Properties Prediction of Torrefied Municipal Solid Waste using Linear Correlation Model

Jamin, Nur Ashikin; Saleh, Suriyati; Samad, Noor Asma Fazli Abdul

doi:10.1016/j.egypro.2019.01.217

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…In this study, the chemical exergy values of torrefied biomass materials were estimated from raw biomass without performing an experimental torrefaction procedure. Previous studies have predicted the chemical exergy of torrefied materials [34][35][36][37], but all these works include the calculations using the experimental procedure. However, no work has yet been published on estimating the chemical exergy of torrefied biomass from raw biomass.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning

Kartal

Özveren

2022

Biomass and Bioenergy

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Section: Introductionmentioning

confidence: 99%

Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning

Kartal

Özveren

2022

Biomass and Bioenergy

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“…In previous work, we used a regression model based on torrefaction duration and temperature for fuel properties determination of torrefied biomass [ 42 , 43 , 44 , 45 ]. Other models that use process kinetics (mass loss) are available [ 46 , 47 ] to predict the properties of carbonized materials. In recent years, neural networks have also been used for the prediction of HHV [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Calorific Value of Carbonized Solid Fuel Produced from Refuse-Derived Fuel in the Low-Temperature Pyrolysis in CO2

et al. 2020

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The decrease in the calorific value of refuse-derived fuel (RDF) is an unintended outcome of the progress made toward more sustainable waste management. Plastics and paper separation and recycling leads to the overall decrease in waste’s calorific value, further limiting its applicability for thermal treatment. Pyrolysis has been proposed to densify energy in RDF and generate carbonized solid fuel (CSF). The challenge is that the feedstock composition of RDF is variable and site-specific. Therefore, the optimal pyrolysis conditions have to be established every time, depending on feedstock composition. In this research, we developed a model to predict the higher heating value (HHV) of the RDF composed of eight morphological refuse groups after low-temperature pyrolysis in CO2 (300–500 °C and 60 min) into CSF. The model considers cardboard, fabric, kitchen waste, paper, plastic, rubber, PAP/AL/PE (paper/aluminum/polyethylene) composite packaging pack, and wood, pyrolysis temperature, and residence time. The determination coefficients (R2) and Akaike information criteria were used for selecting the best model among four mathematical functions: (I) linear, (II) second-order polynomial, (III) factorial regression, and (IV) quadratic regression. For each RDF waste component, among these four models, the one best fitted to the experimental data was chosen; then, these models were integrated into the general model that predicts the HHV of CSF from the blends of RDF. The general model was validated experimentally by the application to the RDF blends. The validation revealed that the model explains 70–75% CSF HHV data variability. The results show that the optimal pyrolysis conditions depend on the most abundant waste in the waste mixture. High-quality CSF can be obtained from wastes such as paper, carton, plastic, and rubber when processed at relatively low temperatures (300 °C), whereas wastes such as fabrics and wood require higher temperatures (500 °C). The developed model showed that it is possible to achieve the CSF with the highest HHV value by optimizing the pyrolysis of RDF with the process temperature, residence time, and feedstock blends pretreatment.

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Urban lignocellulosic waste as biofuel: thermal improvement and torrefaction kinetics

Silveira

Santanna

Souto

et al. 2022

J Therm Anal Calorim

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Properties Prediction of Torrefied Municipal Solid Waste using Linear Correlation Model

Cited by 8 publications

References 2 publications

Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning

Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning

The Prediction of Calorific Value of Carbonized Solid Fuel Produced from Refuse-Derived Fuel in the Low-Temperature Pyrolysis in CO2

Urban lignocellulosic waste as biofuel: thermal improvement and torrefaction kinetics

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