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 (CO, CH, CO, and H).
View Article and Find Full Text PDFDue to the broad interest in using biochar from biomass pyrolysis for the adsorption of heavy metals (HMs) in wastewater, machine learning (ML) has recently been adopted by many researchers to predict the adsorption capacity (η) of HMs on biochar. However, previous studies focused mainly on developing different ML algorithms to increase predictive performance, and no study shed light on engineering features to enhance predictive performance and improve model interpretability and generalizability. Here, based on a dataset widely used in previous ML studies, features of biochar were engineered-elemental compositions of biochar were calculated on mole basis-to improve predictive performance, achieving test R of 0.
View Article and Find Full Text PDFHydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization.
View Article and Find Full Text PDFBiochar 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, CO adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious.
View Article and Find Full Text PDFHydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil).
View Article and Find Full Text PDFHydrothermal treatment (HTT) is a potential technology for producing biofuel from wet biomass. However, the aqueous phase (AP) is generated inevitably in the process of HTT, and studies are lacking on the detailed exploration of AP properties. Therefore, machine learning (ML) models were built for predicting the pH, total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) of the AP based on biomass feedstock and HTT parameters.
View Article and Find Full Text PDFCo-liquefaction was combined with hydrothermal liquefaction (HTL) aqueous phase (AP) recirculation to improve the practicality of HTL process. The Chlorella powder (CL), soybean straw (SS), and their mixture (CS) with ratio 1:1 were processed at 300 °C for 20 min, and the AP was recirculated four times. The yield of CS bio-crude was increased (from 24.
View Article and Find Full Text PDFHydrothermal liquefaction (HTL) of algae is a promising biofuel production technology. However, it is always difficult and time-consuming to identify the best optimal conditions of HTL for different algae by the conventional experimental study. Therefore, machine learning (ML) algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs, and bio-oil yield (Yield_oil), and the contents of oxygen (O_oil) and nitrogen (N_oil) in bio-oil as outputs.
View Article and Find Full Text PDFThe treatment of wastewater by microalgae has been studied and proved to be effective through previous studies. Due to the small size of microalgae, how to efficiently harvest microalgae from wastewater is a crucial factor restricting the development of algal technologies. Fungi-assisted microalgae bio-flocculation for microalgae harvesting and wastewater treatment simultaneously, which was overlooked previously, has attracted increasing attention in the recent decade due to its low cost and high efficiency.
View Article and Find Full Text PDFBiomass is a type of renewable and sustainable resource that can be used to produce various fuels, chemicals, and materials. Nitrogen (N) in biomass such as microalgae should be reduced if it is used to produce fuels, while the retention of N is favorable if the biomass is processed to yield chemicals or materials with N-containing functional groups. The engineering of the removal and retention of N in hydrochar during hydrothermal carbonization (HTC) of biomass rich in protein is a research hot spot in the past decade.
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