Dataset Information

Biomass Pyrolysis Solids as Reducing Agents: Comparison with Commercial Reducing Agents.

ABSTRACT: Biomass is one of the most suitable options to be used as renewable energy source due to its extensive availability and its contribution to reduce greenhouse gas emissions. Pyrolysis of lignocellulosic biomass under appropriate conditions (slow heating rate and high temperatures) can produce a quality solid product, which could be applicable to several metallurgical processes as reducing agent (biocoke or bioreducer). Two woody biomass samples (olives and eucalyptus) were pyrolyzed to produce biocoke. These biocokes were characterized by means of proximate and ultimate analysis, real density, specific surface area, and porosity and were compared with three commercial reducing agents. Finally, reactivity tests were performed both with the biocokes and with the commercial reducing agents. Bioreducers have lower ash and sulfur contents than commercial reducers, higher surface area and porosity, and consequently, much higher reactivity. Bioreducers are not appropriate to be used as top burden in blast furnaces, but they can be used as fuel and reducing agent either tuyére injected at the lower part of the blast furnace or in non-ferrous metallurgical processes where no mechanical strength is needed as, for example, in rotary kilns.

SUBMITTER: Adrados A

PROVIDER: S-EPMC5456575 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

Dataset Information

Biomass Pyrolysis Solids as Reducing Agents: Comparison with Commercial Reducing Agents.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets