Distribution models for nitrophenols in a liquid-liquid system

•Experimental data for liquid-liquid equilibrium distribution of nitrophenols (DNP and TNP).•Statistical predictive models for distribution ratio based on operating conditions.•Distribution ratio of each nitrophenol between the phases differed (DDNP≈2DTNP). The formation of nitrophenols by-products...

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Bibliographic Details
Published in:Chemical engineering science 2018-11, Vol.189, p.266-276
Main Authors: Lopes, A.L.C.V., Ribeiro, A.F.G., Reis, M.P.S., Silva, D.C.M., Portugal, I., Baptista, C.M.S.G.
Format: Article
Language:English
Subjects:
Benzene nitration
Distribution ratio
Multivariate linear regression
Nitrophenols
Predictive models
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Summary:•Experimental data for liquid-liquid equilibrium distribution of nitrophenols (DNP and TNP).•Statistical predictive models for distribution ratio based on operating conditions.•Distribution ratio of each nitrophenol between the phases differed (DDNP≈2DTNP). 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 (Dj,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 Dj 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 wj,eA. Subsequently two independent data sets were built for development and external validation of prediction multivariate linear regression (MLR) models, at 30°C. The fitting results (R2 and Rad2⩾0.90) and the prediction results (Rpred,DNP2=0.931,Rpred,TNP2=0.908) confirmed the quality of the wj,eAmodels. Statistical significant predictive MLR models were also developed for Dj(which is related with wj,eA), at 30°C, with DNP evidencing a higher affinity for the organic phase (i.e. DDNP≈2DTNP).
ISSN:0009-2509
1873-4405
DOI:10.1016/j.ces.2018.04.056