Pyrolysis of de-fatted microalgae residue: A study on thermal-kinetics, products? optimization, and neural network modelling

The present work focused on the pyrolysis-based valorization of microalgae biomass residual for the generation of sustainable fuel and value-added chemicals. Key pyrolysis factors, including temperature, residence time, particle size, and heating rate, were modeled via an artificial neural network (ANN) and response surface methodology (RSM) models. The use of such an integrated technique was able to conquer the individual constraints of both modeling approaches. RSM model for H2-rich syngas demonstrated that the value; R2 = 0.99, minimum p = 0.00, and maximum F = 3877.16 has close relation among the statistical parameters. The ANN model for H2-rich syngas revealed that higher R2 = 0.9985 and lower RSME = 0.1038 were obtained for the training phase, while; the higher R2 and lower RSME values of 0.9862 and 0.2661 were estimated for the training phase hence showed a better agreement among the parameters. Optimum H2 production of 44.46 vol% was produced at temperature = 516.76 degrees C, residence time = 17.7 min, particle size = 0.23 mm, and heating rate = 17.37 degrees C/min. All the pyrolytic products have been characterized in detail and are recommended for high enduse. The GC/MS technique revealed that bio-oil was constituted of various organic complexes, which could be used as a substitute for hydrocarbon fuels after undergoing certain upgradation procedures (e.g., hydrotreating, hydrodeoxygenation, hydrodenitrogenation, and hydrocracking) and extracted into different chemicals. In addition, the biochar was characterized using the SEM technique, which also demonstrated its potential as fuel and in a variety of other applications. The study showed that de-fatted Chlorella sorokiniana residue (De-CR) could be efficiently employed to produce bio-energy precursors.

Automated summary

This study focuses on the pyrolysis-based valorization of microalgae biomass residual for the production of sustainable fuel and value-added chemicals. The key factors of pyrolysis were modeled using artificial neural network and response surface methodology models. The results show that the integrated technique overcomes the constraints of individual modeling approaches and achieves high gas yields. Additionally, the pyrolytic products were characterized and recommended for high-end applications.