10.1021/ie5010898.s001
Mingsen Liao
Mingsen
Liao
Yuehong Zhao
Yuehong
Zhao
Pengge Ning
Pengge
Ning
Hongbin Cao
Hongbin
Cao
Hao Wen
Hao
Wen
Yi Zhang
Yi
Zhang
Optimal
Design of Solvent Blend and Its Application
in Coking Wastewater Treatment Process
American Chemical Society
2014
NLP model
case study
blend exhibits
Optimal Design
MIBK concentration
MIBK loss
nonlinear programming
coking wastewater treatment
candidate diluters
makeup cost
distribution coefficient
extraction performance
molar fraction
Coking Wastewater Treatment ProcessOne
tar removal
phenol
2014-10-01 00:00:00
Journal contribution
https://acs.figshare.com/articles/journal_contribution/Optimal_Design_of_Solvent_Blend_and_Its_Application_in_Coking_Wastewater_Treatment_Process/2250724
One
of the key steps of coking wastewater treatment is phenolic
and tar removal via extraction. However, the high loss of the extractant,
i.e., methyl isobutyl ketone (MIBK), leads to the high cost of the
process. The adoption of a novel solvent or solvent blend is considered
as an efficient way to address this problem. In this paper, seven
solvents (benzene, toluene, m-xylene, ethylbenzene, 1, 3, 5-trimethylbenze,
cyclohexane, and octanol), selected as candidate diluters for MIBK
according to operating requirements, are studied with a nonlinear
programming (NLP) model based on ideal counter-current extraction.
The results, verified with experiments, suggest toluene is the most
promising candidate. Further investigation of this solvent blend reveals
that both <i>D</i><sub>blend</sub> (the distribution coefficient
of phenol between solvent blend and water) and <i>m</i><sub>MIBK</sub> (the MIBK concentration in raffinate) increase with <i>x</i><sub>MIBK</sub> (the molar fraction of MIBK in blend).
The trade-off between the extraction performance and MIBK loss recommends
the blend with <i>x</i><sub>MIBK</sub> = 0.05 as extractant
for coking wastewater treatment. An industrial process consisting
of extraction, back stripping, distillation, and mixer is presented.
A corresponding NLP model is established for its operating optimization.
To improve the accuracy, the representatives of typical phenolics
and tar in wastewater (2,4 dimethyl phenol, m-xylene, and quinolone)
are also considered in addition to phenol. The case study indicates
that the blend exhibits economic advantage over pure MIBK with a makeup
cost of 11.15 ¥/t, much less than the 185.15 ¥/t in the
case of MIBK.