Iron corrosion in acidic media is a natural phenomenon that converts elemental iron to a more chemically-stable form,i.e.its oxide and hydroxide.In this study,the iron corrosion process is modeled as a completely impl...Iron corrosion in acidic media is a natural phenomenon that converts elemental iron to a more chemically-stable form,i.e.its oxide and hydroxide.In this study,the iron corrosion process is modeled as a completely implicit problem,solved by a novel finite difference model to provide insight into the ionic aspects of corrosion behavior.This new mathematical model eliminates the chemical potential parameters from the corrosion process equations,thereby reducing the need for experimental determination of chemical potentials.The eliminatedchemical-potential-parameters model predicts and quantifies key parameters(concentrations of conjugate base ion,iron(Ⅱ)ion,hydrogen ion,anodic and cathodic potentials,and the electrical current density)associated with the iron corrosion process in acidic solutions.The rigorous derivation and novel application of the eliminated-chemical-potential-parameters model and its results provide new insights into the iron corrosion process.The present model is also applicable in any industrial process which is associated with metal corrosion.The model helps to guide the design of future corrosion resistant systems,and various experimental studies pertaining to corrosion inhibition techniques.展开更多
Floating Storage and Regasification Units(FSRU)form a rapidly expanding sector of LNG business.In many cases,FSRU now provide a more cost-effective and very flexible way to deliver natural gas to end users in comparis...Floating Storage and Regasification Units(FSRU)form a rapidly expanding sector of LNG business.In many cases,FSRU now provide a more cost-effective and very flexible way to deliver natural gas to end users in comparison with shore-based terminals.Due to enhanced operations FSRU are more complex compared to LNG carriers(LNGC).FSRU are essentially merge of the attributes of shore-based terminals and LNGC.The existing FSRU fleet is formed of new-build vessels and converted LNGC.Together with their advantages FSRU come with the inherent problems of handling and storing LNG.Here we focus on the rollover issues that occur on FSRU and suggest ways to improve handling to minimize the impacts of those events.Rollover is a physical mixing process in a single tank with two or more different parcels of LNG of different compositions,temperatures and densities that can manifest in large boil-off rates,beyond handling-equipment capacities,and large tank pressure increases culminating rapidly.If prevention/mitigation actions are not implemented,uncontrolled venting of boil off gas in vapor form to the atmosphere is a likely consequence involving flammability hazards and tank structure over-pressurization with potential damage.This study provides in-depth analysis of FSRU rollovers based on observations of more than twenty rollovers on many different FSRU.The analysis focuses on LNG saturated vapor pressure(SVP)rather than the traditional approach of focusing on boil-off rate(BOG).This approach allows efficient rollover management without any in-built rollover prevention means.Strategies are developed for managing a combination of FSRU tanks utilizing rollover prevention and mitigation actions,as well as efficient pre-planning for LNG stock management.Novel rules of thumb for predicting time to rollover onset,based on many observed FSRU rollovers provide operators with real-time insight to what rollover preventive and mitigating actions are effective in specific circumstances.展开更多
An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents.It is applied to a well-studied forest-fire dataset from Por...An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents.It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables.The total burned area distribution of the 517 burn events in that dataset is highly positively skewed.The model is transparent and avoids regressions and hidden layers.This increases its detailed datamining capabilities.It matches the highest burned-area prediction accuracy achieved for this datasetwith a wide range of traditionalmachine learning algorithms.The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions.Optimizing with mean absolute error(MAE)and root mean square error(RMSE)as separate objective functions provides complementary information with which to data mine each total burnedarea incident.Such insight offers potential agricultural,ecological,environmental and forestry benefits by improving the understanding of the key influences associated with each burn event.Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types.Such prediction accuracy and insight leads to confidence in how each prediction is derived.It provides knowledge to make appropriate responses and mitigate specific burn incidents,as they occur.Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.展开更多
文摘Iron corrosion in acidic media is a natural phenomenon that converts elemental iron to a more chemically-stable form,i.e.its oxide and hydroxide.In this study,the iron corrosion process is modeled as a completely implicit problem,solved by a novel finite difference model to provide insight into the ionic aspects of corrosion behavior.This new mathematical model eliminates the chemical potential parameters from the corrosion process equations,thereby reducing the need for experimental determination of chemical potentials.The eliminatedchemical-potential-parameters model predicts and quantifies key parameters(concentrations of conjugate base ion,iron(Ⅱ)ion,hydrogen ion,anodic and cathodic potentials,and the electrical current density)associated with the iron corrosion process in acidic solutions.The rigorous derivation and novel application of the eliminated-chemical-potential-parameters model and its results provide new insights into the iron corrosion process.The present model is also applicable in any industrial process which is associated with metal corrosion.The model helps to guide the design of future corrosion resistant systems,and various experimental studies pertaining to corrosion inhibition techniques.
文摘Floating Storage and Regasification Units(FSRU)form a rapidly expanding sector of LNG business.In many cases,FSRU now provide a more cost-effective and very flexible way to deliver natural gas to end users in comparison with shore-based terminals.Due to enhanced operations FSRU are more complex compared to LNG carriers(LNGC).FSRU are essentially merge of the attributes of shore-based terminals and LNGC.The existing FSRU fleet is formed of new-build vessels and converted LNGC.Together with their advantages FSRU come with the inherent problems of handling and storing LNG.Here we focus on the rollover issues that occur on FSRU and suggest ways to improve handling to minimize the impacts of those events.Rollover is a physical mixing process in a single tank with two or more different parcels of LNG of different compositions,temperatures and densities that can manifest in large boil-off rates,beyond handling-equipment capacities,and large tank pressure increases culminating rapidly.If prevention/mitigation actions are not implemented,uncontrolled venting of boil off gas in vapor form to the atmosphere is a likely consequence involving flammability hazards and tank structure over-pressurization with potential damage.This study provides in-depth analysis of FSRU rollovers based on observations of more than twenty rollovers on many different FSRU.The analysis focuses on LNG saturated vapor pressure(SVP)rather than the traditional approach of focusing on boil-off rate(BOG).This approach allows efficient rollover management without any in-built rollover prevention means.Strategies are developed for managing a combination of FSRU tanks utilizing rollover prevention and mitigation actions,as well as efficient pre-planning for LNG stock management.Novel rules of thumb for predicting time to rollover onset,based on many observed FSRU rollovers provide operators with real-time insight to what rollover preventive and mitigating actions are effective in specific circumstances.
文摘An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents.It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables.The total burned area distribution of the 517 burn events in that dataset is highly positively skewed.The model is transparent and avoids regressions and hidden layers.This increases its detailed datamining capabilities.It matches the highest burned-area prediction accuracy achieved for this datasetwith a wide range of traditionalmachine learning algorithms.The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions.Optimizing with mean absolute error(MAE)and root mean square error(RMSE)as separate objective functions provides complementary information with which to data mine each total burnedarea incident.Such insight offers potential agricultural,ecological,environmental and forestry benefits by improving the understanding of the key influences associated with each burn event.Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types.Such prediction accuracy and insight leads to confidence in how each prediction is derived.It provides knowledge to make appropriate responses and mitigate specific burn incidents,as they occur.Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.
