A complete review on the oxygen-containing functional groups of biochar: Formation mechanisms, detection methods, engineering, and applications.

Biochar is a porous carbon material generated by the thermal treatment of biomass under anaerobic or anoxic conditions with wealthy Oxygen-containing functional groups (OCFGs). To date, OCFGs of biochar have been extensively studied for their significant utility in pollutant removal, catalysis, capacitive applications, etc. This review adopted a whole system philosophy and systematically summarizes up-to-date knowledge of formation, detection methods, engineering, and application for OCFGs. The formation mechanisms and detection methods of OCFGs, as well as the relationships between OCFGs and pyrolysis conditions (such as feedstocks, temperature, atmosphere, and heating rate), were discussed in detail. The review also summarized strategies and mechanisms for the oxidation of biochar to afford OCFGs, with the performances and mechanisms of OCFGs in the various application fields (environmental remediation, catalytic biorefinery, and electrode material) being highlighted. In the end, the future research direction of biochar OCFGs was put forward.

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Deep dewatering of refinery oily sludge by Fenton oxidation and its potential influence on the upgrading of oil phase.

Highly efficient dewatering is essential to the reduction and reclamation disposal of oily sludge, which is a waste from the extraction, transportation, and refining of crude oil. How to effectively break the water/oil emulsion is a paramount challenge for dewatering of oily sludge. In this work, a Fenton oxidation approach was adopted for the dewatering of oily sludge. The results show that the oxidizing free radicals originated from Fenton agent effectively tailored the native petroleum hydrocarbon compounds into smaller organic molecules, hence destructing the colloidal structure of oily sludge and decreasing the viscosity as well. Meanwhile, the zeta potential of oily sludge was increased, implying the decrease of repulsive electrostatic force to realize easy coalescence of water droplets. Thus, the steric and electrostatic barriers which restrained the coalescence of dispersed water droplets in water/oil emulsion were removed. With these advantages, the Fenton oxidation approach derived the significant decrease of water content, in which 0.294 kg water was removed from per kilogram oily sludge under the optimal operation condition (i.e., pH value of 3, solid-liquid ratio of 1:10, Fe2+ concentration of 0.4 g/L and H2O2/Fe2+ ratio of 10:1, and reaction temperature of 50 °C). In addition, the quality of oil phase was upgraded after Fenton oxidation treatment accompanying with the degradation of native organic substances in oily sludge, and the heating value of oily sludge was increased from 8680 to 9260 kJ·kg-1, which would facilitate to the subsequent thermal conversion like pyrolysis or incineration. Such results demonstrate that the Fenton oxidation approach is efficient for the dewatering as well as the upgrading of oily sludge.

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar.

Biochar produced from pyrolysis of biomass is a platform porous carbon material that have been widely used in many areas. Specific surface area (SSA) and total pore volume (TPV) are decisive to biochar application in hydrogen uptake, CO2 adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious. Machine learning (ML) was used to effectively aid the prediction and engineering of biochar properties. The prediction of biochar yield, SSA, and TPV was achieved via random forest (RF) and gradient boosting regression (GBR) with test R2 of 0.89-0.94. ML model interpretation indicates pyrolysis temperature, biomass ash, and volatile matter were the most important features to the three targets. Pyrolysis parameters and biomass mixing ratios for biochar production were optimized via three-target GBR model, and the optimum schemes to obtain high SSA and TPV were experimentally verified, indicating the great potential of ML for biochar engineering.