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

Abstract

A major difficulty in operating biorefinery processes is the large feedstock variability. A systematic multivariate analysis (sMVA) strategy for improved process

Tropical agroindustrial biowaste revalorization through integrative biorefineries—review part I: coffee and palm oil by-products

“…Nevertheless, there are still many challenges that should be addressed to optimize and integrate a full process in the oil palm industry. These obstacles have been thoroughly described by others [85][86][87][88][89].…”

Iterative lumping approach for representing lipid feedstocks in fatty acid distillation simulation and optimization

“…A selection of important process parameters was chosen based on a previously performed multivariate data analysis, which investigated the effect of the variability in process parameters on product quality in FA distillation. 1 A list of the investigated process parameters is given in Table 3. The sensitivity of the process indicators yield, purity and total heat duty are determined for a −10 to +10% change of these selected independent variables, except if not possible due to process constraints.…”

“…Hydrolysis and distillation are the two key operations in the production of renewables‐based FAs. Important considerations for FA distillation are a high purity, resulting in low color values and a good stability (storage and heat) of the distilled FAs, a high yield, and a reduced steam consumption 1 . This motivates a holistic model‐based optimization approach of FA distillation that takes into account three process indicators: yield and purity of the product fraction and heat duty of the process.…”

“…In addition, the process model is used to validate if simulated purity can be used as a proxy for color quality of the distilled FAs. In a previous study, multivariate data analysis was used to identify key process parameters influencing color quality 1 . Data from this study, investigating the effect of side reflux ratio on the color and heat stability of distilled FAs, were used to validate if model purity follows the same trend as FA color and heat stability.…”

“…However, a specific challenge in oleochemical processes, especially for those implemented in Western Europe, is the pronounced feedstock variability, resulting in differences in composition and quality of the product. 1 Model predictive control could be used to tackle this challenge of feedstock diversity by changing process conditions for compositional variability of the incoming feedstock. 16 However, using only one key compound for all FAs in a predictive model would not allow diversifying between feedstocks.…”