Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy (original) (raw)
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Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review
Biomass used in energy conversion processes is typically characterized by high variability, making its utilization challenging. Therefore, there is a need for a fast and non-destructive method to determine feedstock/product properties and directly monitor process reactors. The near-infrared spectroscopy (NIRS) technique together with advanced data analysis methods offers a possible solution. This review focuses on the introduction of the NIRS method and its recent applications to physical, thermochemical, biochemical and physiochemical biomass conversion processes represented mainly by pelleting, combustion, gasification, pyrolysis, as well as biogas, bioethanol, and biodiesel production. NIRS has been proven to be a reliable and inexpensive method with a great potential for use in process optimization, advanced control, or product quality assurance.
Fuel Processing Technology, 2018
The use of woodchip, and biofuels in general, is a fundamental step towards the transition from fossil fuel to renewable energy. The growth in the demand for wood fuels and the inherent variability in the properties of woody material lead to the need to verify its quality. EN ISO 17225-4 divides woodchip in different quality classes according to chemical-physical parameters and quality attributes. In this study, we have coupled near infrared spectroscopy with Partial Least Square regression to model gross calorific value and ash content of woodchip samples. Moreover, variables selection methods were tested in order to improve the models and get better prediction. Gross calorific value and ash content were predicted with a standard error of 234 J/g and 0.44%, respectively. The results indicate that the models could be used in screening applications and near infrared spectroscopy is a promising tool in the evaluation of biomass quality.
In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O mode...
Rapid Quality Control of Woodchip Parameters Using a Hand-Held Near Infrared Spectrophotometer
Processes, 2020
Near infrared spectroscopy is a non-invasive and rapid technique to support the analysis of solid biofuels such as woodchip, which is considered as a suitable alternative for energy production, according to European goals for fossil fuel reduction. Chemical and physical properties of the woodchip influence combustion performance, so the most discriminant parameters such as moisture and ash content and gross calorific value were constantly monitored. The aim of this study was the development of prediction models for these three parameters with the use of a hand-held NIR spectrometer. Laboratory analyses were carried out to evaluate the quality of several Italian samples from a power plant, and PLS regression models were developed to test prediction accuracy. Moreover, the most relevant wavelengths were investigated to discriminate chemical compounds influence. Prediction models demonstrated the capacity of handheld MicroNIR instrument to be considered a practical tool for solid biofu...
Fuel, 2018
The increasing concern regarding energy supply and the consequent rapid growth of the pellet market lead to the need to classify the product quality. To this aim, chemical-physical parameters and qualitative attributes are defined by the technical standards EN ISO 17,225 to classify the quality of biofuels, but, while the former can be determined by traditional chemical analysis, no methodologies have been set for the latter one. Hence, nearinfrared spectroscopy was tested to obtain information about the origin and the source of the pellet, at the moment only declared by the producers and difficult to be achieved by conventional analysis. In fact, the great strength of the technique is based on the fact that biomass features could be read simultaneously with a rapid and cheap NIR measurement. Checking the presence of treated wood (e.g. residues from wood processing industry) especially in densified products, such as pellets and briquettes, is particular important since in several European countries, e.g. Italy, these materials are considered as waste. In this study more than a hundred samples of virgin and treated wood (residues from wood processing industries) were analysed by means of FT-NIR. Partial Least Square regression-Discriminant Analysis was used in order to classify samples between the two classes and different variables selection methods were tested in order to improve the classification performance of the models. The results obtained demonstrated that near infrared analysis coupled with multivariate analysis can be used in screening applications to classify virgin wood from glue-laminated wood and treated wood. In particular, the model for the discrimination of treated wood (except glue-laminated samples) from virgin wood performs 100% correct classification and the model for the discrimination between virgin wood and glue-laminated wood only has a 3.6% misclassification rate. The methodology can be considered as the first one able to provide information about the origin of the biomass in a rapid and cheap way.
IM Publications Open LLP eBooks, 2019
With the aim to reduce the dependence on fossil fuels and mitigate climate change, biomass for energy use is becoming more and more important. In particular, wood pellets are gaining greater attention because of the easy logistics and their high energy density in comparison to other solid biomasses. This is also demonstrated by the rapid growth of its demand in Europe. For pellets, traceability is a very important and complex issue, since the feedstock employed is de-structured by grinding and densification and thus losing qualitative information. As a consequence, a multitude of wood sources can participate to their blend in a concealed way, modifying the quality. The international standard EN ISO 17225-2 defines different quality classes for woody pellets taking into consideration chemical-physical parameters and the provenance traceability and composition of the material. In particular, the European standard considers the possibility of using by-products and residues from the wood processing industry, i.e. wood containing glue residues, for pellet production, but Italian national legislation considered these materials like waste. This work aimed at verifying the ability of Fourier Transform−Near-Infrared (FT-NIR) spectroscopy to discriminate between treated and virgin wood. For this purpose, more than one hundred samples of virgin and treated wood deriving from the wood processing industry were collected and analyzed by FT-NIR. The results obtained showed that this technique is able to provide qualitative information about pellet traceability. Therefore, the methodology should be considered as a valid tool for pellet quality control, because it allows to obtain information about the origin of the material used for its production.
Renewable Energy, 2020
In a previous study, a near infrared (NIR) spectroscopy model was developed using a spectra of ground bamboo samples. Although the previous report on ground bamboo described a good performance, operation in the power plant was found to be inconvenient due to preparation costs and labour required for the necessary preparation of ground samples. Thus, this study presents the comparison of the performance of an NIR model that was developed by direct scanning of bamboo chips to the previously developed model for ground samples. Special emphasis is put on the comparison of the spectral reproducibility. Bamboo chip models were developed based on PLS regression with variable selection methods in order to achieve the optimal model. The moisture content (MC) and ash content (A) of the developed bamboo chip models could be applied toward quality assurance. The volatile matter (VM) and fixed carbon (FC) models could be used for approximating predictions. The gross and net calorific value (GCV and NCV) models could be used for most applications. The root mean square (RMS) value of pretreated spectra of different particle size had no statistically significant differences. The study's findings indicate that the model developed using NIR spectroscopy protocol with wood chips spectra can be used as a classification tool and is an effective method for estimating bamboo chip energy quality. The big particle size of wood chips affect negatively the prediction model, however, it could be solved through spectral pre-processing technique, thus eliminating the need for grinding feedstock samples.
Prediction of Heating and Ignition Properties of Biomass Dusts Using Near Infrared Spectroscopy
2014
Dusts (i.e. particles of size less than 500 μm) are generated during handling and processing of biomass feedstock. Similar to damages that have been reported from ignition of dusts obtained from industries, ignited biomass dust may potentially cause fire and explosion in biorefinery plants that can result in human fatalities, serious injuries and substantial monetary loss. Control measures to prevent the heating and ignition of biomass dusts will play a critical role in development of safety guidelines and standards for bio-based industries. The research aims at quantifying and predicting (using NIR spectroscopy) the heating and ignition properties of dust from ten biomass feedstocks. Three different types of coals were also used for comparison purposes. The range of values obtained for these properties were 240°C-335°C (minimum hot surface ignition temperature, MIT), 266°C-448°C (temperature of onset of rapid volatilization, TORV), 304°C-485°C (temperature of maximum rate of mass loss, TMML), 242°C-423°C (oxidation temperature, TOXY), 206°C-249°C (temperature of onset of rapid exothermic reaction, TRE) and 354°C-429°C (maximum temperature reached during exothermic reaction, TME). Coefficient of determination (R 2) values for internal validation of prediction models developed using PCA on raw NIR spectral data for MIT, TORV, TMML, TOXY, TRE and TME were 0.994, 0.984, 0.963, 0.737, 0.931 and 0.901 respectively, whereas, first derivative NIR spectral data yielded R 2 (calibration) for these properties as 0.976, 0.964, 0.943, 0.798, 0.923 and 0.895 respectively. Four different biomass dusts (eucalyptus, pine, sweetgum and switchgrass) were used to validate the prediction models externally. Coefficient of determination (R 2) values iii for all the models was obtained less than 0.28. Poor performance of models under external validation was attributed to small sample sizes of the feedstocks that were used during building of prediction models. I would like to thank my parents, Dr. J.S. Dhiman and Mrs. Manjeet Kaur Dhiman for their unconditional support and care throughout my life. They were a great source of inspiration to me to study further. I will always be indebted to them. I would also like to thank my brother Mankaran Dhiman for encouraging me. I wish to thank my friend Aman for supporting me and being patient during my course of study. I would like to express my sincere gratitude to my advisor Dr. Oladiran Fasina for his patience, motivation, guidance and continuous support of my Masters research. I am highly indebted by his guidance in writing of this thesis. I would like to thank my research committee members: Dr. Brian Via, Dr. Sushil Adhikari and Dr. Timothy McDonald for their encouragement, support and insightful comments. I would also like to thank Christian Brodbeck, Jonathan Griffith and James for their support in procurement of raw material for my research. I also wish to thank my lab mates: Oluwatosin, Gbenga, Gurdeep and Anshu for stimulating discussions and hours of working together. I am very thankful to my friends Jatinder, Raman, Gurjot, Jass, Deep, Manbir, Gurjeet and Roger for their encouragement. I would like to thank Alabama Agricultural Experiment Station, United States Department of Agriculture (USDA) funded Southeast Integrated Biomass Supply System (IBSS) and Department of Energy (DOE) for providing infrastructure and funding for my research.
Assessing Trees, Wood and Derived Products with near Infrared Spectroscopy: Hints and Tips
Journal of Near Infrared Spectroscopy, 2016
Wood is a renewable and valuable resource for a variety of end-use application areas. However, rapid and reliable assessments are needed to identify the quality of the tree, timber or wood product at all stages of production and processing. The ideal technology for assessing wood and wood products must provide reliable data, be user-friendly, cost-competitive and provide a rapid analysis. The ultimate application of near infrared (NIR) spectroscopy of wood or wood products is to substitute for costly and time-consuming reference measurements in order to aid process optimisation or determine properties and genetic traits on large numbers of individual samples. Increased interest in the application of NIR spectroscopy in various research fields including wood is observed nowadays. A vast number of publications highlight the potential of NIR spectroscopy for the characterisation of wood in a broad area of uses. The Journal of Near Infrared Spectroscopy has published two special issues ...