• Energy Research
• 12. Responsible consumption close
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• University of North Texas close

Modeling is regarded as a suitable tool to improve biomass pyrolysis in terms of efficiency, product yield, and controllability. However, it is crucial to develop advanced models to estimate products' yield and composition as functions of biomass type/characteristics and process conditions. Despite many developed models, most of them suffer from insufficient validation due to the complexity in determining the chemical compounds and their quantity. To this end, the present paper reviewed the modeling and verification of products derived from biomass pyrolysis. Besides, the possible solutions towards more accurate modeling of biomass pyrolysis were discussed. First of all, the paper commenced reviewing current models and validating methods of biomass pyrolysis. Afterward, the influences of biomass characteristics, particle size, and heat transfer on biomass pyrolysis, particle motion, reaction kinetics, product prediction, experimental validation, current gas sensors, and potential applications were reviewed and discussed comprehensively. There are some difficulties with using current pyrolysis gas chromatography and mass spectrometry (Py-GC/MS) for modeling and validation purposes due to its bulkiness, fragility, slow detection, and high cost. On account of this, the applications of Py-GC/MS in industries are limited, particularly for online product yield and composition measurements. In the final stage, a recommendation was provided to utilize high-temperature sensors with high potentials to precisely validate the models for product yield and composition (especially CO, CO2, and H2) during biomass pyrolysis.

Abstract This work emphases the influence of using different heating sources (direct thermal, solar, infrared, microwave heating) on the pyro-oil yield. The effect of the dominating process parameters, namely the heating rate and final temperature, are thoroughly discussed with respect to the heating and reaction mechanism involved. Emphasis is then placed on reviewing the application of microwave (MW) heating in pyrolysis as a relatively new technology with many promising features, particularly the little-known mechanisms of MW heating, new MW heating pattern and pathway using MW absorbents for pyrolysis of waste materials. Machine learning (ML) techniques were then used to statistically analyze the 182 observations in 59 pyrolysis cases obtained from previous pyrolysis practices. The ML linear regression model was developed to predict oil yield by five input variables (feedstock type, feedstock size, heating rate, final temperature, and heating source), which can be used as a guideline for pyrolysis production management. By comparing three heating sources (direct, solar and MW), MW heating is found to be the most efficient method to achieve the highest oil yield. The Decision Tree Analysis demonstrates that the importance order for key variables is as: Log feedstock size > Log heating rate > Heating rate > Temperature > Feedstock size > Heating sources > Feedstock type. Future work should focus on optimizing the heating method and heating rate to achieve optimal yield and quality of pyro-oil. The findings are envisaged to be useful for scaling up the pyrolysis of waste materials for industrial energy applications.