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Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production

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Abstract
A major difficulty in operating biorefinery processes is the large feedstock variability. A systematic multivariate analysis (sMVA) strategy for improved process understanding of industrial biorefinery processes is proposed to support identification of effects of feedstock and process variability on product quality. This sMVA strategy comprises nine steps categorized in data set organization, exploratory analysis, and regression. Different MVA techniques are used, such as principal component analysis (PCA) and partial least squares regression (PLS). As a case study, two main operations in fatty acid production are investigated: oil hydrolysis and fatty acid distillation. Key feedstock properties and process parameters affecting the product properties were identified for both operations. For fatty acid production, product quality largely depends on the type of fat or oil used, such as canola or palm oil, due to the large difference in composition and quality between the oil types. However, if a single oil type is used, the variability in product quality does not always critically depend on the variability in feedstock properties. For both operations, flow rate variations, mainly caused by planning issues, were identified as a main cause. In oil hydrolysis, the feed flow rate influences the residence time and thereby directly influences the hydrolysis and side reactions. In fatty acid distillation, a better control is required of the middle reflux ratio in relation to this changing flow rate. The case study showed that applying our proposed sMVA strategy improves the understanding of a biorefinery process by identifying critical sources of variability, which allows more targeted decisions for optimization and control.

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MLA
Nachtergaele, Pieter, et al. “Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production.” Industrial & Engineering Chemistry Research, vol. 59, no. 16, American Chemical Society, 2020, pp. 7732–45.
APA
Nachtergaele, P., De Meester, S., Thybaut, J., Drijvers, D., Saeys, W., & Dewulf, J. (2020). Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production. Industrial & Engineering Chemistry Research, 59(16), 7732–7745.
Chicago author-date
Nachtergaele, Pieter, Steven De Meester, Joris Thybaut, David Drijvers, Wouter Saeys, and Jo Dewulf. 2020. “Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production.” Industrial & Engineering Chemistry Research 59 (16): 7732–45.
Chicago author-date (all authors)
Nachtergaele, Pieter, Steven De Meester, Joris Thybaut, David Drijvers, Wouter Saeys, and Jo Dewulf. 2020. “Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production.” Industrial & Engineering Chemistry Research 59 (16): 7732–7745.
Vancouver
1.
Nachtergaele P, De Meester S, Thybaut J, Drijvers D, Saeys W, Dewulf J. Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production. Industrial & Engineering Chemistry Research. 2020;59(16):7732–45.
IEEE
[1]
P. Nachtergaele, S. De Meester, J. Thybaut, D. Drijvers, W. Saeys, and J. Dewulf, “Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production,” Industrial & Engineering Chemistry Research, vol. 59, no. 16, pp. 7732–7745, 2020.
@article{8663128,
  abstract     = {A major difficulty in operating biorefinery processes is the large feedstock variability. A systematic multivariate analysis (sMVA) strategy for improved process understanding of industrial biorefinery processes is proposed to support identification of effects of feedstock and process variability on product quality. This sMVA strategy comprises nine steps categorized in data set organization, exploratory analysis, and regression. Different MVA techniques are used, such as principal component analysis (PCA) and partial least squares regression (PLS). As a case study, two main operations in fatty acid production are investigated: oil hydrolysis and fatty acid distillation. Key feedstock properties and process parameters affecting the product properties were identified for both operations. For fatty acid production, product quality largely depends on the type of fat or oil used, such as canola or palm oil, due to the large difference in composition and quality between the oil types. However, if a single oil type is used, the variability in product quality does not always critically depend on the variability in feedstock properties. For both operations, flow rate variations, mainly caused by planning issues, were identified as a main cause. In oil hydrolysis, the feed flow rate influences the residence time and thereby directly influences the hydrolysis and side reactions. In fatty acid distillation, a better control is required of the middle reflux ratio in relation to this changing flow rate. The case study showed that applying our proposed sMVA strategy improves the understanding of a biorefinery process by identifying critical sources of variability, which allows more targeted decisions for optimization and control.},
  author       = {Nachtergaele, Pieter and De Meester, Steven and Thybaut, Joris and Drijvers, David and Saeys, Wouter and Dewulf, Jo},
  journal      = {Industrial & Engineering Chemistry Research},
  language     = {eng},
  number       = {16},
  pages        = {7732--7745},
  publisher    = {American Chemical Society},
  title        = {Multivariate Analysis of Industrial Biorefinery Processes: Strategy for Improved Process Understanding with Case Studies in Fatty Acid Production},
  url          = {http://dx.doi.org/10.1021/acs.iecr.0c00515},
  volume       = {59},
  year         = {2020},
}

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