Investigation on the co-pyrolysis of agricultural waste and high-density polyethylene using TG-FTIR and artificial neural network modelling

Li, Ji-Shuo; Yao, Xiwen; Chen, Shoukun; Xu, Kaili; Fan, Bingjie; Yang, Dexin; Geng, Limin; Hai-ming, Qiao

doi:10.1016/j.psep.2022.02.033

Cited by 49 publications

(7 citation statements)

References 48 publications

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“…Bu et al (2019) [ 29 ] presented the kinetic parameters for orders 1, 2, and 3. Lai et al (2021) [ 30 ] and Li et al (2022) [ 31 ] used only the first order of CR because it is considered as the main mechanism. Patidar et al (2022) [ 32 ] revealed that the diffusion model was best suited to reflect the degradation process using the CR method.…”

Section: Resultsmentioning

confidence: 99%

Kinetics Study of Polypropylene Pyrolysis by Non-Isothermal Thermogravimetric Analysis

Dubdub

2023

Materials

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Polypropylene (PP) is considered as one of six polymers representative of plastic wastes. This paper attempts to obtain information on PP polymer pyrolysis kinetics with the help of thermogravimetric analysis (TGA). TGA is used to measure the weight of the sample with temperature increases at different heating rates—5, 10, 20, 30, and 40 K min−1—in inert nitrogen. The pyrolytic kinetics have been analyzed by four model-free methods—Friedman (FR), Flynn–Wall–Qzawa (FWO), Kissinger–Akahira–Sunose (KAS) and Starnik (STK)—and by two model-fitting methods—Coats–Redfern (CR) and Criado methods. The values of activation energies of PP polymer pyrolysis at different conversions are in good agreement with the average of (141, 112, 106, 108 kJ mol−1) for FR, FWO, KAS and STK, respectively. Criado methods have been implemented with the CR method to obtain the reaction mechanism model. As per Criado’s method, the most controlling reaction mechanism has been identified as the geometrical contraction models—cylinder model.

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Section: Resultsmentioning

confidence: 99%

Kinetics Study of Polypropylene Pyrolysis by Non-Isothermal Thermogravimetric Analysis

Dubdub

2023

Materials

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“…In order to characterize the pyrolysis reaction characteristics of different biomass types, the comprehensive volatiles release characteristic index D was introduced. The value of index D was obtained by comprehensive calculation of TG and DTG results obtained from biomass pyrolysis, and its calculation formula was as follows 11 :

D goodbreak= \frac{italicDT G_{\max} \times italicDT G_{mean}}{T_{s} \times T_{\max} \times Δ T_{1 / 2}}

where DTG max is the maximum value of mass loss rate; DTG mean is the mean value of mass loss rate; T s is the temperature at which volatiles begin to release; T max is the temperature corresponding to the maximum mass loss rate; and Δ T 1/2 is the temperature corresponding to DTG / DTG max = 1/2.…”

Section: Methodsmentioning

confidence: 99%

Section: Tg-ftirmentioning

confidence: 99%

“…Xin et al 10 discovered that torrefaction significantly influenced the gaseous products released during the thermal decomposition of herbaceous residues using TG‐FTIR. Li et al 11 investigated the co‐pyrolysis behavior of agricultural waste and high‐density polyethylene using TG‐FTIR. They found that there was a certain synergistic or inhibitory effect between the two biomasses during co‐pyrolysis, which influenced the formation of gaseous products.…”

Section: Introductionmentioning

confidence: 99%

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Study on pyrolysis behavior of agricultural and forestry wastes using thermogravimetric‐Fourier transform infrared spectrometer (TG‐FTIR)

Liu,

Li,

Liu

et al. 2023

Asia-Pacific J Chem Eng

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To investigate the effective utilization of agricultural and forestry wastes for mitigating the shortage of primary energy and environmental pollution, the pyrolysis behavior of corn straw (CS), wheat straw (WS), and poplar sawdust (PS) was studied using thermogravimetric–Fourier transform infrared (TG‐FTIR) analysis. The activation energies and pyrolysis mechanism functions of different biomass types were calculated. The results showed that the functional group structures of the gaseous products generated from the pyrolysis of different biomass types were similar, mainly including OH, aliphatic CH, CO, aromatic ring skeleton, aromatic CH, and CO groups. These functional group structures mainly existed in oxygen‐containing compounds such as phenols, ketones, aldehydes, acids, esters, alcohols, and ethers. The pyrolysis process of agricultural and forestry wastes exhibited certain differences, with the PS undergoing thermal decomposition more easily, while the thermal decomposition mechanism functions of the two agricultural wastes (CS and WS) were similar. PS had a later initial release time for volatile matter, and the oxygen compounds produced by its pyrolysis were the highest, followed by WS and CS.

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“…Li et al (2022) [ 14 ] applied the ANN (multi-layer, feed-forward, network-backpropagation) for one run with a heating rate 20 K min −1 for the co-pyrolysis of agricultural waste and HDPE. They applied the mass change of feedstock as the output variable and temperature as the input variable.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Dubdub

2023

Polymers

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Among machine learning (ML) studies, artificial neural network (ANN) analysis is the most widely used technique in pyrolysis research. In this work, the pyrolysis of polypropylene (PP) polymers was established using a thermogravimetric analyzer (TGA) with five sets of heating rates (5–40 K min−1). TGA data was used to exploit an ANN network by achieving a feed-forward backpropagation optimization technique in order to predict the weight-left percentage. Two important ANN model input variables were identified as the heating rate (K min−1) and temperature (K). For the range of TGA values, a 2-10-10-1 network with two hidden layers (Logsig-Tansig) was concluded to be the best structure for predicting the weight-left percentage. The ANN demonstrated a good agreement between the experimental and calculated values, with a high correlation coefficient (R) of greater than 0.9999. The final network was then simulated with the new input data set for effective performance. In addition, a sensitivity analysis was performed to identify the uncertainties associated with the relationship between the output and input parameters. Temperature was found to be a more sensitive input parameter than the heating rate on the weight-left percentage calculation.

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Investigation on the co-pyrolysis of agricultural waste and high-density polyethylene using TG-FTIR and artificial neural network modelling

Cited by 49 publications

References 48 publications

Kinetics Study of Polypropylene Pyrolysis by Non-Isothermal Thermogravimetric Analysis

Kinetics Study of Polypropylene Pyrolysis by Non-Isothermal Thermogravimetric Analysis

Study on pyrolysis behavior of agricultural and forestry wastes using thermogravimetric‐Fourier transform infrared spectrometer (TG‐FTIR)

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

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