Flame temperature analysis of biodiesel blends and components

Meeting sustainable energy demand with minimum environmental impact is a major area of concern in the energy sector. Alternative fuels such as biodiesel, ethanol etc. have been quite promising for fulfilling both these aspects. While biodiesel reduces emissions of CO, life cycle CO₂, SO_x, volatile organic compounds (VOC) and particulate matter (PM) significantly, the propensity for the production of NO_x is an important problem that requires extensive research. NO_x emission from a direct-injection diesel engine is mainly due to formation of thermal NO that is described by Zeldovich mechanism. Thus, studying temperature profile during biodiesel combustion can provide useful insights to the formation and destruction of NO_x. The main objective of this work is to investigate the effect of component methyl esters of biodiesel on open air flame temperature distribution and the effect of blending biodiesel with diesel and oxygenates (ethanol and methyl acetate) on open air flames. This objective was achieved by obtaining thermocouple measurements and thermal infrared imaging of local flame temperatures of wick-generated open air flames. A relationship between blend proportions and relative flame temperatures were obtained. In general, it was found that blending oxygenates such as ethanol and methyl acetate into petroleum diesel tended to increase the flame temperature in comparison with straight diesel fuel. The analyses of relative flame temperatures of different components of biodiesel were performed to evaluate the effect of unsaturation level and the hydrocarbon chain length on the flame temperature. It was found that the saturated methyl esters resulted in greater flame temperatures in comparison to unsaturated methyl esters. It was also revealed that shorter chained fatty acid methyl esters lead to higher flame temperatures as compared to its longer chained counterparts.     

Multi‐parametric linear programming under global uncertainty

Multi‐parametric programming has proven to be an invaluable tool for optimisation under uncertainty. Despite the theoretical developments in this area, the ability to handle uncertain parameters on the left‐hand side remains limited and as a result, hybrid, or approximate solution strategies have been proposed in the literature. In this work, a new algorithm is introduced for the exact solution of multi‐parametric linear programming problems with simultaneous variations in the objective function's coefficients, the right‐hand side and the left‐hand side of the constraints. The proposed methodology is based on the analytical solution of the system of equations derived from the first order Karush–Kuhn–Tucker conditions for general linear programming problems using symbolic manipulation. Emphasis is given on the ability of the proposed methodology to handle efficiently the LHS uncertainty by computing exactly the corresponding nonconvex critical regions while numerical studies underline further the advantages of the proposed methodology, when compared to existing algorithms. © 2017 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 63: 3871–3895, 2017     

Effect of incompletely converted soybean oil on biodiesel quality

Presence of fully converted monoalkyl esters is the major requirement in quality biodiesel. Due to high associated costs with testing and widespread production of biodiesel not only in commercial scale but also in small scale, there is a high propensity of substandard biodiesel entering the market and being used in compression ignition engines. Due to this reason, it is important to understand how low grade biodiesel with a lower methyl ester conversion affects the parameters of quality standards and as a result, engine performance and durability. In this paper, the performance of fatty acid methyl esters with different proportions of unconverted triglycerides has been evaluated. The study has comprehensively evaluated the effect of unconverted triglycerides on flash point, water and sediment, kinematic viscosity, sulfur content, sulfated ash, copper strip corrosion, cetane number, cloud point, carbon residue, acid number, free glycerin, total glycerin, phosphorus content and distillation temperature.     

Polarographic reduction of aryl hydrazono alloxans

The polarographic reduction of a number of coupled products of aryl diazonium salts with 1,3-dimethyl barbituric acid have been studied over a wide pH range. The effects of substituent groups, solvent composition, ionic strength, different cations and surface active compounds on the electrode reaction is discussed.     

Disturbance rejecting optimal regulation of hyperbolic systems

Optimal regulation of hyperbolic systems in the presence of unknown exogenous and initial disturbances is considered. Necessary conditions for determining the optimal control that tracks a desired trajectory in the presence of disturbances are developed. These necessary conditions have the form of a twopoint boundary-value problem along with certain equality and inequality conditions. The results also characterize the worst possible disturbances that are accommodated by the optimum controller without any serious performance degradation. Numerical results on the control of a vibrating beam are presented.     

A way forward towards a technology-driven development of industry 4.0 using big data analytics in 5G-enabled IIoT

The evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G-enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G-enabled IIoT. The paper presents the state-of-the-art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT-driven 5G technology. We have also highlighted the revolution of IIoT-driven 5G framework which satisfies the demands of IIoT applications.     

A novel image mining technique for classification of mammograms using hybrid feature selection

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and gray-level co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for gray-level co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging.