Prediction of batch sorption of barium and strontium from saline water
- Reddy, B. S.; Maurya, A. K.; Sathishkumar, V; Narayana, P. L.; Reddy, M. H.; Baazeem, Alaa; Cho, Kwon-Koo; Reddy, N. S.
- Issue Date
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Artificial neural networks; Prediction; Sorption; Sensitivity analysis; Saline water; Petroleum industries
- ENVIRONMENTAL RESEARCH, v.197
- Journal Title
- ENVIRONMENTAL RESEARCH
- Celestite and barite formation results in contamination of barium and strontium ions hinder oilfield water purification. Conversion of bio-waste sorbent products deals with a viable, sustainable and clean remediation approach for removing contaminants. Biochar sorbent produced from rice straw was used to remove barium and strontium ions of saline water from petroleum industries. The removal efficiency depends on biochar amount, pH, contact time, temperature, and Ba/Sr concentration ratio. The interactions and effects of these parameters with removal efficiency are multifaceted and nonlinear. We used an artificial neural network (ANN) model to explore the correlation between process variables and sorption responses. The ANN model is more accurate than that of existing kinetic and isotherm equations in assessing barium and strontium removal with adj. R-2 values of 0.994 and 0.991, respectively. We developed a standalone user interface to estimate the barium and strontium removal as a function of sorption process parameters. Sensitivity analysis and quantitative estimation were carried out to study individual process variables' impact on removal efficiency.
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- 공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles
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