Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 29, Issue 1, Pages 1482-1490Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-15785-3
Keywords
Wastewater pollution; Wastewater monitoring; Stripped sour water; Phenols; Projection to latent structures regression; Online prediction
Categories
Funding
- Associated Laboratory for Sustainable Chemistry-Clean Processes and Technologies-LAQV
- CICECO-Aveiro Institute of Materials
- Portuguese national funds from Fundacao para a Ciencia e Tecnologia (Portugal) [UIDB/50006/2020, UIDP/50011/2020]
- ERDF [POCI-01-0145-FEDER-007265]
- Galp and Fundacao para a Ciencia e Tecnologia (Portugal) [PD/BDE/128604/2017]
- Doctoral Program in Refining, Petrochemical and Chemical Engineering (EngIQ)
- Fundacao para a Ciencia e Tecnologia (Portugal) [IF/00758/2015]
- Fundação para a Ciência e a Tecnologia [UIDP/50011/2020, PD/BDE/128604/2017] Funding Source: FCT
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The study focused on identifying the wastewater stream with the highest phenolic impact at a large oil refinery. Through multivariate data analysis and development of predictive models, correlations between process parameters and phenols content in wastewater were established to improve wastewater management system. An optimised system leads to improvement in wastewater quality and reduction in pollutant discharge costs.
The present study focused on the methodology for identification of the wastewater stream that presents the highest phenolic impact at a large oil refinery. As a case-study, the oil refinery, Petrogal S.A., in Sines, Portugal, was selected. Firstly, stripped sour water from the cracking complex was identified as the most relevant wastewater stream concerning phenolic emission. Secondly, multivariate data analysis was used, through projection to latent structures (PLS) regression, to find existing correlations between process parameters and phenols content in stripped sour water. The models developed allowed the prediction of phenols concentration with predictive errors down to 20.16 mg/L (corresponding to 8.2% average error), depending on the complexity of the correlation used, and R-2 values as high as 0.85. Models were based in input parameters related to fluid catalytic crackers (FCC) feedstock quality, crudemix and steam injected in the catalyst stripper. The studied data analysis approach showed to be useful as a tool to predict the phenolic content in stripped sour water. Such prediction would help improve the wastewater management system, especially the units responsible for phenol degradation. The methodology shown in this work can be used in other refineries containing catalytic cracking complexes, providing a tool which allows the online prediction of phenols in stripped sour water and the identification of the most relevant process parameters. An optimised system at any refinery leads to an improvement in the wastewater quality and costs associated with pollutant discharge; thus, the development of monitoring online tools, as proposed in this work, is essential.
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