Mining the synergistic effect in hydrothermal co-liquefaction of real feedstocks through machine learning approaches
Notice bibliographique
Résumé
Hydrothermal co-liquefaction (co-HTL) of different feedstocks has received much research attention, not only because its significant importance in real industrial applications, but also due to the potential synergy in biocrude yield by tuning mixed feedstock’s biochemical composition and reaction conditions. Although some attempts have been made to search for the synergy from co-liquefying various feedstocks, these processes were remarkably time and labor consuming, and often with low rate of success. Therefore, this study for the first time employed machine learning algorithms to mine the synergistic effect in co-HTL. Started with single task prediction, three machine learning algorithms, including Adaboost, Gradient Boosting Regression and Random Forest, were trained and tested for predicting co-HTL biocrude yield and relative co-liquefaction effect (CE). It was found that their prediction performances were favorable over traditional mathematical equations, in which Gradient Boosting Regression exhibited the best performance for co-HTL biocrude yield prediction (training and testing R 2 of 0.976 and 0.812 respectively), and Adaboost better estimated relative CE. Feature importance analysis further revealed that co-HTL biocrude yield was mainly influenced by the reaction temperature, but relative CE was closely related to mixed feedstock’s lipid and carbohydrate content, implying that the synergism/antagonism from co-HTL was more dependent on the biochemical composition of mixed feedstock than reaction conditions. Multitask predictions, estimating biocrude yield and relative CE simultaneously that are usually required in real co-HTL practices, suggested Adaboost was the most satisfying algorithm (training R 2 of 0.922) among studied ones. An optimal relative CE of 22.07 % along with 36.31 wt% (daf) biocrude yield could be obtained when the mixed feedstock contained 42.93 % protein, 50.49 % carbohydrate, 6.58 % lipid at a temperature of 320 °C, which were in well agreement with experimental results from co-HTL of biomass model components. A mini application software (exe. file including machine learning algorithm) was also developed for quick estimation of synergy and co-HTL biocrude yield by simply inputting mixed feedstock’s biochemical composition and reaction conditions, showing promising potential for academic and industrial practices to mine the co-HTL synergy and design processes efficiently.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».