A numerical study on biomass fast pyrolysis process: A comparison between full lumped modeling and hybrid modeling combined with CFD

Lee, Yu Ri; Choi, Hang Seok; Park, Hoon Chae; Lee, Ji Eun

doi:10.1016/j.compchemeng.2015.07.007

Cited by 31 publications

(12 citation statements)

References 61 publications

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“…Kinetic reaction models based on thermodynamic equilibrium calculations can provide this flexibility and have been developed for combustion or gasification reactions [15][16][17], but proven to be unsuitable for predicting pyrolysis reactions [18]. Current approaches for modelling pyrolysis processes focus strongly on computational fluid dynamic (CFD) [19][20][21] or single particle models [22,23], while others consider isolated biomass components (like e.g. lignin) [24] or determine only the lumped yields of the principal pyrolysis products (gas, char, oil) [25][26][27][28][29][30], while they do not model their detailed composition.…”

Section: Introductionmentioning

confidence: 99%

A kinetic reaction model for biomass pyrolysis processes in Aspen Plus

et al. 2017

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This paper presents a novel kinetic reaction model for biomass pyrolysis processes. The model is based on the three main building blocks of lignocellulosic biomass, cellulose, hemicellulose and lignin and can be readily implemented in Aspen Plus and easily adapted to other process simulation software packages. It uses a set of 149 individual reactions that represent the volatilization, decomposition and recomposition processes of biomass pyrolysis. A linear regression algorithm accounts for the secondary pyrolysis reactions, thus allowing the calculation of slow and intermediate pyrolysis reactions. The bio-oil is modelled with a high level of detail, using up to 33 model compounds, which allows for a comprehensive estimation of the properties of the bio-oil and the prediction of further upgrading reactions. After showing good agreement with existing literature data, our own pyrolysis experiments are reported for validating the reaction model. A beech wood feedstock is subjected to pyrolysis under welldefined conditions at different temperatures and the product yields and compositions are determined. Reproducing the experimental pyrolysis runs with the simulation model, a high coincidence is found for the obtained fraction yields (bio-oil, char and gas), for the water content and for the elemental composition of the pyrolysis products. The kinetic reaction model is found to be suited for predicting pyrolysis yields and product composition for any lignocellulosic biomass feedstock under typical pyrolysis conditions without the need for experimental data.

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Section: Introductionmentioning

confidence: 99%

A kinetic reaction model for biomass pyrolysis processes in Aspen Plus

et al. 2017

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“…After the above discussion, we adopt the hybrid modeling by combining the mechanism model with the data-driven model in order to solve the problem of dynamometer card inversion. Nowadays, hybrid modeling algorithms have been successfully put into use in a multitude of complex industry processes [9][10][11][12][13][14][15][16][17][18][19][20][21][22], and they mostly consist of serial structure and parallel structure. The serial hybrid model firstly performs data-driven modeling for the uncertain parameters, and then the outputs of data-driven model are taken as the input of the mechanism model, while the outputs of the parallel hybrid model are the sum of the outputs of datadriven model and mechanism model, and the data-driven model is built to compensate the error of the mechanism model.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Electric-Parameter-Based Inversion of Dynamometer Card Using Hybrid Modeling for Beam Pumping System

Wei

Gao

2018

Mathematical Problems in Engineering

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Conventional fault diagnosis and production calculation of an oil well can be conducted with the surface dynamometer cards, which are obtained by load sensor installed on the horse head. This method to measure the dynamometer cards is limited by the sensor maintenance and calibration, battery replacement, and safety hazards for staff. As the basic parameter of the oil extraction industry, electric parameters have the advantages of low cost and high efficiency. So the inversions of dynamometer card with electric parameters are attracting more and more attention. In order to solve the problem of insufficient data and consider the realtime performance in the actual oil extraction process, this paper proposes a novel hybrid model which consists of two parts: the mechanism model of polished rod load and the suspension displacement calculated with the space vector equations of motor and a data-dependent kernel online sequential extreme learning machine (DDKOS-ELM) model proposed to correct the output error of the mechanism model, which improves the kernel function selection and makes it real-time. Thus, the highlights of this paper can be summed up in two points: (1) under the circumstance of the bottom dead point detection without sensors, the mechanism modeling of the polished rod load and suspension displacement has been carried out from the perspective of mathematical model of AC motor; (2) a novel data-driven model based on data-dependent kernel online sequential extreme learning machine (DDKOS-ELM) has been proposed to improve the kernel functions selection. The coefficients in the data-dependent kernel function are optimized with improved free search algorithm (IFSA). The proposed hybrid model has been applied to a normal working oil well and the prediction results show better accuracy than the pure data-driven model and mechanism model.

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“…The comparisons between experimental results and model prediction show that the main experimental trends were well reproduced by the model. Lee et al [33] presented study focuses on process modeling and simulation of a biomass fast pyrolysis. Choi et al [34] also used POLIMI mechanism for fast pyrolysis and the reaction temperature was varied from 400 to 600 • C.…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Analysis on Co-Gasification of Sewage Sludge with Energetic Crops

et al. 2019

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As known, dried sewage sludge, is a by-product produced from waste water treatment, contains significant amounts of organic content, and makes up to 60% with overall calorific value from 9 to 12 MJ/kg. Hence, it can be considered as material for thermal processing focusing on heat and power production. Among thermal conversion technologies, gasification is seen as the effective one because it can be easily combined with heat and power cogeneration units. On the other hand, due to high mineral content (40–50%) in the sludge, it is difficult to be gasified and obtain syngas with calorific value satisfactory enough for fueling the internal combustion engine. The dried sludge can be subjected to be gasified at temperature above 850 °C. However, large amounts of mineral content do not provide favorable conditions to obtain this required temperature. Thus, it is proposed to enrich the sewage sludge with biomass characterized with significantly higher calorific value. In the article, co-gasification of sewage sludge and Virginia Mallow—energetic crops was investigated. Results from experimental and numerical investigation have been presented. The dried sewage sludge enriched with Virginia Mallow at a mass ratio of 0/100%, 50/50% and 100/0% in tests and in the range from 0 to 100% for theoretical analysis was applied in order to achieve effective gasification process. As observed, lignocellulosic biomass like Virginia Mallow contains low amounts of mineral content below 2%, which makes it appropriate for thermal processing. It contributes to more stable and efficient gasification process. Additionally, Virginia Mallow caused that the process temperature possible to achieve, was 950 °C. Thus, sewage sludge was mixed with this high-energy component in order to improve the gasification parameters and obtain syngas with higher calorific value. A zero-dimensional, two-zone model was developed with aid of the POLIMI kinetics mechanism developed by CRECK Modeling Group to simulate gasification of low calorific substances enriched with high calorific biomass. Obtained results showed that sewage sludge can be completely gasified at presence of Virginia Mallow. Syngas calorific value of approximately 5 MJ/Nm3 was produced from this gasification process. The maximal percentage of Virginia Mallow in the mixture with the sewage sludge was set at 50% due to economic aspects of the technology. It was found, that satisfactory conditions for effective gasification were achieved at this 50/50% percentage of sewage sludge and Virginia Mallow. Potential intensity of gasification was predicted from this 0-D 2-zones model, which calculates area of reduction zone to area of combustion zone. This reduction-to-combustion area ratio for the sewage sludge-Virginia Mallow mixture was estimated at value of 2. Finally, the model was successfully verified with results from tests, hence it was proposed as a tool for preliminary investigation on poor fuels gasification.

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A numerical study on biomass fast pyrolysis process: A comparison between full lumped modeling and hybrid modeling combined with CFD

Cited by 31 publications

References 61 publications

A kinetic reaction model for biomass pyrolysis processes in Aspen Plus

A kinetic reaction model for biomass pyrolysis processes in Aspen Plus

Electric-Parameter-Based Inversion of Dynamometer Card Using Hybrid Modeling for Beam Pumping System

Theoretical and Experimental Analysis on Co-Gasification of Sewage Sludge with Energetic Crops

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