Mining the synergistic effect in hydrothermal co-liquefaction of real feedstocks through machine learning approaches

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 R2 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 R2 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 degrees 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.

Automated summary

This study employed machine learning algorithms to mine the synergistic effect in co-HTL for the first time. The algorithms showed favorable prediction performances for co-HTL biocrude yield and relative co-liquefaction effect. The study also developed a mini application software for quick estimation of synergy and co-HTL biocrude yield.