Prediction of Prostate Cancer using Machine Learning Algorithms
Muktevi Srivenkatesh
Dr. M, Srivenkatesh, Associate Professor, Department of Computer Science, GITAM Deemed to be University, Visakhapatnam, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 5353-5362 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6754018520/2020©BEIESP | DOI: 10.35940/ijrte.E6754.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Reactive extraction is a sophisticated separation technique used for the recovery of carboxylic acids from fermentation broth. Levulinic acid is a versatile chemical. A right combination of extractant and diluent will provide a high yield. The reactive extraction of levulinic acid from aqueous solution with tri-n-octylamine (TOA) dissolved in 1-octanol was investigated at room temperature. The effect of pH was studied. From the physical and chemical equilibrium experimental results, the distribution coefficient (KD), extraction efficiency (E%), loading ratio (Z), stoichiometric loading factor (ZS) and modified separation factor (Sf) are calculated. It was found that physical extraction provided less yield compared to chemical extraction. A maximum KD was obtained as 5.248 using 40% TOA (0.9059 mol/L) while 83.99 % of the levulinic acid was extracted. By increasing the initial concentration of levulinic acid increased the concentration of levulinic acid in both the organic phase and aqueous phase. As the concentration of TOA increases from 10 to 40 % (0.2264 mol/L to 0.9059 mol/L), the distribution coefficient and extraction efficiency also increase. By increasing the pH from 3 to 7, the distribution coefficient and extraction efficiency were drastically affected.
Keywords: Reactive Extraction, Levulinic Acid, Tri-n-Octylamine, 1-Octanol, pH.
Scope of the Article: Machine Learning.