This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimiz...This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.展开更多
Perovskite oxides has been attracted much attention as high-performance oxygen carriers for chemical looping reforming of methane,but they are easily inactivated by the presence of trace H_(2)S.Here,we propose to modu...Perovskite oxides has been attracted much attention as high-performance oxygen carriers for chemical looping reforming of methane,but they are easily inactivated by the presence of trace H_(2)S.Here,we propose to modulate both the activity and resistance to sulfur poisoning by dual substitution of Mo and Ni ions with the Fe-sites of LaFeO_(3)perovskite.It is found that partial substitution of Ni for Fe substantially improves the activity of LaFeO_(3)perovskite,while Ni particles prefer to grow and react with H_(2)S during the long-term successive redox process,resulting in the deactivation of oxygen carriers.With the presence of Mo in LaNi_(0.05)Fe_(0.95)O_(3−σ)perovskite,H_(2)S preferentially reacts with Mo to generate MoS_(2),and then the CO_(2)oxidation can regenerate Mo via removing sulfur.In addition,Mo can inhibit the accumulation and growth of Ni,which helps to improve the redox stability of oxygen carriers.The LaNi_(0.05)Mo_(0.07)Fe_(0.88)O_(3−σ)oxygen carrier exhibits stable and excellent performance,with the CH_(4)conversion higher than 90%during the 50 redox cycles in the presence of 50 ppm H_(2)S at 800℃.This work highlights a synergistic effect in the perovskite oxides induced by dual substitution of different cations for the development of high-performance oxygen carriers with excellent sulfur tolerance.展开更多
Radiant syngas cooler(RSC)is widely used as a waste heat recovery equipment in industrial gasification.In this work,an RSC with radiation screens is established and the impact of gaseous radiative property models,gas ...Radiant syngas cooler(RSC)is widely used as a waste heat recovery equipment in industrial gasification.In this work,an RSC with radiation screens is established and the impact of gaseous radiative property models,gas components,and ash particles on heat transfer is investigated by the numerical simulation method.Considering the syngas components and the pressure environment of the RSC,a modified weighted-sum-of-gray-gases model was developed.The modified model shows high accuracy in validation.In computational fluid dynamics simulation,the calculated steam production is only 0.63%in error with the industrial data.Compared with Smith's model,the temperature decay along the axial direction calculated by the modified model is faster.Syngas components are of great significance to heat recovery capacity,especially when the absorbing gas fraction is less than 10%.After considering the influence of particles,the outlet temperature and the proportion of radiative heat transfer are less affected,but the difference in steam output reaches 2.7 t·h^(-1).The particle deposition on the wall greatly reduces the heat recovery performance of an RSC.展开更多
基金This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 813393the funding from the National Natural Science Foundation of China (No. 52177149)
文摘This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.
基金financially supported by the National Natural Science Foundation of China (Nos. 52174279, U2202251, and 52266008)Applied Basic Research Program of Yunnan Province for Distinguished Young Scholars (No. 202201AV070004)+1 种基金Central Guiding Local Science and Technology Development Fund (No. 202207AA110001)the Yunnan Fundamental Research Projects (No. 202301AU070027, 202401AT070388)
文摘Perovskite oxides has been attracted much attention as high-performance oxygen carriers for chemical looping reforming of methane,but they are easily inactivated by the presence of trace H_(2)S.Here,we propose to modulate both the activity and resistance to sulfur poisoning by dual substitution of Mo and Ni ions with the Fe-sites of LaFeO_(3)perovskite.It is found that partial substitution of Ni for Fe substantially improves the activity of LaFeO_(3)perovskite,while Ni particles prefer to grow and react with H_(2)S during the long-term successive redox process,resulting in the deactivation of oxygen carriers.With the presence of Mo in LaNi_(0.05)Fe_(0.95)O_(3−σ)perovskite,H_(2)S preferentially reacts with Mo to generate MoS_(2),and then the CO_(2)oxidation can regenerate Mo via removing sulfur.In addition,Mo can inhibit the accumulation and growth of Ni,which helps to improve the redox stability of oxygen carriers.The LaNi_(0.05)Mo_(0.07)Fe_(0.88)O_(3−σ)oxygen carrier exhibits stable and excellent performance,with the CH_(4)conversion higher than 90%during the 50 redox cycles in the presence of 50 ppm H_(2)S at 800℃.This work highlights a synergistic effect in the perovskite oxides induced by dual substitution of different cations for the development of high-performance oxygen carriers with excellent sulfur tolerance.
基金supported by the National Natural Science Foundation of China(No.22038011,No.22078257,No.22108213,No.52176142)the China Postdoctoral Science Foundation(2021M692548)+1 种基金the Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(Grant YLU-DNL Fund 2022001)the Young Talent Support Plan of Shaanxi Province。
基金supported by the National Natural Science Foundation of China(21878082).
文摘Radiant syngas cooler(RSC)is widely used as a waste heat recovery equipment in industrial gasification.In this work,an RSC with radiation screens is established and the impact of gaseous radiative property models,gas components,and ash particles on heat transfer is investigated by the numerical simulation method.Considering the syngas components and the pressure environment of the RSC,a modified weighted-sum-of-gray-gases model was developed.The modified model shows high accuracy in validation.In computational fluid dynamics simulation,the calculated steam production is only 0.63%in error with the industrial data.Compared with Smith's model,the temperature decay along the axial direction calculated by the modified model is faster.Syngas components are of great significance to heat recovery capacity,especially when the absorbing gas fraction is less than 10%.After considering the influence of particles,the outlet temperature and the proportion of radiative heat transfer are less affected,but the difference in steam output reaches 2.7 t·h^(-1).The particle deposition on the wall greatly reduces the heat recovery performance of an RSC.
