Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

Waste streams with high ammonia nitrogen (NH<sub>3</sub>-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH<sub>3</sub>-N from wastewater is therefore of utmost importance to allevi...

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Autores principales:	Arif Reza, Lide Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ammonia removal and recovery response surface methodology artificial neural network Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/54be87aa9eed4306b7d36fe1a9c230b5
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spelling	oai:doaj.org-article:54be87aa9eed4306b7d36fe1a9c230b52021-11-25T18:51:52ZOptimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping10.3390/pr91120592227-9717https://doaj.org/article/54be87aa9eed4306b7d36fe1a9c230b52021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2059https://doaj.org/toc/2227-9717Waste streams with high ammonia nitrogen (NH<sub>3</sub>-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH<sub>3</sub>-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH<sub>3</sub>-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH<sub>3</sub>-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH<sub>3</sub>-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH<sub>3</sub>-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH<sub>3</sub>-N from wastewater under different operating conditions.Arif RezaLide ChenMDPI AGarticleammonia removal and recoveryresponse surface methodologyartificial neural networkChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2059, p 2059 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ammonia removal and recovery response surface methodology artificial neural network Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	ammonia removal and recovery response surface methodology artificial neural network Chemical technology TP1-1185 Chemistry QD1-999 Arif Reza Lide Chen Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping
description	Waste streams with high ammonia nitrogen (NH<sub>3</sub>-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH<sub>3</sub>-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH<sub>3</sub>-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH<sub>3</sub>-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH<sub>3</sub>-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH<sub>3</sub>-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH<sub>3</sub>-N from wastewater under different operating conditions.
format	article
author	Arif Reza Lide Chen
author_facet	Arif Reza Lide Chen
author_sort	Arif Reza
title	Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping
title_short	Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping
title_full	Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping
title_fullStr	Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping
title_full_unstemmed	Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping
title_sort	optimization and modeling of ammonia nitrogen removal from high strength synthetic wastewater using vacuum thermal stripping
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/54be87aa9eed4306b7d36fe1a9c230b5
work_keys_str_mv	AT arifreza optimizationandmodelingofammonianitrogenremovalfromhighstrengthsyntheticwastewaterusingvacuumthermalstripping AT lidechen optimizationandmodelingofammonianitrogenremovalfromhighstrengthsyntheticwastewaterusingvacuumthermalstripping
_version_	1718410586624098304

Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

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