Yield and Energy Modeling for Biochar and Bio-Oil Using Pyrolysis Temperature and Biomass Constituents Article link copied!

Pyrolysis offers a sustainable and efficient approach to resource utilization and waste management, transforming organic materials into valuable products. The quality and distribution of the pyrolysis products highly depend on the constituents’ properties and set process parameters. This research ai...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Awad, Mahmoud (author)
مؤلفون آخرون: Makkawi, Yassir (author), Hassan, Noha (author)
التنسيق: article
منشور في: 2024
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/11073/26341
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
الوصف
الملخص:Pyrolysis offers a sustainable and efficient approach to resource utilization and waste management, transforming organic materials into valuable products. The quality and distribution of the pyrolysis products highly depend on the constituents’ properties and set process parameters. This research aims to investigate and model this dependency, offering decision-makers a tool to guide them when designing the process for a particular application. Experimental data on the pyrolysis of various types of feedstocks processed at a wide range of pyrolysis temperatures (350–650 °C) are utilized to develop the prediction models. Four variables are modeled: the yield and energy content for both the biochar and bio-oil as a function of the pyrolysis temperature and feedstock characteristics. The models developed had very good prediction power with the coefficient of determination above 90%. The results highlight the advantages of food waste (leftover) as a suitable feedstock to produce biochar at the pyrolysis temperature within the range of 450–550 °C. Furthermore, the biofuels produced from food waste are found to be of good quality, with the bio-oil exceptionally high in energy content (HHV = 34.6 MJ/kg), which is almost 80% of that of diesel. The developed models provide a tool for predicting the biofuel yield and quality based on the feedstock selection and process temperature.