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The present study analyzes performance of algae biodiesel in low-powered single-cylinder diesel engine running at rated constant speed of 1500 rpm and 210 bar fuel injection pressure. Algae biodies...

Underground coal gasification (UCG) is a promising clean coal technology to convert unmineable and deep coal reserves into syngas, which can be used in many industrial applications. In UCG field, real time monitoring of hydrological and geological conditions such as water influx rate, cavity growth and its interaction with overburden is a formidable task. UCG project Thar (UPT) lacks real time data acquisition system to monitor these parameters. In this work, a 3D axisymmetric cavity simulation model (CAVSIM) is parameterized with operating conditions of UPT and properties of Lignite B coal of Thar coal fields. For model validation, a comparison has been made between simulated and the UPT field data for the composition and heating value of syngas. The results of CAVSIM are also compared with our previous ID packed bed model, which show the superiority of CAVSIM model. Moreover, a comprehensive simulation study has been carried out to predict the cavity growth and its interaction with overburden. The effect of operating parameters of UPT on volumetric cavity growth and heating value of syngas are also investigated.

Two challenges that have motivated researchers are the mitigation of emissions and a reduction in the reliance on diesel fuel. A potential replacement for diesel is biodiesel, which is derived from animal fat or vegetable oil. The large number of studies on the performance and emission characteristics of biodiesel is notable. In such studies, the noise emissions have seldom been disregarded or treated as a trivial matter. Extending the previously published research by the authors, an experimental investigation was carried out to study the effects of new fuel types on the noise emissions. Blends of Jatropha methyl ester (JME) biodiesel suspended with zinc oxide (ZnO) nanoparticles along with hydrogen (H2) in dual-fuel mode were used as fuel for an experimental diesel engine test rig. The noise levels in decibels (dB) under variations in the biodiesel percentage, nanoparticle size, and flow rates of H2 at different loads were recorded. It was observed that 20% and 30% JME biodiesel blends suspended with ZnO nanoparticles of 40 nm in size have superior noise attenuation. To avoid a strenuous experimentation, an artificial neural network model was developed for noise prediction with a regression coefficient of 0.9992.

The calculation of eigenvalues (EVs) is important in the domain of complex computation and processing of matrices of a higher order. For real-time applications, to get superior performance, the architecture of EV computation comes in handy. In this paper, the authors have investigated an efficient architecture for EV computation using the Jacobi algorithm on the Xilinx Zed-Board FPGA evaluation platform, Artix-7 family. The iterative algorithm proves to be faster and more efficient in terms of area consumed in the FPGA architecture and also in terms of accuracy of the EV computation. The proposed architecture can take up to matrices of order $\mathbf{20}\ \mathbf{x}\ \mathbf{20}$ using the Jacobi algorithm. The algorithm also works for odd order matrices with few modifications in the input matrix. The architecture was implemented on Zynq-7000 xc7z020clg484-1 FPGA and it takes around 4216 LUTs out of 53200 LUTs for matrices of dimensions $\mathbf{4}\ \mathbf{x}\ \mathbf{4}$ .

An imperative requirement of a modern machining system is to detect tool wear while machining to maintain the surface quality of the product. Vibration signatures emanating during machining with a single point cutting tool have proven to be good indicators for the tool’s health. The current research undertaken utilizes vibration signatures while turning EN9 and EN24 steel alloy to predict tool life using Artificial Neural Network (ANN). During initial meager experimentation, tool acceleration during machining was recorded, and the width of the flank wear at the end of each run was measured using Tool Makers Microscope. The recorded experimental data is utilized to develop the neural network with the variation of operating parameters and corresponding tool vibration with measured tool flank wear. The endeavor undertaken for the development of ANN flank wear prediction model was effective with a regression coefficient of 0.9964. The proposed methodology of indirect measurement of tool wear is efficient, economical for the machining industry to predict tool life, which in turn avoids catastrophic tool failure.

The energy per unit time is an important performance indicator in determining the performance of an underground coal gasification (UCG) site to produce electricity. In literature, model-based strategies are employed by considering UCG as a single input single output (SISO) system, in which only the heating value of syngas is maintained at the desired level by varying inlet gas flow rate. However, the energy per unit time is also dependent on the flow rate of the produced gas mixture. Therefore, in this work, a model-based multi-variable robust control design, based on H ∞ technique is proposed for the UCG process. The actual nonlinear model of UCG is very complex due to its 3D axisymmetric geometry, which makes the model-based control design a formidable task. Thus, a simple linear model with two inputs (flow rate and composition of inlet gas) and two outputs (flow rate and heating value of syngas) is identified by using subspace-based (N4SID) system identification technique. The linear model is then employed to design the H ∞ ( S / K S mixed sensitivity) multi-variable robust controller. The simulation results show that the designed controller has achieved both robust stability and performance in the presence of modeling inaccuracies and external disturbance. Furthermore, the designed controller is also implemented on the actual nonlinear cavity simulator (CAVSIM) for the UCG process. The controller exhibits an adequate performance by tracking the desired set points for the heating value and flow rate of the syngas.

Emissions from a Direct Injection (DI) diesel engine can be reduced by the addition of metallic fuel-borne additives. However, the effect of fuel-borne catalysts in a dual fuel engine with hydrogen (H2) as a secondary fuel is not well known. Hence, experimentation was carried out to investigate the effects of nano-metallic oxide fuel additives on the major physicochemical properties and performance of jatropha biodiesel blends in a DI diesel engine in dual fuel mode. Jatropha methyl ester (JME) biodiesel is produced from degummed crude jatropha oil after reducing the free fatty acid (FFA) content to less than 2% and performing transesterification using a 20 kHz frequency ultrasonicator with a sodium hydroxide (NaOH) catalyst. Zinc oxide (ZnO) nanoparticles at 100 ppm with a size of 20 and 40 nm were suspended in primary fuel of JME biodiesel. H2 as a secondary fuel with flowrates of 0.5 and 1.5 L/min was maintained during the experiments. The experimental results reveal that the nanoparticle size influences the engine performance and emissions. The presence of nanoparticles in the fuel blends reduced the nitrogen oxide (NOx) emissions. However, the effects of the size and concentration were marginal with an increasing H2 flowrate. With an increasing H2 flow rate, hydrocarbon (HC) emissions decreased for nanoparticles of size 20 nm, but increased for 40 nm. Smoke opacity was increased compared with pure biodiesel owing to the presence of surfactant Triton-X100.

This paper studied tri-basic potassium phosphate for transesterification process with degummed crude Jatropha curcas oil using constant-temperature, ultrasonic water bath generating low-intensity pulses with good energy distribution converting the maximum amount of biodiesel. Tri-basic potassium phosphate is suitable for J. curcas oil when the free fatty acid (FFA) content is less than 2%. The optimal reaction levels are catalyst 1.0 wt%, temperature of 50°C, and methanol-to-oil molar ratio of 12:1. The yield is 98% after 45 min, at 20 kHz frequency. The catalytic activity is found similar to potassium hydroxide and the catalyst solubility is only 4.27 ppm.

In pursuit of environmental friendly alternative fuels for diesel engines, biodiesel is a promising alternative. Efforts are on in utilizing the biodiesel with diesel in Internal Combustion (IC) engines, because of its reduced pollution characteristics. Long-term effects of these biofuels in IC engines have not been explored earnestly. Enduring effects of which are high noise & vibration and irregular & erratic combustion leading to knocking. Few researches on vibration analysis of biodiesel blends have been reported. Hence, an effort is made to study the vibration characteristics of biodiesel blends amidst Zinc Oxide (ZnO) nano particles with hydrogen in dual fuel mode. Initially, experimentation is carried out to record vibration signatures with 100 ppm concentration of ZnO particles of 20 & 40 nm sizes suspended in Jatropha Methyl Ester (JME) biodiesel along with hydrogen as secondary fuel. In order to avoid strenuous experimentation Artificial Neural Network (ANN) model was developed to predict Root Mean Square (RMS) of velocity. ANN predictions are found to be scrupulously matching with the experimental values as manifested by the regression values of 0.97185, 0.98574 & 0.96913 for prediction of RMS velocities in horizontal, vertical & axial directions respectively. It is found that the best fuel blend with least vibration is B30 & B20 with nano particle of size 40 nm for hydrogen flow rates of 0.5, 1.0 & 1.5 l/min.