Engineering Journal (Faculty of Engineering, Chulalongkorn University, Bangkok)
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Simulation of Process Structure and Operating Parameters on the Efficiency of the Chemical Looping Combustion Combined with Humid Air Turbine Cycle Using Statistical Experimental Design
Get PDFThis study’s objective is to investigate the process structure and operating variables that affect the efficiency of the CLC combined with humid air turbine (HAT) unit to produce electricity. The investigation was carried out by using the Aspen Plus program with Peng-Robinson-Boston-Mathias (PR-BM) thermodynamics properties. In this study, the process structure and operating parameters were investigated. The process structure was related to process configuration, which reflected the number of compressor stages. The operating parameters were pressure, airflow rate, and compression methods. The four investigated responses consist of LHV efficiency, power production from the air reactor, work of air compressors, and air compressor discharge temperature. The 3k factorial experimental design was employed. After that, the result was analyzed by the analysis of variance (ANOVA). The result showed that the highest LHV efficiency was at 55.87 % when seven stages of compressors were used and the operating condition was at 15 atm of pressure in the air reactor, air compression using method 3, and 61,000 kmol/hr of airflow rate. The pressure and the method of compression highly affected LHV efficiency, as shown by their p-values. The pressure had the highest effect on LHV efficiency. The high pressure provided high power production. Method 3 provided the highest discharged temperature from the air compressor, which was the reason for the high power production in the air reactor. The compression ratio of the last compressor would be 65% of the pressure in the air reactor. Moreover, the efficiency could be improved to 57.67% by increasing the loading of Ni on the oxygen carrier from 25% to 40%. The benefit of the paper will be preliminary data for operation and investment decisions on a CLC power production because this result has not yet been demonstrated
Coastal Upwelling Investigation in the Gulf of Thailand Using Ekman Transport and Sea Surface Temperature Upwelling Indices
Get PDFTwo different upwelling indices: (1) Ekman transport upwelling index (UIET) and (2) sea surface temperature upwelling index (UISST) were evaluated determine likely locations of coastal upwelling occurring in the Gulf of Thailand (GoT). GoT covers the area between longitude 98.0°E to 106.0°E and latitude 5.0°N to 14.0°N. In addition, inter-annual and annual variability of UIET along the east and west coasts were also investigated. UIET was estimated using monthly averaged wind velocity during 2003--2018, while UISST was calculated based on the difference between coastal and oceanic monthly mean sea surface temperature at the same latitude. Based on spatial UIET, favorable upwelling conditions existed mainly along the east and west coasts during northeast and southwest monsoons, respectively. Furthermore, the favorable upwelling conditions were also found sparsely along the east coast and the west coast during the first and second inter-monsoon. The spatial UISST showed favorable upwelling condition along the west and east coasts around Ca Mau Cape during northeast and southwest monsoons. Meanwhile, during first and second inter-monsoons the favorable upwelling conditions rarely occurred. Disagreement of spatiotemporal coastal upwelling between UIET and UISST were likely due to the shallowness and bottom friction in the GoT. Considering inter-annual variability of meridionally averaged UIET along east and west coasts, it was found that favorable/unfavorable upwelling conditions were associated with Multivariate ENSO Index signal. Interestingly, annual cycle of meridionally averaged UIET along west and east coasts showed alternation of favorable and unfavorable upwelling conditions, respectively
Improving the Accuracy of Daylight Calculation with Impact of Sun-shading Devices for the Russian Standard
No full textLighting engineering represents a complex scientific field, which requires the generalization of knowledge in assessing visual comfort, constructive and architectural solutions for the adaptation to the site climate. Global environmental issues and sustainable development require architectural design to serve the modern building achieve maximum energy performance. Especially in hot regions, the specific tasks of designing daylight systems cannot be sufficiently completed without carefully considering the problems of insolation and sunscreens. The present method in Russian regulations is a daylight factor method calculation with high accuracy under the overcast sky and without impact of shading devices. Unfortunately, this method revealed a few possible inaccuracies if considering the impact of shading devices and should be improved with the proposal of two coefficients Kshad and Kref, which replace the approximate factor τ4. The results show that a significant effect from the shading coefficient reduces light entering the room. Light reflection from shading devices is slightly increased by up to 15% when the ground surface has a high reflectance. With this approach, it is possible to complete the method of side-lighting calculation with specific parameters and finishing surfaces of the shading devices. The research shows the potential of increasing light reflection to the room under the real sky with sunlight. Finishing the daylight factor calculation would potentially lead to the development of a method to define the window area in design stage to adequate the daylight requirements, which now processes most in the rule of thumb
Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete
Get PDFConcrete-filled steel tube (CFST) columns are used in the construction industry because of their high strength, ductility, stiffness, and fire resistance. This paper developed machine learning techniques for inferring the axial strength in short CFST columns infilled with various strength concrete. Additive Random Forests (ARF) and Artificial Neural Networks (ANNs) models were developed and tested using large experimental data. These data-driven models enable us to infer the axial strength in CFST columns based on the diameter, the tube thickness, the steel yield stress, concrete strength, column length, and diameter/tube thickness. The analytical results showed that the ARF obtained high accuracy with the 6.39% in mean absolute percentage error (MAPE) and 211.31 kN in mean absolute error (MAE). The ARF outperformed significantly the ANNs with an improvement rate at 84.1% in MAPE and 65.4% in MAE. In comparison with the design codes such as EC4 and AISC, the ARF improved the predictive accuracy with 36.9% in MAPE and 22.3% in MAE. The comparison results confirmed that the ARF was the most effective machine learning model among the investigated approaches. As a contribution, this study proposed a machine learning model for accurately inferring the axial strength in short CFST columns
Enhancing BIM Diffusion through Pilot Projects in Vietnam
Get PDFSignificant BIM uptake barriers in Vietnam include the lack of awareness and knowledge on BIM, high investment costs, and shortage of regulations, standards or guidance related to the AEC industry. This research focuses on enhancing BIM diffusion through eight selected projects with diversity in the type, scale (grade), location and funding sources - representing the myriad of construction project classifications in Vietnam. Additional reporting and interview sessions were held with BIM professionals involved in the projects, with the notion of immersing into professional experience during the project. These experiences were extracted to provide implementation experience and lessons learnt for future projects in Vietnam. This research highlighted the key issues around BIM skills and competencies, BIM investment and BIM regulations, standards and guidance. The research findings will support stakeholders to adopt the solutions to meet BIM adoption challenges to enhance the effectiveness of BIM implementation in future construction projects
Removal of Hydrocarbons from Drill Cuttings Using Flotation Enhanced Stirred Tank (FEST)
Get PDFTreatment of drill cuttings (DC) by washing processes consumes a considerable volume of solvents, resulting in high chemical wastes and operating cost. To minimize the chemical use, this work aims to develop an integrated process of dissolved air flotation (DAF) and mechanical stirring, called Flotation Enhanced Stirred Tank (FEST), as a pretreatment process for DC washing, in which total petroleum hydrocarbon (TPH) was a major pollutant of concern. The performance of an individual DC treatment process (stirring and DAF) was firstly investigated to determine the optimal experimental range. Then, response surface methodology with central composite design was applied to optimize three operational factors (saturated pressure (Ps), mixing speed (Vm), and treatment time (t)) for the integrated process, having TPH removal efficiency as the response output. Effects of hydrodynamic condition in terms of a/G ratio on the TPH removal performance were also analyzed. The experimental results revealed that mixing speed and saturated pressure were the significant factors affecting the TPH removal efficiency. FEST could yield the maximum TPH removal of 47% under the Ps of 4 bars, Vm of 400 rpm, and t of 70 min, showing its better performance than a single process from which less than 40% TPH removal was achieved. Combining DAF with stirring resulted in more turbulence in the system and thus improving the contact between hydrocarbon and bubbles. Therefore, better TPH removal could be obtained from FEST at lower a/G ratios compared to DAF. Furthermore, using saline water as a treatment medium was also possible. Overall, FEST exhibited its potential as an environmentally friendly process for the pretreatment of DC
Kinetics on Biomass Conversion of Terminalia Catappa L. Shell through Isothermal Pyrolysis
Get PDFLignocellulose decomposition in the pyrolitic process are affected by several factors, mainly temperature and reaction time. Conducting isothermal pyrolisis on Terminalia Catappa L., this study aims to determine the reaction mechanism and to justify the kinetics. Powder sample of Terminalia Catappa L. was prepared by grounding it to certain particle size. The temperature was varied and kept constant at 350оC, 400оC, 450оC, 500оC, and 550оC with time interval of 30, 60, and 90 minutes. For kinetics study, data were obtained by measuring the liquid and gas products every 5 minutes. While the solid yield can be calculated using MATLAB simulation program based on the mass balance conception. The results showed that the increase of temperature accelerates the pyrolitic reaction rate increasing the liquid and gas products yield but decreasing the solid product yield. Furthermore the kinetics model of Terminalia Catappa L. pyrolysis was verified to understand the reaction mechanism. It was found that the pyrolysis reaction of Terminalia Catappa L. seed shells refers to the secondary decomposition reaction with the reaction kinetics parameter value of char for 488 min-1 for the exponential factor. , and the reaction kinetics parameter value of Tar 0.38 min-1 for the exponential factor. The primer and secondary decomposition reaction with the reaction kinetics parameter value of tar 71 min-1 for the exponential factor, 0.43 min-1 for the exponential factor, respectively
Estimating Concrete Compressive Strength Using MARS, LSSVM and GP
Get PDFThe estimation of concrete compressive strength is utmost important for the construction of a building. Organizations have a limited budget for mix design; therefore, proper estimation of concrete data has a significant impact on site operations and the construction of the building. In this paper, the prediction of concrete compressive strength is done by Multivariate Adaptive Regression Spline (MARS), Least Squares Support Vector Machine (LSSVM) and genetic programming (GP) which is a very new approach in the field of concrete technology. MARS is a supervised technique, performs well for high dimensional data, interacts less with the input variables, whereas LSSVM is generally based on a statistical learning algorithm and GP builds equations that are generated for modeling. All the developed LSSVM, MARS and GP gives an equations for prediction of compressive strength which makes easy to predict the compressive strength of the concrete. The efficiency of the MARS, LSSVM and GP are measured by the comparative study of the statistical parameters and can be concluded that the all the models performed very well as the output results are very close to the desired value, while the MARS slightly outperformed the other two models
A Comparative Study of Customer Preferences for Telecommunication Technologies in Pakistan
Get PDFThe telecommunication industry is a huge and ever-growing industry and it is contributing dominantly to the economy in terms of revenue generation and being one of the biggest taxpayers to the government. Telecommunication is now considered to be a basic necessity. In this paper, a study has been conducted to know the customer preferences of telecommunication technologies in Lahore, Pakistan, and also to compare different customer preferences for telecommunication technologies. From the results of our study, we have concluded that there is a great variation among people in terms of their usage of mobile phones and internet services. Furthermore, results regarding the relationship between the socio-demographic characteristics and the preference towards specific telephony and internet service providers have also been analyzed. This work is unique when the scenario of Pakistan is considered. No such study has been conducted in Pakistan to know the customer preferences in Pakistan. Some recommendations for the telecommunication operators have also been discussed which can help to know their customer needs in a better fashion
Influence of Feedstock Particle Size from Merbau Wood (Intsia bijuga) on Bio-Oil Production Using a Heat Pipe Fin L-Shaped Condenser in a Pyrolysis Process
Get PDFBio-oil (liquid smoke) can be produced by condensing pyrolysis vapor. As a raw material, the sawdust from Merbau wood (Intsia bijuga) is heated to generate vapor. These vapors are then condensed in a liquid collecting system (LCS). The particle size of the sawdust influences the heating rate and eventually affects bio-oil production. To increase the yield of bio-oil while decreasing the power consumption of the process, the LCS design must be improved. In the present study, the authors aim to understand the relationship between the particle size and the liquid smoke yield using an LCS equipped with L-shaped heat pipe fin condensers. To this end, we experimentally investigated the influence of the particle size (2 mm, 0.707 mm, and 0.595 mm) of the raw material on the liquid smoke yield of an LCS in a pyrolysis process. The results show that increasing the feedstock particle size of Merbau wood sawdust increases the yield of liquid smoke. With a particle size of 2 mm, we achieved a 6% higher yield of liquid smoke than that with a particle size of 0.595 mm. In addition, the installation of the L-shaped heat pipe fin condensers improved the LCS performance