Distribution models for nitrophenols in a liquid-liquid system
authors Lopes, ALCV; Ribeiro, AFG; Reis, MPS; Silva, DCM; Portugal, I; Baptista, CMSG
nationality International
journal CHEMICAL ENGINEERING SCIENCE
author keywords Benzene nitration; Distribution ratio; Multivariate linear regression; Nitrophenols; Predictive models
keywords INDUSTRIAL BENZENE NITRATION; WATER PARTITION-COEFFICIENTS; ORGANIC-COMPOUNDS; SULFURIC-ACID; MASS-TRANSFER; MIXED ACID; NITROBENZENE; MICROREACTOR; PREDICTION
abstract The formation of nitrophenols by-products is still of major concern for the economics and environmental impact of the industrial process of benzene (Bz) nitration to mononitrobenzene (MNB) with mixed acid (sulphuric and nitric acids). The knowledge of nitrophenol (NP) distribution ratios in the liquid-liquid mixture (D-j, j = {NP}) is desirable for process optimization and for understanding the reaction mechanisms behind nitrophenols formation. In this study, a data-driven approach was implemented to provide prediction models for D-j of 2,4-dinitrophenol (DNP) and of 2,4,6-trinitrophenol (TNP) in a biphasic liquid system with a composition representative of the industrial processes. In the first step, screening tests were performed to identify the main variables influencing the experimental equilibrium weight fractions of nitrophenols in the aqueous phase (w(j,e)(A)). Subsequently two independent data sets were built for development and external validation of prediction multivariate linear regression (MLR) models, at 30 degrees C. The fitting results (R-2 and R-ad(2) >= 0.90) and the prediction results (R-pred,DNP(2) = 0.931, R-pred,TNP(2) = 0.908) confirmed the quality of the w(j,e)(A) models. Statistical significant predictive MLR models were also developed for D-j (which is related with w(j,e)(A)), at 30 degrees C, with DNP evidencing a higher affinity for the organic phase (i.e. D-DNP approximate to 2D(TNP)). (C) 2018 Elsevier Ltd. All rights reserved.
publisher PERGAMON-ELSEVIER SCIENCE LTD
issn 0009-2509
year published 2018
volume 189
beginning page 266
ending page 276
digital object identifier (doi) 10.1016/j.ces.2018.04.056
web of science category Engineering, Chemical
subject category Engineering
unique article identifier WOS:000437974700022
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journal analysis (jcr 2019):
journal impact factor 3.871
5 year journal impact factor 3.78
category normalized journal impact factor percentile 75.874
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