A conceptual framework for biomass gasifier design using a semi-empirical model and heuristic algorithm

Yan, Beibei; Zhao, Sheng; Li, Jian; Chen, Guanyi; Tao, Junyu

doi:10.1016/j.cej.2021.130881

Cited by 18 publications

(2 citation statements)

References 46 publications

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“…The development and utilization of nonfossil energy sources are the main way to promote the green and low-carbon energy transition . Biomass stands out as the sole renewable resource containing carbon, characterized by its inherent carbon-neutral nature . Therefore, biomass is considered to have great advantages in solving current environmental and energy challenges worldwide .…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on the Steam Gasification of Biomass in a 100 kW Dual Fluidized Bed Gasifier

Yang,

Hu,

Chen

et al. 2024

Energy Fuels

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The dual fluidized bed (DFB) shows great potential for the steam gasification of biomass to produce hydrogen. Understanding the hydrodynamics and thermochemical characteristics in a pilot-scale DFB system is crucial for optimizing the operating conditions and devising the reactor design. In this study, a three-dimensional model, based on the multiphase particle-in-cell (MP-PIC) method, is developed for a 100 kW biomass DFB system featuring a staged gasification reactor. The entire DFB system is simulated, with a focus on the gasification reaction in the gasifier reactor and ignoring the reactions in the combustion reactor. The basic simulation settings are optimized based on the pressure distribution observed in the cold experiment results. The optimized model correctly predicts the reactor temperature, pressure distribution, and gas production characteristics of the gasification process. Then, the effects of temperature, steam-to-biomass ratio (S/B), and gasification atmosphere on gasification performance are explored based on the verified model. It shows that the H 2 production and CO 2 production are increased together with an increasing H 2 /CO ratio with the increase of gasification temperature. With the increase of S/B, the yields of H 2 and CO 2 and the ratio of H 2 /CO first increase and then decrease. The optimal S/B for this DFB is 0.5, as excessive steam can lower the reaction temperature, which is unfavorable for gasification conversion. Changing the gasification agent from pure steam to a CO 2 /H 2 O mixture results in increased CO yield and a lower H 2 content because of the competition of CO/H 2 O gasification and water gas shift reactions. This study provides meaningful insights on biomass gasification with steam in a large-scale fluidized bed gasifier, and the findings are beneficial for designing and optimizing DFB, particularly in the context of staged gasification processes.

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Section: Introductionmentioning

confidence: 99%

Numerical Study on the Steam Gasification of Biomass in a 100 kW Dual Fluidized Bed Gasifier

Yang,

Hu,

Chen

et al. 2024

Energy Fuels

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“…Excellent correlations (R 2 > 0.98) appear between the results achieved by these ANNs and the published experimental data in nearly all cases. In a recent study, Yan et al [36] used thermodynamic ANN modeling to accurately predict gasification outputs, while the non-dominated sorting genetic algorithm-II was used to evaluate different gasifier design parameters. To train the thermodynamic ANN model, the researchers used 166 experimentally reported data points.…”

Section: Introductionmentioning

confidence: 99%

The dimensional design of a laboratory-scale fluidized bed gasifier using machine learning based on a kinetic method

Kartal

Özveren

2022

Energy Conversion and Management

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Artificial intelligence and machine learning at various stages and scales of process systems engineering

Srinivasan,

Puliyanda,

Thosar

et al. 2024

Can J Chem Eng

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We review the utility and application of artificial intelligence (AI) and machine learning (ML) at various process scales in this work, from molecules and reactions to materials to processes, plants, and supply chains; furthermore, we highlight whether the application is at the design or operational stage of the process. In particular, we focus on the distinct representational frameworks employed at the various scales and the physics (equivariance, additivity, injectivity, connectivity, hierarchy, and heterogeneity) they capture. We also review AI techniques and frameworks important in process systems, including hybrid AI modelling, human‐AI collaborations, and generative AI techniques. In hybrid AI models, we emphasize the importance of hyperparameter tuning, especially in the case of physics‐informed regularization. We highlight the importance of studying human‐AI interactions, especially in the context of automation, and distinguish the features of human‐complements‐AI systems from those of AI‐complements‐human systems. Of particular importance in the AI‐complements‐human framework are model explanations, including rule‐based explanation, explanation‐by‐example, explanation‐by‐simplification, visualization, and feature relevance. Generative AI methods are becoming increasingly relevant in process systems engineering, especially in contexts that do not belong to ‘big data’, primarily due to the lack of high quality labelled data. We highlight the use of generative AI methods including generative adversarial networks, graph neural networks, and large language models/transformers along with non‐traditional process data (images, audio, and text).

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A conceptual framework for biomass gasifier design using a semi-empirical model and heuristic algorithm

Cited by 18 publications

References 46 publications

Numerical Study on the Steam Gasification of Biomass in a 100 kW Dual Fluidized Bed Gasifier

Numerical Study on the Steam Gasification of Biomass in a 100 kW Dual Fluidized Bed Gasifier

The dimensional design of a laboratory-scale fluidized bed gasifier using machine learning based on a kinetic method

Artificial intelligence and machine learning at various stages and scales of process systems engineering

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