Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is compulsory.Thus,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality.The present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor set-up.To this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are investigated.The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield.The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression.The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of 0.9608.Thus,we applied a PAT approach to generate further insight into this established industrial process.

Prediction of the biogas production using GA and ACO input features selection method for ANN model

This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate.The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany.In this context,the concentration of volatile fatty acids,total solids,volatile solids acid detergent fibre,acid detergent lignin,neutral detergent fibre,ammonium nitrogen,hydraulic retention time,and organic loading rate were used.Artificial neural networks(ANN)were established to predict the biogas production rate.An ant colony optimization and genetic algorithms were implemented to perform the variable selection.They identified the significant process variables,reduced the model dimension and improved the prediction capacity of the ANN models.The best prediction of the biogas production rate was obtained with an error of prediction of 6.24%and a coefficient of determination of R2=0.9.