The effects of various factors on the electrocarboxylation of organic compounds with carbon dioxide catalyzed by metalloporphyrin are studied.The optimal potential of electrocar- boxylation is -1.6 V(vs.SCE).A weak pr...The effects of various factors on the electrocarboxylation of organic compounds with carbon dioxide catalyzed by metalloporphyrin are studied.The optimal potential of electrocar- boxylation is -1.6 V(vs.SCE).A weak protic solvent methanol can enhance catalytic activity. Tetrabutylammonium iodide is the best one of five electrolytes.The yields and current efficiencies of electrocarboxylation are increased slowly as the concentration of catalyst increases.展开更多
Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity an...Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.展开更多
文摘The effects of various factors on the electrocarboxylation of organic compounds with carbon dioxide catalyzed by metalloporphyrin are studied.The optimal potential of electrocar- boxylation is -1.6 V(vs.SCE).A weak protic solvent methanol can enhance catalytic activity. Tetrabutylammonium iodide is the best one of five electrolytes.The yields and current efficiencies of electrocarboxylation are increased slowly as the concentration of catalyst increases.
基金supported by the Yalong River Joint Funds of the National Natural Science Foundation of China(No.U1965207)the National Natural Science Foundation of China(Nos.51839007,51779169,and 52009090)。
文摘Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.
