degradation and reduces viscosity at high shear rates and quickly destabilized and therefore unable to achieve the expected effects. High salinity also causes the molecular chain of the polymer to collapse, which results in a much smaller molecule and hence, produces a lower viscosity solution. Adding nanoparticle to polymer solutions is now required to alter their properties. Therefore, this study aims to investigate the effect of silicon dioxide nanoparticles (SiO2) addition to the stability of HPAM at high temperatures and salinity. The shear viscosity and the flooding performance at high temperature and high salinity gauge the stability of HPAM and the hybrid HPAM- SiO2. A series of stability measurements as well as core flooding experiment with variations of conditions were conducted in order to know the improvement offered by this nanoparticle towards HPAM polymer. At a temperature of 110 0C, the addition of 1 wt% SiO2 nanoparticle have enhanced the viscosity of 0.015 wt% HPAM, from 3.4 cP to 6.8 cP. This resulted in an almost 90% oil recovery rate. It also strengthened HPAM's salt tolerance at concentration of 5 wt% of NaCl by raising its viscosity up to 4.6 cP. This HPAM hybrid also have improve the oil recovery factor for this condition as well up to 85%. In conclusion, adding nanoparticles to HPAM will unquestionably increase the stability and potentially be used in EOR operations
Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
No full textCapnography has received considerable attention owing to its important applications in assessing asthma and other pulmonary diseases. Monitoring abnormal changes in the recorded carbon dioxide waveform (i.e., capnogram signal) allows for detecting respiratory malfunctioning and thereby averting potential asthma attacks. Detecting asthma based on the non-stationary capnogram signal remains an open research problem. In this thesis, an automatic computational framework is proposed to detect asthma. The presented framework includes two main stages. The first stage is responsible for discarding the distorted segments of the recorded capnogram signals. This task was performed in previous studies either manually by visual inspection, using threshold-based or template matching methods. In the current work, a machine learning-based approach is presented to automatically classify artefact-free and distorted capnogram segments. For this purpose, different time- and frequencydomain features are proposed. The time-domain features include energy, variance, skewness, kurtosis, Hjorth parameters and mean absolute deviation (MAD). The frequency-domain features include the area under the magnitude Fourier spectrum in addition to the number of relatively high spectral peaks for a particular frequency range. Different classifiers are trained and tested using the most relevant features: Hjorth activity and MAD. These classification models include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT) andNaive Bayes (NB) classifiers. The results showed that the SVM classifier can provide classification accuracy, specificity, sensitivity and precision of 89%, 91%, 87% and 92.1%, respectively. In addition, a multiple classifiers voting approach is proposed for this classification task. Using this cooperative classification, the specificity is increased from 91% to 94%. The second stage accepts the clean capnogram segments from the first stage and carries out the classification of healthy and asthmatic capnograms. The proposed features are based on Empirical Mode Decomposition (EMD) which is suitable for analyzing the non-stationary capnogram signal in addition to the variance of the raw signal. Unlike the traditional features, the proposed features are extracted from the frequency-domain representation of the signal’s first Intrinsic Mode Function (IMF). The results showed that the NB classifier can provide classification accuracy, specificity, sensitivity and precision of 96.5%, 97%, 96% and 97.18%, respectively
Comparison of calcium hydroxide treated and untreated pumpkin flesh at different dehydration temperature
Full text linkPumpkin is widely planted worldwide, including Malaysia. Some regions have the limitation of oversupply or limited supply for fresh pumpkin. This has raised the urge to produce pumpkin derived products to avoid spoilage and wastage of fresh pumpkin and to cater the high demand from consumers due to its known health benefits. Dehydration is one of food preservation method. Starting from food dehydrating directly under sunlight to developing time saving and cost effective technologies with the aim to improve the quality of dehydrated products. Food dehydrator is a practical technology that can be used to dehydrate pumpkins. It could be used to dehydrate foods at different temperatures with a good air aeration to accelerate the dehydrating process. Four temperatures (50℃, 60℃, 70℃ and 80℃) had been tested to dehydrate pumpkin flesh in the present study. The pumpkin flesh was treated with calcium hydroxide (Ca(OH)2) for better dehydration and food preservation. The dried pumpkins were then ground into powder for storage. The quality of pumpkin products was proven by extracting bioactive components from dried pumpkin using ethanol extraction. The good quality of dried pumpkins was also determined for its biochemical compounds such as ß-carotene, riboflavin, caffeic acid, and quercetin by HPLC. The antimicrobial activities of the extracts towards four microorganisms (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumonia) were also analysed using disc diffusion method. Dehydration of pumpkin flesh was found to exhibit falling rate pattern, which is common in the dehydration of agricultural products. The treated pumpkin flesh that dehydrated at 70°C was able to produce the highest extraction yield (73.54%). However, the degradation of bioactive compounds could be happened during the process of pre-treatment, dehydration, extraction and storage. The explanation was given to the observation in the present study because the assigned compounds could not be detected and no significant inhibition was observed for the selected pathogenic microorganisms. Although dehydration was successfully carried out, the quality of dried pumpkin was not satisfied. Therefore, it is recommended to improve the pre-treatment and extraction techniques to ensure high quality of dehydrated pumpkin flesh for human consumption
Development of drilling non-productive time reduction framework using lean six sigma methodology for buntal exploration wells
Full text linkDrilling Non-Productive Time (NPT) is loss time incurred when drilling activity has to be stopped or rate of penetration is very low during a drilling operation. Drilling NPT results in drilling cost overruns and delay in drilling schedule. Presently, there are several methods which are being deployed to mitigate drilling NPT and these methods would fall into either the anticipative or targeted category. Anticipative category focuses on development of model, database, and risk management framework to enhance information sharing and decision-making among drilling operation stakeholder while the targeted category revolves around deployment of surgical solutions which focus on one or a handful of drilling NPT categories such as wellbore instability, equipment failure etc. Both categories generally lack Drilling NPT severity assessment, validation of Root Causes and derivation of Root Cause Analysis-centric solutions, resulting in knee-jerk reactions to Drilling NPT mitigation. This study, therefore, aims to develop a Drilling NPT reduction framework using Lean Six Sigma (a process improvement methodology) based on retrospective drilling data from Buntal Exploration Wells. Lean Six Sigma advocates data-driven analytics and decision-making in solving operational problems. The drilling NPT reduction framework will follow the DMAIC (Define, Measure, Analyse, Improve and Control) stage-gate model of Lean Six Sigma. In the case of Buntal Exploration Wells, it is observed that the drilling process is not capable from operational and financial standpoints in meeting the Drilling Plan requirements (as illustrated by Process Capability Indexes which are lower than 1.0). In view of retrospective nature of the available drilling data, root causes are validated graphically and qualitatively with equipment failure appearing as the leading contributor of Drilling NPT. Potential solutions are then ideated based on the validated root causes or Vital Few Factors. It is expected that the Drilling NPT reduction framework will pave way for future research through capitalization on quantitative data and continuous feedback from the drilling fraternity