Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process
Side reactions are undesirable in most industrial processes, as they decrease process yield and selectivity. For this reason, mononitrobenzene’s manufacturers set nitrophenols minimization as a critical goal, along with the MNB production targets. The mechanism of these side reactions in benzene nit...
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2013-06-01
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Series: | Chemical Engineering Transactions |
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doaj-0d9a4456e2264f3f8f542fe16d685f252021-02-21T21:13:18ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162013-06-013210.3303/CET1332209Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration ProcessA. NogueiraD. SilvaM. ReisC. BaptistaSide reactions are undesirable in most industrial processes, as they decrease process yield and selectivity. For this reason, mononitrobenzene’s manufacturers set nitrophenols minimization as a critical goal, along with the MNB production targets. The mechanism of these side reactions in benzene nitration is still under debate and, so far, none of the alternatives has achieved general consensus in the scientific community. As an alternative, industrial data may provide valuable information on the contribution of inlet process variables and operating conditions upon the formation of nitrophenolic compounds in the adiabatic nitration process. In this work, Partial Least Squares regression was applied to data collected from a mononitrobenzene industrial production plant. This methodology allowed concluding that nitration temperature and mixed acid volumetric flow rate as the most influential variables in nitrophenols formation. The models developed enable proper estimates of DNP and TNP concentrations in the industrial process, although their explanation power is lower than those previously obtained by Quadros et al. (2005), in a pilot plant, and by Portugal et al. (2009) in their extended models.https://www.cetjournal.it/index.php/cet/article/view/6603 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. Nogueira D. Silva M. Reis C. Baptista |
spellingShingle |
A. Nogueira D. Silva M. Reis C. Baptista Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process Chemical Engineering Transactions |
author_facet |
A. Nogueira D. Silva M. Reis C. Baptista |
author_sort |
A. Nogueira |
title |
Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process |
title_short |
Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process |
title_full |
Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process |
title_fullStr |
Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process |
title_full_unstemmed |
Prediction of the By-products Formation in the Adiabatic Industrial Benzene Nitration Process |
title_sort |
prediction of the by-products formation in the adiabatic industrial benzene nitration process |
publisher |
AIDIC Servizi S.r.l. |
series |
Chemical Engineering Transactions |
issn |
2283-9216 |
publishDate |
2013-06-01 |
description |
Side reactions are undesirable in most industrial processes, as they decrease process yield and selectivity. For this reason, mononitrobenzene’s manufacturers set nitrophenols minimization as a critical goal, along with the MNB production targets. The mechanism of these side reactions in benzene nitration is still under debate and, so far, none of the alternatives has achieved general consensus in the scientific community. As an alternative, industrial data may provide valuable information on the contribution of inlet process variables and operating conditions upon the formation of nitrophenolic compounds in the adiabatic nitration process. In this work, Partial Least Squares regression was applied to data collected from a mononitrobenzene industrial production plant. This methodology allowed concluding that nitration temperature and mixed acid volumetric flow rate as the most influential variables in nitrophenols formation. The models developed enable proper estimates of DNP and TNP concentrations in the industrial process, although their explanation power is lower than those previously obtained by Quadros et al. (2005), in a pilot plant, and by Portugal et al. (2009) in their extended models. |
url |
https://www.cetjournal.it/index.php/cet/article/view/6603 |
work_keys_str_mv |
AT anogueira predictionofthebyproductsformationintheadiabaticindustrialbenzenenitrationprocess AT dsilva predictionofthebyproductsformationintheadiabaticindustrialbenzenenitrationprocess AT mreis predictionofthebyproductsformationintheadiabaticindustrialbenzenenitrationprocess AT cbaptista predictionofthebyproductsformationintheadiabaticindustrialbenzenenitrationprocess |
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