Browsing by Author "Rene, E.R."

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Acetaminophen micropollutant: Historical and current occurrences, toxicity, removal strategies and transformation pathways in different environments
(2019) Phong, Vo, H.N.; Le, G.K.; Hong, Nguyen, T.M.; Bui, X.-T.; Nguyen, K.H.; Rene, E.R.; Vo, T.D.H.; Thanh, Cao, N.-D.; Raj Mohan, Balakrishnan
Acetaminophen (ACT) is commonly used as a counter painkiller and nowadays, it is increasingly present in the natural water environment. Although its concentrations are usually at the ppt to ppm levels, ACT can transform into various intermediates depending on the environmental conditions. Due to the complexity of the ACT degradation products and the intermediates, it poses a major challenge for monitoring, detection and to propose adequate treatment technologies. The main objectives of this review study were to assess (i) the occurrences and toxicities, (2) the removal technologies and (3) the transformation pathways and intermediates of ACT in four environmental compartments namely wastewater, surface water, ground water, and soil/sediments. Based on the review, it was observed that the ACT concentrations in wastewater can reach up to several hundreds of ppb. Amongst the different countries, China and the USA showed the highest ACT concentration in wastewater (?300 ?g/L), with a very high detection frequency (81 100%). Concerning surface water, the ACT concentrations were found to be at the ppt level. Some regions in France, Spain, Germany, Korea, USA, and UK comply with the recommended ACT concentration for drinking water (71 ng/L). Notably, ACT can transform and degrade into various metabolites such as aromatic derivatives or organic acids. Some of them (e.g., hydroquinone and benzoquinone) are toxic to human and other life forms. Thus, in water and wastewater treatment plants, tertiary treatment systems such as advanced oxidation, membrane separation, and hybrid processes should be used to remove the toxic metabolites of ACT. 2019 Elsevier Ltd
Acetaminophen micropollutant: Historical and current occurrences, toxicity, removal strategies and transformation pathways in different environments
(Elsevier Ltd, 2019) Vo, H.N.; Le, G.K.; Nguyen, T.M.; Bui, X.-T.; Nguyen, K.H.; Rene, E.R.; Vo, T.D.H.; Cao Ngoc, N.-D.; Mohan, R.
Acetaminophen (ACT) is commonly used as a counter painkiller and nowadays, it is increasingly present in the natural water environment. Although its concentrations are usually at the ppt to ppm levels, ACT can transform into various intermediates depending on the environmental conditions. Due to the complexity of the ACT degradation products and the intermediates, it poses a major challenge for monitoring, detection and to propose adequate treatment technologies. The main objectives of this review study were to assess (i) the occurrences and toxicities, (2) the removal technologies and (3) the transformation pathways and intermediates of ACT in four environmental compartments namely wastewater, surface water, ground water, and soil/sediments. Based on the review, it was observed that the ACT concentrations in wastewater can reach up to several hundreds of ppb. Amongst the different countries, China and the USA showed the highest ACT concentration in wastewater (?300 ?g/L), with a very high detection frequency (81–100%). Concerning surface water, the ACT concentrations were found to be at the ppt level. Some regions in France, Spain, Germany, Korea, USA, and UK comply with the recommended ACT concentration for drinking water (71 ng/L). Notably, ACT can transform and degrade into various metabolites such as aromatic derivatives or organic acids. Some of them (e.g., hydroquinone and benzoquinone) are toxic to human and other life forms. Thus, in water and wastewater treatment plants, tertiary treatment systems such as advanced oxidation, membrane separation, and hybrid processes should be used to remove the toxic metabolites of ACT. © 2019 Elsevier Ltd
ANN & regresssion analysis based predictions of BOD5 & COD for refinery wastewater
(2007) Rene, E.R.; Saidutta, M.B.
Industrial wastewater quality is indicated by several physico-chemical and biological parameters. If a Suitable correlation is established between them, some difficult and not instantaneously available parameters can be easily predicted. Such correlations are traditionally achieved by regression analysis. However, non-linear fluctuations are not easily represented by these correlations. Models based on artificial neural networks (ANNs) are fast emerging as an alternative tool to predict and forecast water quality parameters based on a well-defined set of training data that are easily available. The present study reports the correlations for BOD5 and COD with TOC for a refinery wastewater. Additionally, 12 ANN based models were developed to forecast the BOD5 and COD by considering other water quality indices as the input data. The results from this study indicate that ANNs are simple and reliable, under adequately trained conditions.
ANN &
(regresssion analysis based predictions of BOD5 &) Rene, E.R.; Saidutta, M.B.
Chemical Engineering World, 2007, Vol.42, 5, pp.-
Energy and environment - An introduction
(Springer Science and Business Media Deutschland GmbH, 2021) Hoang, G.M.; Watsuntorn, W.; Chulalaksananukul, W.; Mohan Balakrishnan, R.M.; Rene, E.R.
[No abstract available]
Experimental and Numerical Study of the Hydrodynamics of a Thin Film Reactor (TFR) for the Decarboxylation of Anacardic Acid
(2018) Shrutee, L.; Van, Geel, T.; Rene, E.R.; Raj Mohan, Balakrishnan; Dutta, A.
A newly designed laboratory scale thin film reactor (TFR) was tested for the decarboxylation of anacardic acid in Cashew Nut Shell Liquid (CNSL) and to investigate the fluid flow behaviour under the influence of temperature since the fluid properties like viscosity and density have strong dependence on temperature. The CNSL containing 60-65 % anacardic acid was decarboxylated to produce cardanol and CO2 at wall temperatures ranging between 393 K and 433 K, respectively. The characteristics of the CNSL, essentially a non-Newtonian fluid, was analysed at different temperatures and its rheological behaviour was studied using the well-known power law model. It was observed that CNSL follows a pseudoplastic behaviour and its viscosity, along with the liquid residence time, was found to decrease till 413 K, while a further increase in temperature resulted in product degradation due to charring, accompanied by an increase in viscosity and residence time. Using measured values for the viscosity, the film thickness was calculated for each wall temperature within the 393-433 K temperature range, showing an increase of the film thickness with temperature and viscosity. Computational Fluid Dynamics (CFD) studies were carried out for the first time for this reactor configuration, using the volume of fluid (VOF) model for the reactive flow. The results obtained from these simulations were in concurrence with the experimental outcomes: velocity profiles along the length of the reactor show its highest values at a wall temperature of 413 K, while lower velocity values were observed when the temperatures were lower or greater than 413 K. 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.
Experimental and Numerical Study of the Hydrodynamics of a Thin Film Reactor (TFR) for the Decarboxylation of Anacardic Acid
(Walter de Gruyter GmbH, 2018) Shrutee, L.; van Geel, T.; Rene, E.R.; Raj Mohan, B.; Dutta, A.
A newly designed laboratory scale thin film reactor (TFR) was tested for the decarboxylation of anacardic acid in Cashew Nut Shell Liquid (CNSL) and to investigate the fluid flow behaviour under the influence of temperature since the fluid properties like viscosity and density have strong dependence on temperature. The CNSL containing 60-65 % anacardic acid was decarboxylated to produce cardanol and CO2 at wall temperatures ranging between 393 K and 433 K, respectively. The characteristics of the CNSL, essentially a non-Newtonian fluid, was analysed at different temperatures and its rheological behaviour was studied using the well-known power law model. It was observed that CNSL follows a pseudoplastic behaviour and its viscosity, along with the liquid residence time, was found to decrease till 413 K, while a further increase in temperature resulted in product degradation due to charring, accompanied by an increase in viscosity and residence time. Using measured values for the viscosity, the film thickness was calculated for each wall temperature within the 393-433 K temperature range, showing an increase of the film thickness with temperature and viscosity. Computational Fluid Dynamics (CFD) studies were carried out for the first time for this reactor configuration, using the volume of fluid (VOF) model for the reactive flow. The results obtained from these simulations were in concurrence with the experimental outcomes: velocity profiles along the length of the reactor show its highest values at a wall temperature of 413 K, while lower velocity values were observed when the temperatures were lower or greater than 413 K. © 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.
Photocatalytic degradation of Irgalite violet dye using nickel ferrite nanoparticles
(2019) Vijay, S.; Raj Mohan, Balakrishnan; Rene, E.R.; Priyanka, U.
Nanotechnologies have prominent applications in the field of science and technology owing to their size-tunable properties providing a promising approach for degradation of various pollutants. In this scenario, the present work aims to study the effect of nickel ferrite nanoparticles on the degradation of Irgalite violet dye by Fenton s reaction using oxalic acid as an oxidizing agent in the presence of sunlight. The effect of pH and adsorbent dosage on the rate of dye degradation was monitored. Based on these studies it was observed that 99% dye degradation was achieved for catalyst dosage of 0.2 g, 400 ppm dye concentration and 2.0 mM oxalic acid at pH 3.0 within 60 min. The studies reveal that the degradation follows pseudo-first-order kinetics and the catalyst reusability remained constant almost for five cycles. Further, nickel ferrite nanoparticles are proven to be an efficient alternative for the removal of dyes from coloured solutions. IWA Publishing 2019
Photocatalytic degradation of Irgalite violet dye using nickel ferrite nanoparticles
(IWA Publishing 12 Caxton Street London SW1H 0QS, 2019) Vijay, S.; Mohan Balakrishnan, R.M.; Rene, E.R.; Uddandarao, P.
Nanotechnologies have prominent applications in the field of science and technology owing to their size-tunable properties providing a promising approach for degradation of various pollutants. In this scenario, the present work aims to study the effect of nickel ferrite nanoparticles on the degradation of Irgalite violet dye by Fenton’s reaction using oxalic acid as an oxidizing agent in the presence of sunlight. The effect of pH and adsorbent dosage on the rate of dye degradation was monitored. Based on these studies it was observed that 99% dye degradation was achieved for catalyst dosage of 0.2 g, 400 ppm dye concentration and 2.0 mM oxalic acid at pH 3.0 within 60 min. The studies reveal that the degradation follows pseudo-first-order kinetics and the catalyst reusability remained constant almost for five cycles. Further, nickel ferrite nanoparticles are proven to be an efficient alternative for the removal of dyes from coloured solutions. © IWA Publishing 2019
Photocatalytic degradation of Irgalite violet dye using nickel ferrite nanoparticles
(IWA Publishing, 2020) Vijay, S.; Mohan Balakrishnan, R.M.; Rene, E.R.; Priyanka, U.
Nanotechnologies have prominent applications in the field of science and technology owing to their size-tunable properties providing a promising approach for degradation of various pollutants. In this scenario, the present work aims to study the effect of nickel ferrite nanoparticles on the degradation of Irgalite violet dye by Fenton’s reaction using oxalic acid as an oxidizing agent in the presence of sunlight. The effect of pH and adsorbent dosage on the rate of dye degradation was monitored. Based on these studies it was observed that 99% dye degradation was achieved for catalyst dosage of 0.2 g, 400 ppm dye concentration and 2.0 mM oxalic acid at pH 3.0 within 60 min. The studies reveal that the degradation follows pseudo-first-order kinetics and the catalyst reusability remained constant almost for five cycles. Further, nickel ferrite nanoparticles are proven to be an efficient alternative for the removal of dyes from coloured solutions. © 2020 IWA Publishing.
Prediction of bod and cod of a refinery wastewater using multilayer artificial neural networks
(2008) Rene, E.R.; Saidutta, M.B.
In the recent past, artificial neural networks (ANNs) have shown the ability to learn and capture non-linear static or dynamic behaviour among variables based on the given set of data. Since the knowledge of internal procedure is not necessary, the modelling can take place with minimum previous knowledge about the process through proper training of the network. In the present study, 12 ANN based models were proposed to predict the Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) concentrations of wastewater generated from the effluent treatment plant of a petrochemical industry. By employing the standard back error propagation (BEP) algorithm, the network was trained with 103 data points for water quality indices such as Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Phenol concentration, Ammoniacal Nitrogen (AMN), Total Organic Carbon (TOC) and Kjeldahl's Nitrogen (KJN) to predict BOD and COD. After appropriate training, the network was tested with a separate test data and the best model was chosen based on the sum square error (training) and percentage average relative error (% ARE for testing). The results from this study reveal that ANNs can be accurate and efficacious in predicting unknown concentrations of water quality parameters through its versatile training process. 2008 Journal of Urban and Environmental Engineering (JUEE). All rights reserved.
Prediction of bod and cod of a refinery wastewater using multilayer artificial neural networks
(Universidade Federal da Paraiba, 2008) Rene, E.R.; Saidutta, M.B.
In the recent past, artificial neural networks (ANNs) have shown the ability to learn and capture non-linear static or dynamic behaviour among variables based on the given set of data. Since the knowledge of internal procedure is not necessary, the modelling can take place with minimum previous knowledge about the process through proper training of the network. In the present study, 12 ANN based models were proposed to predict the Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) concentrations of wastewater generated from the effluent treatment plant of a petrochemical industry. By employing the standard back error propagation (BEP) algorithm, the network was trained with 103 data points for water quality indices such as Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Phenol concentration, Ammoniacal Nitrogen (AMN), Total Organic Carbon (TOC) and Kjeldahl's Nitrogen (KJN) to predict BOD and COD. After appropriate training, the network was tested with a separate test data and the best model was chosen based on the sum square error (training) and percentage average relative error (% ARE for testing). The results from this study reveal that ANNs can be accurate and efficacious in predicting unknown concentrations of water quality parameters through its versatile training process. © 2008 Journal of Urban and Environmental Engineering (JUEE). All rights reserved.
Prediction of water quality indices by regression analysis and artificial neural networks
(2008) Rene, E.R.; Saidutta, M.B.
The quality of wastewater generated in any process industry is generally indicated by performance indices namely BOD, COD and TOC, expressed in mg/L. The use of TOC as an analytical parameter has become more cornmon in recent years especially for the treatment of industrial wastewater. In this study, several empirical relationships were established between BOD and COD with TOC using regression analysis, so that TOC can be used to estimate the accompanying BOD or COD. A new, the use of Artificial Neural Networks has been explored in this study to predict the concentrations of BOD and COD, well in advance using some easily measurable water quality indices. The total data points obtained from a refinery wastewater (143) were divided into a training set consisting of 103 data points, while the remaining 40 were used as the test data. A total of 12 different models (Al-A12) were tested using different combinations of network architecture. These models were evaluated using the % Average Relative Error values of the test set. It was observed that three models gave accurate and reliable results, indicating the versatility of the developed models.
Prediction of water quality indices by regression analysis and artificial neural networks
(2008) Rene, E.R.; Saidutta, M.B.
The quality of wastewater generated in any process industry is generally indicated by performance indices namely BOD, COD and TOC, expressed in mg/L. The use of TOC as an analytical parameter has become more cornmon in recent years especially for the treatment of industrial wastewater. In this study, several empirical relationships were established between BOD and COD with TOC using regression analysis, so that TOC can be used to estimate the accompanying BOD or COD. A new, the use of Artificial Neural Networks has been explored in this study to predict the concentrations of BOD and COD, well in advance using some easily measurable water quality indices. The total data points obtained from a refinery wastewater (143) were divided into a training set consisting of 103 data points, while the remaining 40 were used as the test data. A total of 12 different models (Al-A12) were tested using different combinations of network architecture. These models were evaluated using the % Average Relative Error values of the test set. It was observed that three models gave accurate and reliable results, indicating the versatility of the developed models.
Solar assisted photocatalytic degradation of organic pollutants in the presence of biogenic fluorescent ZnS nanocolloids
(2019) Uddandarao, P.; Hingnekar, T.A.; Raj Mohan, Balakrishnan; Rene, E.R.
The main aim of this study was to ascertain the photocatalytic degradation of organic pollutants present in aqueous phase using fluorescent biogenic ZnS nanocolloids produced from an endophytic fungus Aspergillus flavus. The degradation studies were carried out using different organic pollutants such as methyl violet (MV), 2,4-dichlorophenoxyacetic acid (2,4-D) and paracetamol (PARA) for 120 min, 270 min and 240 min, respectively, at pH varying from 3.0 to 11.0. The results from this study indicate that the degradation efficiency of ZnS nanocolloids for MV, 2,4-D and PARA were 87%, 33% and 51%, respectively, at the optimum concentration of 100 mg/L of the tested organic pollutants. At different time intervals, the samples were analyzed for their chemical oxygen demand (COD) and total organic carbon (TOC) contents. The reduction of COD and TOC were 78% and 74% for MV at 120 min; 55.5% and 57.2% for 2,4-D at 270 min and 47.6% and 44.5% for PARA at 240 min, respectively. The degradation pathway was determined based on the mass spectrum and the intermediates formed; in addition, the interaction between organic pollutants and nanocolloids was also elucidated based on atomic force microscopy (AFM) and fluorescence spectrum. 2019 Elsevier Ltd
Solar assisted photocatalytic degradation of organic pollutants in the presence of biogenic fluorescent ZnS nanocolloids
(Elsevier Ltd, 2019) Uddandarao, P.; Hingnekar, T.A.; Mohan Balakrishnan, R.M.; Rene, E.R.
The main aim of this study was to ascertain the photocatalytic degradation of organic pollutants present in aqueous phase using fluorescent biogenic ZnS nanocolloids produced from an endophytic fungus Aspergillus flavus. The degradation studies were carried out using different organic pollutants such as methyl violet (MV), 2,4-dichlorophenoxyacetic acid (2,4-D) and paracetamol (PARA) for 120 min, 270 min and 240 min, respectively, at pH varying from 3.0 to 11.0. The results from this study indicate that the degradation efficiency of ZnS nanocolloids for MV, 2,4-D and PARA were 87%, 33% and 51%, respectively, at the optimum concentration of 100 mg/L of the tested organic pollutants. At different time intervals, the samples were analyzed for their chemical oxygen demand (COD) and total organic carbon (TOC) contents. The reduction of COD and TOC were 78% and 74% for MV at 120 min; 55.5% and 57.2% for 2,4-D at 270 min and 47.6% and 44.5% for PARA at 240 min, respectively. The degradation pathway was determined based on the mass spectrum and the intermediates formed; in addition, the interaction between organic pollutants and nanocolloids was also elucidated based on atomic force microscopy (AFM) and fluorescence spectrum. © 2019 Elsevier Ltd