Correlative HHV prediction from proximate and ultimate analysis of char obtained from co-cracking of residual fuel oil with plastics

Kasar, Pamreishang; Ahmaruzzaman, Md.

doi:10.1007/s11814-021-0790-8

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…Higher carbon content was reported to improve the combustion properties of fuel and thus resulting in higher HHV of bio‐oil 34 . However, lower oxygen content was preferable in any fuel as it reduced the HHV of fuel 35 . From the decision rules generated as shown in Table 6, we can conclude that to achieve HHV of at least Class 2, the carbon and oxygen content of the feedstock sample needs to be greater than 44% and below 45%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…34 However, lower oxygen content was preferable in any fuel as it reduced the HHV of fuel. 35 From the decision rules generated as shown in Table 6, we can conclude that to achieve HHV of at least Class 2, the carbon and oxygen content of the feedstock sample needs to be greater than 44% and below 45%, respectively. For example, Rule 27 from Case Study 1: Reduct 2 stated that "If the feedstock sample has a carbon content of greater or equal to 48.5% and oxygen content of greater or equal to 43.45 %, then the produced bio-oil will be having a HHV values within the range of 20.00 to 29.00 MJ/kg.…”

Section: Mechanistic Plausibility Of Rulesmentioning

confidence: 99%

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Estimation of fast pyrolysis bio‐oil properties from feedstock characteristics using rough‐set ‐based machine learning

Chong

Thangalazhy‐Gopakumar

Tan

et al. 2022

Intl J of Energy Research

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Summary In this work, a data‐driven rough‐set‐based machine learning model has been proposed as a pre‐processing and predictive modelling tool to predict the pyrolysis bio‐oil properties based on pyrolysis temperature and feedstock characteristics. A database consisting of feedstock proximate and ultimate analyses, pyrolysis temperature, bio‐oil's pH value, and bio‐oil's higher heating value was compiled and used to train the rough‐set‐based machine learning model. The resulting rule‐based rough‐set‐based machine learning model demonstrated promising strength, certainty, and coverage factor. Furthermore, the emergent patterns and mechanistic plausibility of the rough‐set‐based machine learning models were analysed. The generated rules illustrated reasonable predictive capability in estimating the higher heating value and pH value of bio‐oil based on the feedstock characterisation and pyrolysis temperature. Rough‐set‐based machine learning model is thus demonstrated to be a simple and straightforward approach for feedstock composition and pyrolysis temperature selection in pyrolysis/co‐pyrolysis bio‐oil production.

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Section: Resultsmentioning

confidence: 99%

Section: Mechanistic Plausibility Of Rulesmentioning

confidence: 99%

Estimation of fast pyrolysis bio‐oil properties from feedstock characteristics using rough‐set ‐based machine learning

Chong

Thangalazhy‐Gopakumar

Tan

et al. 2022

Intl J of Energy Research

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“…The final analysis considers the amount of hydrogen (H), carbon (C), sulphur (S), oxygen (O) and nitrogen (N) (N). Estimating the quantities of volatile matter, moisture, ash and fixed carbon using proximate analysis [3,4]. Similarly, thermal parameters such as heating values, notably the Higher Heating Value (HHV) [5,6], play a significant role in the design of WTE plants.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Higher Heating Value for MSW Using DSVM and BSOA

Jose¹,

Sasipraba²

2023

Intelligent Automation &Amp; Soft Computing

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In recent decades, the generation of Municipal Solid Waste (MSW) is steadily increasing due to urbanization and technological advancement. The collection and disposal of municipal solid waste cause considerable environmental degradation, making MSW management a global priority. Waste-to-energy (WTE) using thermochemical process has been identified as the key solution in this area. After evaluating many automated Higher Heating Value (HHV) prediction approaches, an Optimal Deep Learning-based HHV Prediction (ODL-HHVP) model for MSW management has been developed. The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste, based on its oxygen, water, hydrogen, carbon, nitrogen, sulphur and ash constituents. In addition, the ODL-HHVP model contains a Deep Support Vector Machine (DSVM) regression component that can accurately predict the HHV. In addition, the Beetle Swarm Optimization (BSO) method is utilised as a hyperparameter optimizer in conjunction with the DSVM model, resulting in the highest HHV prediction accuracy. A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method. The Multiple Linear Regression (MLR), Genetic Programming (GP), Resilient backpropagation (RP), Levenberg Marquardt (LM) and DSVM approaches have attained an ineffective result with RMSEs of 4.360, 2.870, 3.590, 3.100 and 3.050, respectively. The experimental findings demonstrate that the ODL-HHVP technique outperforms existing state-of-art technologies in a variety of respects.

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“…Further, to understand the reactions which could have occurred during the co-cracking process, the characterization of the liquid products, in addition to the solid product 22 , was necessary. The main objective of the study was to understand the average structural parameters, molecular weight distribution, calorific values, flash point, pour point, and density, besides the elemental content in the liquid products.…”

Section: Introductionmentioning

confidence: 99%

Characterization of liquid products obtained from catalytic binary co-cracking of residual fuel oil with various waste plastics

Kasar

Ahmaruzzaman

2022

Sci Rep

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Recycling polymeric waste and heavy oil residues are important for energy recovery and raw material processing. Catalytic pyrolysis is a unique technology used to generate alternative energy, and it can stands out to be one of the environmentally friendly and alternative routes for the generation of renewable energy. Limited study has been reported in the literature on the co-cracking of residual fuels with waste plastics to establish its properties and potential. In this study, we have characterized the products in liquid form resulting from the co-cracking of residual fuel oil (RFO) with plastic waste in an isothermal condition. The characterization was carried out using nuclear magnetic resonance (1H NMR & 13C NMR), Fourier transforms infrared spectroscopy (FTIR), gel permeation chromatography (GPC), bomb calorimetry, and ultimate analyzer, in addition to the characterization of the flashpoint, pour point, and density. As a result of co-cracking, the liquid exhibits a significant decline in the overall molecular weight and an increase in the content of saturated aliphatic carbon and a decrease in the protonated aromatic carbons with aliphatic compounds as the primary constituent were observed from the spectra, having a pour point of 291.15–192.15 K and high calorific values between 42–45 MJ/kg. The characteristics of the liquid reveal a synergistic effect of co-cracking and demonstrate the potential of the co-cracking process of waste plastics with residual fuel to be an alternate source of energy and added-value chemical product recovery routes.

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Correlative HHV prediction from proximate and ultimate analysis of char obtained from co-cracking of residual fuel oil with plastics

Cited by 8 publications

References 25 publications

Estimation of fast pyrolysis bio‐oil properties from feedstock characteristics using rough‐set ‐based machine learning

Estimation of fast pyrolysis bio‐oil properties from feedstock characteristics using rough‐set ‐based machine learning

Estimation of Higher Heating Value for MSW Using DSVM and BSOA

Characterization of liquid products obtained from catalytic binary co-cracking of residual fuel oil with various waste plastics

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