Pedro Felizardo

Multivariate near infrared spectroscopy models for predicting the methyl esters content in biodiesel.

Biodiesel is the main alternative to fossil diesel. The key advantages of its use are the fact that it is a non-toxic renewable resource, which leads to lower emissions of polluting gases. European governments are targeting the incorporation of 20% of biofuels in the general fuels until 2020. Chemically, biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils/fats react with an alcohol, in the presence of a catalyst. The European Standard (EN 14214) establishes 25 parameters that have to be analysed to certify biodiesel quality and the analytical methods that should be used to determine those properties. This work reports the use of near infrared (NIR) spectroscopy to determine the esters content in biodiesel as well as the content in linolenic acid methyl esters (C18:3) in industrial and laboratory-scale biodiesel samples. Furthermore, calibration models for myristic (C14:0), palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2) acid methyl esters were also obtained. Principal component analysis was used for the qualitative analysis of the spectra, while partial least squares regression was used to develop the calibration models between analytical and spectral data. The results confirm that NIR spectroscopy, in combination with multivariate calibration, is a promising technique to assess the biodiesel quality control in both laboratory-scale and industrial scale samples.

Multivariate near infrared spectroscopy models for predicting the iodine value, CFPP, kinematic viscosity at 40 °C and density at 15 °C of biodiesel

Biodiesel is one of the main alternatives to fossil diesel. It is a non-toxic renewable resource, which leads to lower emissions of polluting gases. In fact, European governments are targeting the incorporation of 20% of biofuels in the fossil fuels until 2020. Chemically, biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol, in the presence of a catalyst. The European Standard (EN 14214) establishes 25 parameters that have to be analysed to certify biodiesel quality and the analytical methods that should be used to determine those properties. This work reports the use of near infrared (NIR) spectroscopy to determine some important biodiesel properties: the iodine value, the cold filter plugging point, the kinematic viscosity at 40 degrees C and the density at 15 degrees C. Principal component analysis was used to perform a qualitative analysis of the spectra and partial least squares regression to develop the calibration models between analytical and spectral data. The results support that NIR spectroscopy, in combination with multivariate calibration, is a promising technique applied to biodiesel quality control, in both laboratory and industrial-scale samples.

Monitoring biodiesel fuel quality by near infrared spectroscopy

Biodiesel is produced mainly by a transesterification reaction which involves the reaction of vegetable oils, animal fats or waste oils with an alcohol (such as methanol) in the presence of a catalyst (such as sodium hydroxide or methoxide). Since the presence of contaminants can cause severe engine problems, the assessment of the biodiesel quality is very important. This work reports the use of near infrared (NIR) spectroscopy to determine the content of water and methanol in industrial and laboratory-scale biodiesel samples. A qualitative analysis of the spectra by principal components analysis was carried out and partial least squares regression was used to develop calibration models between spectral and analytical data. The results indicate that the use of NIR spectroscopy, in combination with multivariate calibration, is a promising technique to assess the biodiesel quality in both laboratory-scale and industrial-scale samples.

Monitoring the quality of oils for biodiesel production using multivariate near infrared spectroscopy models

Biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol in the presence of a catalyst. The quality of the oils used for biodiesel production strongly influences the final properties of biodiesel, namely its compliance to the European Standard. This work reports the use of near infrared (NIR) spectroscopy in the quality control of several oil properties, such as the iodine value, the water content and the acid number but, more importantly, the weight–weight percentages (wt%) of soybean, palm and rapeseed oil in mixtures. Principal component analysis was used to perform a qualitative analysis of the spectra, whereas partial least squares regression allowed the development of calibration models between analytical reference data and NIR spectra. The calibration ranges were 60–126 g I2 100 g−1 for the iodine value, 478–2500 mg kg−1 for the water content and 0.13-6.56 mg KOH g−1 for the acid number, whereas the validation errors were around 3.1 g I2 100 g−1, 111 mg kg−1 and 0.22 mg KOH g−1, respectively. The results obtained show that NIR spectroscopy is a promising technique to carry out the quality control of the commonly used vegetable oils for biodiesel production, namely the quality assurance and authenticity. Furthermore, it is of great value to have a simple, fast and reliable method to identify the composition of an oil mixture and/or some of its quality parameters, prior to storage or upon admission of a new lot of oil.

Process Analytical Technology: A Common Approach across Different Industries:

Introduction A s a process analytical chemistry (PAC) technique, near infrared (NIR) spectroscopy has been called the workhorse of the process analytics toolbox and rightly so given its multi-purpose capabilities, speed and ease of use, which make it a dependable process monitoring technology applicable across many fields. However, the perspective beyond PAC— of NIR spectroscopy being a key component of efforts in process analysis to establish process understanding and improved process control—has only recently been valued with the advent of PAT (Process Analytical Technology). NIR spectroscopy is a true enabler of science-based process development and manufacturing approaches (i.e. Quality by Design, QbD). In PAT, process monitoring is intertwined with state estimation, process understanding and control goals. While the domain of PAC is monitoring (i.e. sample analysis), the domain of PAT is the process itself. Figure 1 summarises the three levels of NIR use over a process life-cycle, from process development to routine manufacturing. In fact, scale-up and continuous improvement can be guided by NIR when it is used as a process PAT tool, although such adjustments will be harder or impossible to realise when NIR is only used as a mono-parametric PAC tool. If we accept that most hardware and software difficulties have been addressed, solved or significantly mitigated over the past decade, there are at least three key areas in which NIR may grow as a PAT tool. First, using whole-sample spectra to assess a process state, second, fusing NIR spectral information with process data or other information (e.g. process variables or other PAT tools) and third, using fused and nonfused NIR information across multiple unitoperations for feed-forward control, recipe management and multivariate supervisory control. In this short paper, we will briefly discuss advances in those future growth areas using industrial case studies from different processing industries with which we have been involved and show that a common PAT approach is possible with NIR as a PAT tool. Parameter Calibration range NIR accuracy ASTM method accuracy

Predicting methanol and water content in biodiesel by near infrared spectroscopy

Introduction T he production of biodiesel may be achieved by a homogeneous (sodium hydroxide or methoxide) catalysed transesterifi cation reaction between a lipid (vegetable oil or fat) and a short chain alcohol, such as methanol, to produce an ester and a by-product, glycerol. This reaction occurs stepwise, with mono and diglycerides as intermediate products. At the end of the reaction period, the glycerol-rich phase is separated from the ester layer by decantation or centrifugation. After separation, the biodiesel phase is contaminated with mono-, diand triglycerides, methanol, catalyst, free glycerol and soaps, and has to be purifi ed to comply with standards such as the European Standard EN 14214. The washing of the esters phase with water followed by vacuum drying is the most commonly-used process for biodiesel purifi cation. Since it is possible to produce biodiesel from several different feedstocks and technologies, the quality control of the fi nal product is of great concern and the European Standard EN 14214 establishes 25 parameters that have to be analysed to certify biodiesel quality. Among these, the contents in water and methanol are two important parameters. Therefore, EN imposes a maximum content of 0.05% (m/m) of water and 0.2% of methanol in biodiesel. Conventional analyses of biodiesels are very expensive and time-consuming and near infrared (NIR) spectroscopy in combination with multivariate data analysis appears potentially to be a cheaper and faster alternative. The use of partial least squares (PLS) or principal components regression (PCR) allows the development of calibration models between spectral and analytical data. This report analyses the effect of applying several commonly used pre-processing techniques prior to the application of PLS and PCR, on the quality of the calibration models developed to relate the NIR spectrum of biodiesel and its content of methanol and water. Experimental Industrial-scale and laboratory-scale samples of biodiesel produced from soybean, mixtures of soybean and palm, and from waste frying oils were prepared according to a procedure that guaranties independent variations of both water and methanol. Industrial samples of biodiesel produced from soybean, palm and waste frying oils were supplied by two Portuguese industrial companies. Karl Fisher titration was performed in a Metrohm 682 titroprocessor while methanol content was analysed by head space gas chromatography using a HP 5890 equipped with a PoraPlot Q packed column (3 m long). The NIR diffuse transfl ectance spectra of the biodiesel samples were acquired using an ABB Bomem MB160 spectrometer equipped with an InGaAs detector and a transfl ectance probe from Solvias. All calculations were carried out using Matlab Version 6.5 (MathWorks, Natick, MA) and the PLS Toolbox Version 3.0 (Eigenvector Research Inc., USA) for Matlab. Prior to PCR or PLS regressions, various pre-processing techniques were applied to spectral data: untreated data (identifi ed in the tables below as none); mean centring (MC); multiplicative scatter correction (MSC) followed by MC (MSC + MC); standard normal variate scaling (SNV) plus MC (SNV + MC); fi rst and second order Savitsky–Golay derivative followed by mean centring (SV1 + MC and SV2 + MC, respectively); MC followed by the orthogonal signal correction (MC + OSC) and, fi nally, the SV1 + MC and SV2 +MC followed by OSC (SV1 + MC + OSC, SV2 + MC + OSC, respectively).