Methyl esters selectivity of transesterification reaction with homogenous alkaline catalyst to produce biodiesel in batch, plug flow, and continuous stirred tank reactors

Selectivity concept is essential in establishing the best operating conditions for attaining maximum production of the desired product. For complex reaction such as biodiesel fuel synthesis, kinetic studies of transesterification reaction have revealed the mechanism of the reaction and rate constants. The objectives of this research are to develop the kinetic parameters for determination of methyl esters and glycerol selectivity, evaluate the significance of the reverse reaction in transesterification reaction, and examine the influence of reaction characteristics (reaction temperature, methanol to oil molar ratio, and the amount of catalyst) on selectivity. For this study, published reaction rate constants of transesterification reaction were used to develop mathematical expressions for selectivities. In order to examine the base case and reversible transesterification, two calculation schemes (Case 1 and Case 2) were established. An enhanced selectivity was found in the base case of transesterification reaction. The selectivity was greatly improved at optimum reaction temperature (60 C), molar ratio (9 : 1), catalyst concentration (1.5 wt.%), and low free fatty acid feedstock. Further research might explore the application of selectivity for specifying reactor configurations

Methyl esters selectivity of transesterification reaction with homogenous alkaline catalyst to produce biodiesel in batch, plug flow, and continuous stirred tank reactors

Selectivity concept is essential in establishing the best operating conditions for attaining maximum production of the desired product. For complex reaction such as biodiesel fuel synthesis, kinetic studies of transesterification reaction have revealed the mechanism of the reaction and rate constants. The objectives of this research are to develop the kinetic parameters for determination of methyl esters and glycerol selectivity, evaluate the significance of the reverse reaction in transesterification reaction, and examine the influence of reaction characteristics (reaction temperature, methanol to oil molar ratio, and the amount of catalyst) on selectivity. For this study, published reaction rate constants of transesterification reaction were used to develop mathematical expressions for selectivities. In order to examine the base case and reversible transesterification, two calculation schemes (Case 1 and Case 2) were established. An enhanced selectivity was found in the base case of transesterification reaction. The selectivity was greatly improved at optimum reaction temperature (60 C), molar ratio (9 : 1), catalyst concentration (1.5 wt.%), and low free fatty acid feedstock. Further research might explore the application of selectivity for specifying reactor configurations

Congestion control in wireless sensor networks

Information-sensing and data-forwarding in Wireless Sensor Networks (WSN) often incurs high traffic demands, especially during event detection and concurrent transmissions. Managing such large amounts of data remains a considerable challenge in resource-limited systems like WSN, which typically observe a many-to-one transmission model. The result is often a state of constant buffer-overload or congestion, preventing desirable performance to the extent of collapsing an entire network. The work herein seeks to circumvent congestion issues and its negative effects in WSN and derivative platforms such as Body Sensor Networks (BSN). The recent proliferation of WSN has emphasized the need for high Quality-of-Service (QoS) in applications involving real-time and remote monitoring systems such as home automation, military surveillance, environmental hazard detection, as well as BSN-based healthcare and assisted-living systems. Nevertheless, nodes in WSN are often resource-starved as data converges and cause congestion at critical points in such networks. Although this has been a primal concern within the WSN field, elementary issues such as fairness and reliability that directly relate to congestion are still under-served. Moreover, hindering loss of important packets, and the need to avoid packet entrapment in certain network areas remain salient avenues of research. Such issues provide the motivation for this thesis, which lead to four research concerns: (i) reduction of high-traffic volumes; (ii) optimization of selective packet discarding; (iii) avoidance of infected areas; and (iv) collision avoidance with packet-size optimization. Addressing these areas would provide for high QoS levels, and pave the way for seamless transmissions in WSN. Accordingly, the first chapter attempts to reduce the amount of network traffic during simultaneous data transmissions, using a rate-limiting technique known as Relaxation Theory (RT). The goal is for substantial reductions in otherwise large data-streams that cause buffer overflows. Experimentation and analysis with Network Simulator 2 (NS-2), show substantial improvement in performance, leading to our belief that RT-MMF can cope with high incoming traffic scenarios and thus, avoid congestion issues. Whilst limiting congestion is a primary objective, this thesis keenly addresses subsequent issues, especially in worst-case scenarios where congestion is inevitable. The second research question aims at minimizing the loss of important packets crucial to data interpretation at end-systems. This is achieved using the integration of selective packet discarding and Multi-Objective Optimization (MOO) function, contributing to the effective resource-usage and optimized system. A scheme was also developed to detour packet transmissions when nodes become infected. Extensive evaluations demonstrate that incoming packets are successfully delivered to their destinations despite the presence of infected nodes. The final research question addresses packet collisions in a shared wireless medium using distributed collision control that takes packet sizes into consideration. Performance evaluation and analysis reveals desirable performance that are resulted from a strong consideration of packet sizes, and the effect of different Bit Error Rates (BERs)

Development of combined vector and torque control methods for independent two induction motor drives

Many applications use two or more motors operating in parallel configuration by using one variable speed drive. This system is able to control these multiple motors at the same desired motor speed operation which provide advantages in terms of components and cost reduction. However, the system is not able to control each motor separately if it is desired to operate at different speeds and it also cannot withstand the load disturbance. To address this problem, the design of combined Vector Control-Direct Torque Control (DTC) methods is proposed and their performance is investigated for the case of independent controlled two induction motors fed by single Five Leg Inverter (FLI) method. Double Zero Sequence (DZS) Injection Method Space Vector Pulse Width Modulation (SVPWM) scheme is used for the FLI. Simulation results from the Simulink/Matlab that verify the validity of the method are also included. The results show the ability of the proposed method to control motor speed independently under forward-reverse step speed command and load disturbance condition

A Decision Tree Based on Spatial Relationships for Predicting Hotspots in Peatlands

Predicting hotspot occurrence as an indicator of forest and land fires is essential in developing an early warning system for fire prevention.  This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. Spatial data consist of a target layer and ten explanatory layers representing physical, weather, socio-economic and peatland characteristics in the study area Rokan Hilir District, Indonesia. Target objects are hotspots of 2008 and non-hotspot points.  The result is a pruned spatial decision tree with 122 leaves and the accuracy of 71.66%.  The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4.5 algorithm. The ID3 decision tree has accuracy of 49.02% while the accuracy of C4.5 decision tree reaches 65.24%

Burn Area Processing to Generate False Alarm Data for Hotspot Prediction Models

Developing hotspot prediction models using decision tree algorithms require target classes to which objects in a dataset are classified.  In modeling hotspots occurrence, target classes are the true class representing hotspots occurrence and the false class indicating non hotspots occurrence.  This paper presents the results of satellite image processing in order to determine the radius of a hotspot such that random points are generated outside a hotspot buffer as false alarm data.  Clustering and majority filtering were performed on the Landsat TM image to extract burn scars in the study area i.e. Rokan Hilir, Riau Province Indonesia.  Calculation on burn areas and FIRMS MODIS fire/hotspots in 2006 results the radius of a hotspot 0.90737 km.  Therefore, non-hotspots were randomly generated in areas that are located 0.90737 km away from a hotspot. Three decision tree algorithms i.e. ID3, C4.5 and extended spatial ID3 have been applied on a dataset containing 235 objects that have the true class and 326 objects that have the false class. The results are decision trees for modeling hotspots occurrence which have the accuracy of 49.02% for the ID3 decision tree, 65.24% for the C4.5 decision tree, and 71.66% for the extended spatial ID3 decision tree

Harmonic reduction of a single-phase multilevel inverter using genetic algorithm and particle swarm optimization

Inverter play important role in power system especially with it capability on reducing system size and increase efficient. Recent research trend of power electronics system are focusing on multilevel inverter topic in optimization on voltage output, reduce total harmonics distortion, modulation technique and switching configuration. Standalone application multilevel inverter is high focused due to the rise of renewable energy policy all around the world. Hence, this research emphasis on identify best topology of multilevel inverter and optimize it among the diode-clamped, capacitor clamped and cascaded H-bridge multilevel inverter to be used for standalone application in term of total harmonics distortion and voltage boosting capability. The first part of research that is identify best topology multilevel inverter is applying sinusoidal pulse width modulation technique. The result shown cascade H-bridge give the best output in both total harmonics distortion (9.27%) and fundamental component voltage (240 Vrms). The research proceed with optimization with fundamental switching frequency method that is optimized harmonic stepped waveform modulation method. The selective harmonics elimination calculation have adapt with genetic algorithm and particle swarm optimization in order to speed up the calculation. Both bio-inspired algorithm is compared in term of total harmonic distortion and selected harmonics elimination for both equal and unequal sources. In overall result shown both algorithm have high accuracy in solving the non-linear equation. However, genetic algorithm shown better output quality in term of selected harmonics elimination where overall no exceeding 0.4%. Particle swarm optimization shows strength in finding best total harmonics distortion where in 7-level cascaded H-bridge multilevel inverter (m=0.8) show 6.8% only as compared to genetic algorithm. Simulation for 3-level, 5-level and 7-level for each multilevel inverter at different circumferences had been done in this research. The result draw out a conclusion where the possibility of having a filterless high efficient invert can be achieve

The extraction of lignin from empty fruit bunch fiber via microwave-assisted acid hydrotrope solvent

Lignin is a sub-product from lignocellulose apart from cellulose and hemicellulose that produced from empty fruit bunch fiber (EFB). Lignin has low solubility and reactivity due to its bulky macromolecule structre. Being one of the wastes that being generated in massive amount, many alternatives has been taken to transform lignin into valuable products. To do so, many reactions are needed for the lignin to go through. In this study, lignin will be extracted from empty fruit bunch (EFB) with the aid of acid hydrotrope concentration of 30 % and microwave assisted with various extraction heating time and temperature. Characterization of lignin is done using Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric analysis (TGA), Differential Scanning Calorimetry (DSC) and Nuclear magnetic resonance (NMR) while Scanning Electron Microscopy (SEM) and X-ray Powder Diffraction (XRD) used to characterize residues. The highest percentage of lignin yield and its purity obtained are 19.47 % and 96.63 % with the reaction time and temperature of the microwave is 30 minutes and 90 °C. From Fourier Transform Infrared Spectroscopy (FTIR), a wide band at 3430.09 cm-1 and 3413.45 cm-1 are observed due to O-H stretching vibration. As for peak at 1123.17 cm-1 and 1051.26 cm-1, it correspond to syringyl and guaicyl unit in both lignin and raw EFB. As for Thermogravimetric analysis (TGA), it shows that lignin decomposes slowly compared to raw EFB due to the aromatic structure of lignin that is very stable, therefore leading to difficulty of decomposing while from Differential Scanning Calorimetry (DSC), after removing cellulose and hemicellulose, glass transition temperature (Tg) obtained from lignin DSC spectroscopy is 193.05 °C at heat flow of 1.15 mW/mg. Next, from Nuclear magnetic resonance (NMR) spectroscopy, the signals observed around 6.5 – 8.0 ppm indicate aromatic H in syringyl and guaiacyl unit only at lignin spectra while at 3.3 – 4.0 ppm, raw EFB has an intense peak compared to lignin which attribute to methoxyl group. When the residue of the lignin as well as the raw EFB powder is characterized using X-ray Powder Diffraction (XRD), the crystallinity index of the lignin with reaction time and temperature of the microwave 30 minutes and 90 °C is the highest, 69.28 %. As a conclusion, an admissible percent of lignin yield and purity is able to be obtained with addition of acid hydrotrope depending on the variables. From the spectroscopies characterization, it is proved that lignin characteristics and properties are compatible for the production of new and value added products

Use of gas liquid chromatography in combination with pancreatic lipolysis and multivariate data analysis techniques for identification of lard contamination in some vegetable oils

A study was conducted to investigate the use of gas liquid chromatography (GLC) to identify lard (LD) contamination in palm oil (PO), palm kernel oil (PKO), and canola oil (CLO). Vegetable oils were deliberately adulterated with animal fats such as LD, beef tallow (BT), and chicken fat (CF) in varying proportions. In order to monitor the fatty acid (FA) compositional changes due to adulteration, GLC analyses of fatty acid methyl esters (FAME) were performed on 2-monoacylglycerol (2-MG) and neutral triacylglycerol (TAG) isolated from each sample. For the evaluation of FA data, multivariate statistical techniques were employed. The results showed that canonical discriminant (CANDISC) analysis was the most effective technique for discriminating LD-adulterated samples from those adulterated with other animal fats. Additionally, mathematical equations obtained by simple regression analysis could be used for quantification of LD contents in admixtures