Faculty Publications
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Item Enhancing gallic acid content in green tea extract by using novel cell-associated tannase of Bacillus massiliensis(2013) Palabhanvi, B.; Belur, P.D.Gallic acid content in green tea extract was enhanced by using a novel cell-associated tannase of Bacillus massiliensis. Biomass that contains tannase was used for this study. The activity of the cell-associated tannase was stable during 1 week of storage in the refrigerator. Response surface methodology was applied based on central composite design to determine the effects of three independent variables (pH, temperature and incubation time) and their mutual interactions. A total of 16 experiments were conducted; and a statistical model was developed, which predicted 475.74mg/L gallic acid production at pH6.2, 36C and incubation period of 16.71h. The subsequent verification experiments confirmed the validity of the model. Under optimal conditions, 84.7% of the total hydrolyzable tannins were converted to gallic acid and glucose. This naturally immobilized tannase was stable enough to be used for up to 12 runs. Practical Applications: The current study shows that naturally immobilized tannase of Bacillus massiliensis can be used instead of artificially immobilized tannase. Such naturally immobilized tannase has many advantages as it avoids expensive and laborious isolation, purification and immobilization. Ease of separation of cell-associated enzyme from the reaction mixture and absence of any detectable extracellular tannase activity after enzymatic treatment are some of the encouraging facts. Stability during storage up to 7 days, 85% tannic acid hydrolyzing efficiency, activity at pH3.5-8.0 and operational stability for 12 runs are some of the interesting features of this naturally immobilized enzyme. However, its application for tea treatment will be limited until Bacillus massiliensis gets "Generally Recognized As Safe" status. It can be employed, however, for production of gallic acid from agro residues and production of propyl gallate. © 2012 Wiley Periodicals, Inc.Item An optimization study of microwave assisted extraction of oil from oily sludge using response surface methodology(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Kumar, B.; Raj Mohan, B.Petroleum oily sludge, a hazardous waste, generated by the refineries and at the production sites in huge quantities comprises of a mixture of petroleum hydrocarbons, asphaltenes, long chain paraffinic wax, waste water, sediments and metals. The present study is aimed to recover oil from the petroleum oily sludge using n-heptane as the solvent in microwave assisted solvent extraction process and to optimize the process variables for the recovery of oil from the oily sludge. The simultaneous effects of process variables such as irradiation time (2 - 10 minutes), solvent to sludge ratio (40 – 80 wt %), reactant volume (100 – 300 ml) and microwave power (80 – 400 W) on the recovery of oil were evaluated. A central composite design (CCD) and response surface methodology (RSM) were used for the optimization of the extraction process. Based on the CCD, quadratic model was developed to correlate the extraction process variables with the responses and the model was analysed using appropriate statistical method (ANOVA). Optimization of process variables shows that the maximum recovery of oil was about 88.6% at 100 ml of reactant volume with microwave power output of 351 W at 6.5 minutes of irradiation time with 58.99% of nheptane to sludge ratio. © 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Modelling of squeeze casting process using design of experiments and response surface methodology(Maney Publishing maney@maney.co.uk, 2015) Gowdru Chandrashekarappa, M.; Krishna, P.; Parappagoudar, M.B.The present work makes an attempt to model and analyse squeeze casting process by utilising design of experiments and response surface methodology. The input–output data for developing regression models and test cases is obtained by conducting the experiments. Surface roughness, ultimate tensile strength and yield strength have been measured for different combinations of process variables, namely, squeeze pressure, pressure duration, pouring temperature and die temperature. Two non-linear regression models based on central composite design (CCD) and Box-Behnken design (BBD) of experiments have been developed to establish the input–output relationships. The effects of process variables on the measured responses have been studied using surface plots. The performances of the two non-linear models have been tested for their prediction accuracy with the help of 15 test cases. It is observed that, both CCD and BBD, the non-linear regression models are statistically adequate and capable of making accurate predictions. © 2015 W. S. Maney & Son Ltd.Item Microwave-Assisted Extraction of Wax from Oily Sludge: An Experimental Study and its Process Variables Optimization Using Response Surface Methodology(Bellwether Publishing, Ltd., 2015) Kumar, B.; Raj Mohan, B.The wax present in petroleum sludge, generated by refineries and at crude production sites, consists of paraffin hydrocarbons (C18–C36) known as paraffin wax and naphthenic hydrocarbons (C30–C60). The present study is aimed at the recovery of wax from petroleum oily sludge by microwave-assisted solvent extraction using a Toluene/MEK mixture and subsequently de-crystallizing the wax. The process variables affecting the microwave-assisted solvent extraction are optimized for recovery of wax. The simultaneous effects of process variables such as irradiation time (2–10 minutes), solvent to sludge ratio (40–80 wt%), reactant volume (100–300 ml), and microwave power (80–400 W) on the recovery of wax were evaluated. A central composite design and response surface methodology were used for the optimization of the extraction process. Based on the central composite design, quadratic models were developed to correlate the extraction process variables with the responses and the models were analyzed using appropriate statistical methods for analysis of variance. Optimization of process variables shows the maximum recovery of wax was about 79.57% at 300 ml of reactant volume with microwave power output of 400 W at 7.6 minutes of retention time with 56.56% of Toluene/MEK to sludge ratio. © 2015, Taylor & Francis Group, LLC.Item Influence of process parameters on material removal rate and surface roughness in WED-machining of Ti50Ni40Cu10 shape memory alloy(Inderscience Publishers, 2016) Manjaiah, M.; Narendranath, S.; Basavarajappa, S.; Gaitonde, V.N.Among the shape memory alloys (SMAs), TiNi SMAs have been typically used as the functional elements in the larger part of the industries due to exceptional properties like super elasticity and shape memory effect. However, traditional machining of these alloys is fairly complex due to these properties. The non-traditional machining process like electric discharge machining (EDM) exhibits outstanding capability in machining of these alloys. The poor selection of machining parameters may cause increased roughness of workpiece and lesser material removal rate. Hence, an effort has been made in the present work to explore the effects of three process parameters, such as pulse on time, pulse off time and servo voltage in wire electric discharge machining (WEDM) of Ti50Ni40Cu10 shape memory alloy (SMA) using zinc coated brass wire electrode on material removal rate and surface roughness using response surface methodology (RSM)-based mathematical models. The experiments were planned as per central composite design (CCD). The investigations revealed that pulse on time and servo voltage have predominant effects in maximising material removal rate and minimising surface roughness. The best combination of the process parameters for multi-response optimisation was obtained through desirability function. ©2016 Inderscience Enterprises Ltd.Item Squeeze casting process modeling by a conventional statistical regression analysis approach(Elsevier Inc. usjcs@elsevier.com, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.During the casting process, the alloy composition, melt treatment modification, processing method, and process variables change the microstructure, thereby affecting the mechanical properties. The hybrid squeeze casting method has been used to limit casting defects, refine the micro-structure, and enhance the mechanical properties. The process variables influence the mechanical and micro-structure properties during squeeze casting. In the present study, we established nonlinear input–output relationships and explored the physical behavior of this process based on the statistical design of experiments and using the response surface methodology. Experiments were conducted to measure the responses in terms of the density, hardness, and secondary dendrite arm spacing. Two nonlinear regression models, i.e., Box–Behnken design and central composite design, were used to conduct experiments, collect experimental data, identify significant process variables, analyze the collected data, and establish the complex input–output relationships. Surface plots were used to explore the effects of the squeeze pressure, pressure duration, pouring, and die temperature on the measured responses. Analysis of variance tests were conducted to evaluate the statistical suitability of the models developed. Furthermore, the accuracies of the predictions made by the models were investigated based on test cases. We found that both of the nonlinear models were statistically adequate and they provided complete insights into the complex nonlinear input–output relationships. Central composite design performed better for the secondary dendrite arm spacing and hardness responses, whereas its performance was the same as that of Box–Behnken design for the density response. The relationships between the responses (i.e., outputs) were established by generating large volumes of input–output data using the nonlinear regression models. We found that the density, hardness, and secondary dendrite arm spacing responses could be obtained by utilizing the nonlinear regression equations and the same set of process variables. Furthermore, the secondary dendrite arm spacing response could be expressed as third order nonlinear functions of density or hardness (structure to property relationship). The results showed that the secondary dendrite arm spacing had inverse relationships with density and hardness, whereas density and hardness had direct relationships. © 2016 Elsevier LtdItem Isolation and identification of Pseudomonas from wastewater, its immobilization in cellulose biopolymer and performance in degrading Triclosan(Academic Press, 2019) Devatha, C.P.; Narasimhappa, N.Triclosan (TCS) is a well-known emerging contaminant got wide use in daily use products of domestic purpose, which provides the way to enter the ecological cycle, and is preferably detected in sewage treatment plants. In this study, TCS degrading bacteria (TDB) was isolated and identified from a wastewater treatment plant at the National Institute of Technology-Karnataka, Surathkal (NITK), India. The isolate was reported as Pseudomonas strain by performing 16S RNA Sequencing using BLAST analysis. Bacterial growth depends upon several environmental factors. Hence its growth optimization was carried out by response surface method (RSM) based central composite design (CCD) and validated by the artificial neural network (ANN). The Parameters or inputs used for optimization are pH, time (days), agitation (rpm) and sorbent dosage (?g/L). Experiments were conducted in batch mode to achieve optimum growth of bacteria based on RSM trial runs. The RSM model predictions were in better agreement with the experimental results and it was confirmed by ANN. The deviation lies within ±10% with experimental results compared to ANN for maximum trials. Hence optimized parameters were established and arrived at pH - 7, time - 13 days, agitation - 150 rpm, dosage - 1.5 ?g/L presented 69% removal of TCS. Minimum inhibitory assay of isolated strain was conducted to identify the degradation capacity of TCS and it was found out to be lesser than 0.025 mg of TCS. Later the strain was immobilized in two different matrices. One is biopolymer extracted from cellulose (Water Hyacinth) along with sodium alginate and second is free bacteria with sodium alginate and was made in the form of beads. The removal of TCS by TDB-cellulose-alginate (BCA) and TDB-Alginate (BA) beads were 58% and 30% respectively. Hence it was concluded that BCA beads showed effective removal compared to BA beads. Therefore, isolate can degrade TCS when the concentration ranges from 0.025 mg/L to 5.5 ng/L. © 2018 Elsevier LtdItem Modeling and Optimization of Wear Rate of AISI 2507 Super Duplex Stainless Steel(Springer Netherlands rbk@louisiana.edu, 2019) Davanageri, M.B.; Narendranath, S.; Kadoli, R.The present work attempts to study the parameters influencing wear, namely, applied load, heat-treated temperature, sliding velocity, and sliding distance using statistical Design of Experiments (DOE) and Response Surface Methodology (RSM). The wear behavior of super duplex stainless steel was evaluated under dry sliding conditions. A three-level Central Composite Design (CCD) based non-linear model was used to establish input-output relationship based on the collected experimental input-output data. Surface plots were used to study the influence of applied load, heat-treated temperature, sliding distance, and sliding velocity on the wear rate of super duplex stainless steel. The wear rate was observed to vary nearly non-linearly with applied load and linearly with the rest of the input parameters. Analysis of Variance (ANOVA) was conducted to test the statistical adequacy of the non-linear model developed. Applied load and heat-treated temperature were found to have a more positive contribution towards the wear rate than other parameters. Although the sliding velocity had a negligible effect, its interaction with applied load and heat-treated temperature had a significant impact on the wear rate. The regression equation developed was tested for its prediction precision with the help of 20 test cases. Further, attempts were also made to determine the optimum combination of input parameters that minimize the wear rate using the Desirability Function Approach (DFA). The objective of minimizing the wear rate was met with the highest desirability value of 1. Confirmation experiments were conducted for the determined optimal set of input parameters of 20 test cases resulting in an average absolute percent deviation in prediction of 6.34% and 5.58%. © 2018, Springer Science+Business Media B.V., part of Springer Nature.Item Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process(Springer Nature, 2020) Shettigar, A.K.; Gowdru Chandrashekarappa, G.C.M.; Ganesh, G.R.; Vundavilli, P.R.; Parappagoudar, M.B.Intelligent manufacturing requires significant technological interventions to interface manufacturing processes with computational tools in order to dynamically mold the systems. In this era of the 4th industrial revolution, Artificial neural network (ANNs) is a modern tool equipped with a better learning capability (based on the past experience or history data) and assists in intelligent manufacturing. This research paper reports on ANNs based intelligent modelling of a turning process. The central composite design is used as a data-driven modelling tool and huge input–output is generated to train the neural networks. ANNs are trained with the data collected from the physics-based models by using back-propagation algorithm (BP), genetic algorithm (GA), artificial bee colony (ABC), and BP algorithm trained with self-feedback loop. The ANNs are trained and developed as both forward and reverse mapping models. Forward modelling aims at predicting a set of machining quality characteristics (i.e. surface roughness, cylindricity error, circularity error, and material removal rate) for the known combinations of cutting parameters (i.e. cutting speed, feed rate, depth of cut, and nose radius). Reverse modelling aims at predicting the cutting parameters for the desired machining quality characteristics. The parametric study has been conducted for all the developed neural networks (BPNN, GA-NN, RNN, ABC-NN) to optimize neural network parameters. The performance of neural network models has been tested with the help of ten test cases. The network predicted results are found in-line with the experimental values for both forward and reverse models. The neural network models namely, RNN and ABC-NN have shown better performance in forward and reverse modelling. The forward modelling results could help any novice user for off-line monitoring, that could predict the output without conducting the actual experiments. Reverse modelling prediction would help to dynamically adjust the cutting parameters in CNC machine to obtain the desired machining quality characteristics. © 2020, Springer Nature Switzerland AG.Item Influence of slide burnishing process on the surface characteristics of precipitation hardenable steel(Springer Nature, 2021) Sachin, B.; Rao, C.M.; Naik, G.M.; Puneet, N.P.The surface integrity of the material is the predominant necessity of a component to perform efficiently in varying working conditions. To improve the surface integrity of the workpiece secondary finishing processes are being performed. This work attempts to propose a realistic cryogenic slide burnishing condition for improvement of the surface integrity. The slide burnishing was performed by a novel slide burnishing tool on 17–4 precipitation hardenable stainless steel. The experiment was designed based on a central composite design. Initially, the effect of control parameters on the output response was examined by experimental analysis based on the design of experiment. Analysis of variance was used to analyze the influence of the variables on the performance indices. The regression technique was used to develop an empirical model. Optimization of process parameters for finding minimum surface roughness and maximum surface hardness was achieved by a multi-objective genetic algorithm. The optimized solutions were validated by performing confirmation experiments. © 2021, The Author(s).