Machine Learning Based Mobile Network Data Analysis and Prediction in Wireless Communication Network

With the rapid development of wireless networks, more and more online services significantly raise mobile data traffic demands, which causes a massive challenge for wireless network operators. In addition to deploying more communication facilities to improve the whole wireless network capacity, real-time observation and prediction of the mobile data traffic to achieve a dynamic balance of network load can further improve the efficiency of the services from those operators and reduce energy waste. In general, the accuracy of mobile data traffic prediction directly impacts the entire network system's Quality of Service (QoS) and Operating Expenditure (OPEX). Therefore, mobile network traffic prediction is the main research direction of this thesis. Firstly, the user's Point of Interest (POI) exploration is chosen as a key point for analysis. This kind of user mobility modelling represents an essential branch of mobile traffic analysis. By applying machine learning algorithms, clear summaries of the mobility pattern characteristics of typical wireless users are obtained. Through the analysis of these regular characteristics, the value of mobility information related to the user's POI is initially demonstrated. Subsequently, this thesis introduces a data prediction model based on the Long Short-Term Memory (LSTM) model, a typical neural network for sequence prediction. Through verification of predictions using real sampled user data, it further demonstrates that the user's POIs tend to be relatively fixed and exhibit periodicity. Additionally, by comparing the prediction results with those of other models, the advantages of neural networks, particularly LSTM, in sequence prediction are evident. Subsequently, this thesis aims to enhance the accuracy of wireless data traffic prediction by exploring location information. Although former researches have indicated that the distance relationship may affect the similarity of mobile communication traffic across different base stations, there is a lack of studies regarding the selection of the training dataset scope for urban mobile traffic. Building upon the previous research on the user's POI characteristics, this thesis verifies that the mobile network data trends of base stations in distant regions could also exhibit high similarity with applying real-world data, thereby expanding the training sample range. After that, a multi-task learning framework called MTL-STPN is designed to incorporate these highly correlated mobile traffic data as auxiliary content for predicting target region mobile traffic data. The results demonstrate that the designed model achieves nearly a 10\% improvement in mobile traffic prediction compared to the state-of-the-art traffic prediction models with Root Mean Square Error (RMSE) measurement as prediction metrics. This outcome substantiates that reasonable correlations between mobile network traffic samples can be applied to enhance the performance of appropriate algorithms. Finally, to address the more complex and bursty but highly valuable application-level mobile network traffic prediction, specifically Instant Messaging (IM), this thesis further improves upon the characteristics of the sub-models extracted from the multi-task framework and proposes a novel deep stacked learning architecture called SLIM-TP. After operating the sub-models for extracting the spatiotemporal dependencies of traffic as well as the mobile users’ equipment (UEs) behavioural information, the meta-learner is employed to make optimal decisions regarding these features and effectively retain the factors that can enhance prediction accuracy. Experimental results based on a large dataset collected from a real cellular network demonstrate that the proposed model achieves over 40\% improvements in WeChat traffic prediction performance compared to the state-of-the-art traffic prediction models through RMSE measuring. It shows the effectiveness of incorporating high-dimensional data such as user location and related traffic as auxiliary features in complex mobile network traffic prediction scenarios

Contrasting the pyrolysis behavior of selected biomass and the effect of lignin

This study was aimed at comparing the pyrolysis behavior of several selected biomass samples, namely, pine wood, poplar wood, wheat straw, and sugarcane bagasse, with a particular attention to the effect of lignin. Raw samples were first treated using Soxhlet solvent extraction with a 2:1 (v/v) mixture of toluene/ethanol to remove wax. Lignin was then removed by soaking the dewaxed samples in a 1.0 M sodium chlorite solution at 343 K till the solids became white. Fourier transform infrared (FTIR) spectroscopy analysis was applied to characterize the surface functional groups of the samples. The morphology of the samples before and after delignification treatment was analyzed using scanning electron microscope (SEM). The pyrolysis behavior of the raw and treated biomass samples was studied using a thermogravimetric analyzer (TGA) operating in nitrogen at a constant heating rate of 10 K min−1 from room temperature to the final temperature 823 K. The FTIR and SEM results indicated that lignin can be successfully removed from the raw biomass via the chemical treatment used. As expected, the pyrolysis behavior differed significantly among the various raw biomass samples. However, the pyrolysis behavior of the delignified samples showed almost identical thermal behavior although the temperature associated with the maximum rate of pyrolysis was shifted to a lower temperature regime by ca. 50 K. This suggests that the presence of lignin significantly affected the biomass pyrolysis behavior. Thus, the pyrolysis behavior of the biomass cannot be predicted simply from the individual components without considering their interactions

Kinetic Modeling Study of the Effect of Iron on Ignition and Combustion of <i>n</i>‑Heptane in Counter-flow Diffusion Flames

A kinetic modeling study of the effect of iron on the ignition and combustion characteristics of diesel, modeled as <i>n</i>-heptane, in compression ignition engines was carried out using CHEMKIN PRO. The ignition was simulated using the SENKIN code, and combustion was modeled using the OPPDIF code. The kinetic models incorporated <i>n</i>-heptane mechanisms involving 159 species and 1540 reactions and iron reaction mechanisms of 7 iron species and 46 reactions. It was found that small amounts of iron in the fuel significantly reduced the ignition delay time. The ignition delay time decreased with an increasing iron concentration. A reaction pathway analysis showed that the ignition was promoted as a result of an early injection of the OH radicals. It was also showed that the addition of iron increased the peak flame temperature of <i>n</i>-heptane in the counter-flow diffusion flame and reduced the maximum mole fractions of H and O in the peak flame region as a result of the catalytic recombination cycles involving FeO, Fe­(OH)₂, and FeOH. The reaction rates of H + O₂ ⇔ O + OH and CO + OH ⇔ CO₂ + H in the peak flame region were found to increase, which is considered to be responsible for the increased peak flame temperature

A preliminary attempt of direct methanol synthesis from biomass pyrolysis syngas over Cu/ZnO/Al2O3 catalysts

The possibility of direct methanol synthesis from biomass pyrolysis syngas (BPS) over Cu/ZnO/Al2O3 catalysts was investigated using a fixed bed reactor. A commercial benchmark catalyst and an in-house Cu/ZnO/Al2O3 catalyst prepared using the co-precipitation method, Cu/Zn/Al = 68/28/4 wt%, were tested for methanol synthesis from a model BPS containing 25% H2, 25% CO, 20% CH4, 20% CO2, and 10% N2. The experiments were performed under different conditions of temperature (220–260 °C), pressure (2–4 MPa) and time on-stream (TOS, 0–100 h). Methanol was successfully synthesised with the highest space-time yield of 0.185 g gcat−1 h−1. Higher pressures and lower temperatures favoured methanol production. The fresh and used catalyst samples were also characterised using N2-physisorption (BET), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) to investigate the mechanisms of catalyst deactivation. While CH4 present in the BPS did not affect catalyst performance, the high concentration of CO2 enhanced Cu oxidation, leading to decreased catalyst performance with increasing TOS. After 100 h TOS, the methanol yield obtained over the commercial and in-house catalysts decreased by around 9.2% and 4.3%, respectively. This research has provided new insights into the challenges of producing methanol from unconventional syngas and motivated future work to develop a robust catalyst that can serve syngas with H2-deficient and high concentrations of CO2