This study presents the design,construction,and thermal evaluation of a solar-powered cocoa roaster based on a Parabolic Cylinder Collector(PCC)with dual-axis solar tracking.The system integrates three functional subs...This study presents the design,construction,and thermal evaluation of a solar-powered cocoa roaster based on a Parabolic Cylinder Collector(PCC)with dual-axis solar tracking.The system integrates three functional subsystems:the cylindrical-parabolic reflecting surface,the stainless-steel absorber tube,and a microcontrollerbased tracking mechanism.The prototype enables continuous acquisition of key thermal variables(solar irradiance,ambient temperature,absorber surface temperature,and bean temperature),allowing a detailed characterization of heat transfer processes during roasting.Roasting experiments were conducted at controlled durations of 40,55,and 70 min between 10:00 and 14:00 h.Maximum roasting temperatures of 125℃–137℃ were reached under average irradiance levels of 685.7–930.5 W m−2.The lowest final moisture content was 2.19%,within the recommended range for high-quality cocoa.Longer roasting durations promoted thermal energy accumulation within the absorber tube,enhancing convective and radiative heat transfer to the bean mass even under fluctuating irradiance.The experimental trends reveal a strong coupling between irradiance variability,absorber temperature,and internal air-beam heat transfer.Comparison with reference parabolic trough collector studies indicate that,although the process-level roasting efficiency(3.83%–7.45%)is lower than conventional collector-level thermal efficiencies,the operating temperatures and moisture-reduction rates align with the thermal requirements of food-processing systems rather than high-enthalpy solar applications.These results also demonstrate the potential of coupling PCC-based solar concentration with lowtemperature convective–radiative roasting processes.Overall,the findings confirm the feasibility of implementing PCC-based roasting technologies in rural or off-grid regions,where solar-driven heat transfer offers a sustainable,low-cost alternative to fossil-fuel-based roasting systems,enabling a controlled thermophysical environment for cocoa transformation.展开更多
With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roast...With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.展开更多
基金the Program for Teaching Development(PRODEP)for funding the project UJAT-PTC-251(Development and Evaluation of a Cocoa Roaster in the Tabasco Region).
文摘This study presents the design,construction,and thermal evaluation of a solar-powered cocoa roaster based on a Parabolic Cylinder Collector(PCC)with dual-axis solar tracking.The system integrates three functional subsystems:the cylindrical-parabolic reflecting surface,the stainless-steel absorber tube,and a microcontrollerbased tracking mechanism.The prototype enables continuous acquisition of key thermal variables(solar irradiance,ambient temperature,absorber surface temperature,and bean temperature),allowing a detailed characterization of heat transfer processes during roasting.Roasting experiments were conducted at controlled durations of 40,55,and 70 min between 10:00 and 14:00 h.Maximum roasting temperatures of 125℃–137℃ were reached under average irradiance levels of 685.7–930.5 W m−2.The lowest final moisture content was 2.19%,within the recommended range for high-quality cocoa.Longer roasting durations promoted thermal energy accumulation within the absorber tube,enhancing convective and radiative heat transfer to the bean mass even under fluctuating irradiance.The experimental trends reveal a strong coupling between irradiance variability,absorber temperature,and internal air-beam heat transfer.Comparison with reference parabolic trough collector studies indicate that,although the process-level roasting efficiency(3.83%–7.45%)is lower than conventional collector-level thermal efficiencies,the operating temperatures and moisture-reduction rates align with the thermal requirements of food-processing systems rather than high-enthalpy solar applications.These results also demonstrate the potential of coupling PCC-based solar concentration with lowtemperature convective–radiative roasting processes.Overall,the findings confirm the feasibility of implementing PCC-based roasting technologies in rural or off-grid regions,where solar-driven heat transfer offers a sustainable,low-cost alternative to fossil-fuel-based roasting systems,enabling a controlled thermophysical environment for cocoa transformation.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(62394340 and 62073340)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083).
文摘With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.
