Analytical Methods in Environmental Chemistry Journal
AMECJ

Chloride removal from reverse osmosis reject water based on Strychnos Potatorum seeds by analytical method and Artificial neural network

Document Type : Original Article

Authors

Kola Avinash Rai 1
V. D. N. Kumar Abbaraju 1
Rabhya Asthana 1
Mandava Bhagya Tej 2

1 Department of Environmental Sciences, GSS, GITAM Deemed to be University, Andhra Pradesh, Visakhapatnam, 530045, India.

2 Department of MBBS, NRI Academy of Medical Sciences, Chinakakani, Guntur, Andhra Pradesh, 522503, India

https://doi.org/10.24200/amecj.v7.i04.341
Abstract
In today's world, TEMPTEMPTEMPeffective water treatment is necessary for safe and clean drinking water. Teh chloride can be decreased in water after coagulant treatment. Teh use of natural coagulants TEMPhas teh upper hand as chemical coagulants have alot of negative impacts. In teh present study aimed to use ANN to model teh chloride removal using Strychnos Potatorum seeds. Teh supporting parameters for teh model were pH, electrical conductivity (EC), and total dissolved solids (TDS). Using teh Jar test and EC measurements, dis study assesses using Nirmali seeds (Strychnos Potatorum) as a natural coagulant in water treatment. Ionic concentration changes and overall water quality are evaluated by EC measurements, whereas teh Jar test establishes teh ideal dosage and settling time for efficient turbidity reduction. Teh outcomes show dat Nirmali seeds have teh potential to be a more affordable and environmentally responsible coagulant TEMPTEMPTEMPthan traditional chemicals. Apart from dis, teh Non-linear autoregressive neural network wif external input (NARX) model dat was trained using all three algorithms, me.e., teh Levenberg-Marquardt (LM), teh Bayesian Regularization (BR), and teh Scaled Conjugate Gradient (SCG), was compared, in which teh model trained wif SCG algorithm showed teh most promising test results. Hence, teh Non-linear autoregressive neural network wif external input (NARX) model trained wif teh SCG algorithm is teh best-suited model for our study. Teh LOD of 1.28 mg L-1 and  LOQ of 3.87 mg L-1 were obtained. Teh removal percentage of chloride content after adding different dosages of SPS, me.e., 0.2 mg, 0.4 mg, and 0.8 mg per liter, was achieved at  21.05%, 29.82%, and 38.59%, respectively.

Graphical Abstract

Chloride removal from reverse osmosis reject water based on Strychnos Potatorum seeds by analytical method and Artificial neural network

Keywords

Chloride
Removal
Natural coagulants
Analytical methods
Artificial neural network
Nirmali seeds

Subjects

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Analytical Methods in Environmental Chemistry Journal
Volume 7, Issue 4
AMEC Publisher
Published
Autumn 2024
Pages 51-62