Monitoramento do processo de produção de biodiesel via transesterificação utilizando resolução multivariada de curvas com mínimos quadrados alternados (MCR-ALS)

Abstract

The monitoring of biodiesel production is still performed by methods that require high reagent consumption and a long response time, making difficult to control, quickly and effectively, the process variables. To overcome this problem, this work proposed models using Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) and FTIR-ATR to determine methyl esters, diglycerides, triglycerides, fatty acids and monoglycerides concentrations along the transesterification reaction. The models were built using spectral data obtained by mid infrared spectroscopy for reactions in different experimental conditions. The variation of the experimental conditions followed a factorial design 22 with center point, whose variables were the batch of soybean oil and the alcohol: oil molar ratio (between 5:1 and 7: 1). The multivariate calibration models applied the non-negativity and correlation constraints, inserting the data obtained by high performance liquid chromatography (HPLC) as reference values. After meeting the convergence criterion the spectral profiles recovered for the methyl esters and for the triglycerides have shown high correlation when compared with the reference spectra (> 0.985). In addition, the spectra calculated for fatty acids, monoglycerides and diglycerides showed absorption bands characteristic of functional groups present in these molecules, mainly the hydroxyl and carbonyl stretch bands. The root mean square errors of calibration and prediction obtained for the triglycerides and for the methyl esters were within 1.91 to 6.33 %. Thus, the monitoring of biodiesel production, using the multivariate curve resolution method and the correlation constraint, provided important information about the process, faster and at a lower cost than the conventional methodologies.