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Modeling of a heat-integrated biomass downdraft gasifier: Estimating key model parameters using experimental data
Energy Conversion and Management ( IF 9.9 ) Pub Date : 2024-12-13 , DOI: 10.1016/j.enconman.2024.119372 Houda M. Haidar, James W. Butler, Samira Lotfi, Anh-Duong Dieu Vo, Peter Gogolek, Kimberley McAuley
Energy Conversion and Management ( IF 9.9 ) Pub Date : 2024-12-13 , DOI: 10.1016/j.enconman.2024.119372 Houda M. Haidar, James W. Butler, Samira Lotfi, Anh-Duong Dieu Vo, Peter Gogolek, Kimberley McAuley
Kinetic and transport parameters in a model of a heat-integrated biomass downdraft gasifier are poorly known and require estimation. The large number of parameters (40) arises from pyrolysis, combustion, and gasification reactions, as well as heat-transfer phenomena inside the gasifier and associated heat-integration system. Due to complexity of the model and the limited available data, only a subset of the parameters can be reliably estimated. A sensitivity-based approach is used to determine the appropriate number of parameters to estimate while preventing overfitting. It is hypothesized that estimating these important parameters will result in better model predictions. The 40 parameters are ranked from most-estimable to least-estimable based on sensitivity information and initial parameter uncertainties. A mean-squared-error criterion is then used to determine that 27 parameters should be estimated using data from 15 experimental runs, with the remaining 13 parameters fixed at their initial values. A diagnosis of the 13 low-ranked parameters reveals that 8 parameters are not estimated due to correlation with high-ranked parameters and that the remaining 5 parameters have little influence on model predictions. The model is validated using two runs not used for parameter tuning. The updated model is used to predict that a taller gasifier would not improve the quality of the producer gas. Simulations show that increasing the producer-gas demand by 50% results in a 15.2% decrease in H2 /CO ratio, a 52.6% increase in tar content in the producer gas, and a 44% increase in electrical energy output.
中文翻译:
热集成生物质下吸式气化炉建模:使用实验数据估计关键模型参数
热集成生物质下吸式气化炉模型中的动力学和输运参数知之甚少,需要估计。大量参数 (40) 来自热解、燃烧和气化反应,以及气化炉和相关的热集成系统内部的传热现象。由于模型的复杂性和有限的可用数据,只能可靠地估计参数的子集。使用基于灵敏度的方法来确定要估计的适当参数数量,同时防止过拟合。据推测,估计这些重要参数将导致更好的模型预测。这 40 个参数根据敏感性信息和初始参数不确定性从最可估计到最不可估计进行排序。然后使用均方误差标准来确定应使用来自 15 次实验运行的数据估计 27 个参数,其余 13 个参数固定在其初始值。对 13 个低秩参数的诊断表明,由于与高秩参数的相关性,8 个参数未被估计,其余 5 个参数对模型预测的影响很小。该模型使用两次不用于参数调整的运行进行验证。更新后的模型用于预测更高的气化炉不会提高生产炉气的质量。模拟表明,将生产者气体需求增加 50% 会导致 H2/CO 比率降低 15.2%,生产气中的焦油含量增加 52.6%,电能输出增加 44%。
更新日期:2024-12-13
中文翻译:
热集成生物质下吸式气化炉建模:使用实验数据估计关键模型参数
热集成生物质下吸式气化炉模型中的动力学和输运参数知之甚少,需要估计。大量参数 (40) 来自热解、燃烧和气化反应,以及气化炉和相关的热集成系统内部的传热现象。由于模型的复杂性和有限的可用数据,只能可靠地估计参数的子集。使用基于灵敏度的方法来确定要估计的适当参数数量,同时防止过拟合。据推测,估计这些重要参数将导致更好的模型预测。这 40 个参数根据敏感性信息和初始参数不确定性从最可估计到最不可估计进行排序。然后使用均方误差标准来确定应使用来自 15 次实验运行的数据估计 27 个参数,其余 13 个参数固定在其初始值。对 13 个低秩参数的诊断表明,由于与高秩参数的相关性,8 个参数未被估计,其余 5 个参数对模型预测的影响很小。该模型使用两次不用于参数调整的运行进行验证。更新后的模型用于预测更高的气化炉不会提高生产炉气的质量。模拟表明,将生产者气体需求增加 50% 会导致 H2/CO 比率降低 15.2%,生产气中的焦油含量增加 52.6%,电能输出增加 44%。