Anjalai Ammal Mahalingam Engineering College

About: Anjalai Ammal Mahalingam Engineering College is a based out in . It is known for research contribution in the topics: Adsorption & Single crystal. The organization has 98 authors who have published 75 publications receiving 597 citations.

Optimization of operational parameters on performance and emissions of a diesel engine using biodiesel

Abstract: This work investigates the influence of compression ratio on the performance and emissions of a diesel engine using biodiesel (10, 20, 30, and 50 %) blended-diesel fuel. Test was carried out using four different compression ratios (17.5, 17.7, 17.9 and 18.1). The experiments were designed using a statistical tool known as design of experiments based on response surface methodology. The resultant models of the response surface methodology were helpful to predict the response parameters such as brake specific fuel consumption, brake thermal efficiency, carbon monoxide, hydrocarbon and nitrogen oxides. The results showed that best results for brake thermal efficiency and brake specific fuel consumption were observed at increased compression ratio. For all test fuels, an increase in compression ratio leads to decrease in the carbon monoxide and hydrocarbon emissions while nitrogen oxide emissions increase. Optimization of parameters was performed using the desirability approach of the response surface methodology for better performance and lower emission. A compression ratio 17.9, 10 % of fuel blend and 3.81 kW of power could be considered as the optimum parameters for the test engine.

Modelling the kinetics of coagulation process for tannery industry effluent treatment using Moringa oleifera seeds protein

Abstract: The ability of the natural coagulant extracted from Moringa oleifera seeds to remove the turbidity from tannery industry wastewater was studied. Coagulation experiments were performed using conventional jar test apparatus with 4–9 pH range and coagulant dose ranging from 10 to 50 mL. The active coagulation component of M. oleifera seed was prepared separately using NaCl and KCl salt solutions. Turbidity removal efficiency of NaCl-extracted coagulant and KCl-extracted coagulant was compared. The turbidity of raw tannery wastewater was reduced from 121.9 to 29.01 mg/L at an optimum pH 7 and coagulant dosage of 40 mL. The maximum turbidity removal efficiency was observed as 76.2 and 71.2% for NaCl- and KCl-extracted coagulants, respectively. The coagulation kinetic study suggested that the process follows second-order kinetics for both type coagulants, and the parameters for rate equation were obtained from the regression equations.

Comparative studies on modelling and optimization of hydrodynamic parameters on inverse fluidized bed reactor using ANN-GA and RSM

Abstract: Optimization of hydrodynamic parameters in counter flow inverse fluidized bed reactor (IFBR) is studied in this paper. Comparative analysis was made between artificial neural network (ANN) and response surface methodology (RSM) to evaluate the parameters. The effects of operating variables such as bed volume (300–1200 cm3), superficial liquid velocity (0.37–1.84 cm/s) and superficial gas velocity (0.07–0.59 cm/s) on percentage bed expansion, liquid holdup, gas holdup, solid holdup and average pressure drop were evaluated using three-factorial Box-Behnken design (BBD). The same was utilized to train a feed forward multilayer perceptron (MLP), ANN with back-propagation algorithm. The predicted values of the both the methodologies were compared with error functions such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), model predictive error % (MPE), chi-square ( χ 2 ) and correlation coefficient (R2). Optimization of operating conditions was obtained through Derringer’s desirability function (RSM) and genetic algorithm (GA), and the results were compared. It is ascertained that well trained ANN-GA has provide a high sensitive results.