Optimisation of reaction parameters for a novel polymeric additives as flow improvers of crude oil using response surface methodology
In recent years, polymeric additives have received considerable attention as a wax control approach to enhance the flowability of waxy crude oil. Furthermore, the satisfactory model for predicting maximum yield in free radical polymerisation has been challenging due to the complexity and rigours of...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32851/1/Optimisation%20of%20reaction%20parameters%20for%20a%20novel%20polymeric%20additives%20as%20flow%20improvers.pdf http://umpir.ump.edu.my/id/eprint/32851/ https://doi.org/10.1007/s13202-021-01349-1 https://doi.org/10.1007/s13202-021-01349-1 |
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| Summary: | In recent years, polymeric additives have received considerable attention as a wax control approach to enhance the flowability of waxy crude oil. Furthermore, the satisfactory model for predicting maximum yield in free radical polymerisation has been challenging due to the complexity and rigours of classic kinetic models. This study investigated the influence of operating parameters on a novel synthesised polymer used as a wax deposition inhibitor in a crude oil pipeline. Response surface methodology (RSM) was used to develop a polynomial regression model and investigate the effect of reaction temperature, reaction time, and initiator concentration on the polymerisation yield of behenyl acrylate-co-stearyl methacrylate-co-maleic anhydride (BA-co-SMA-co-MA) polymer by using central composite design (CCD) approach. The modelled optimisation conditions were reaction time of 8.1 h, reaction temperature of 102 °C, and initiator concentration of 1.57 wt%, with the corresponding yield of 93.75%. The regression model analysis (ANOVA) detected an R2 value of 0.9696, indicating that the model can clarify 96.96% of the variation in data variation and does not clarify only 3% of the total differences. Three experimental validation runs were carried out using the optimal conditions, and the highest average yield is 93.20%. An error of about 0.55% was observed compared with the expected value. Therefore, the proposed model is reliable and can predict yield response accurately. Furthermore, the regression model is highly significant, indicating a strong agreement between the expected and experimental values of BA-co-SMA-co-MA yield. Consequently, this study’s findings can help provide a robust model for predicting maximum polymerisation yield to reduce the cost and processing time associated with the polymerisation process. |
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