(379a) Kinetic Modeling in Mixing Feedstocks of Hydrothermal Liquefaction

What society terms “waste” today could be viewed as a resource to valorize rather than material to discard. Converting “waste” into value-added products moves us closer to a circular economy and environmental sustainability. The carbon-containing materials that are landfilled today could be converted to more energy-dense oils that can then be processed to make fuels or chemicals. These carbon-containing materials include biomass (e.g., paper, sludges, yard trimmings, food waste) and waste plastics. These materials can be valorized by breaking down the biopolymers and synthetic polymers in hot compressed liquid water (~ 350 °C). This method, termed hydrothermal liquefaction (HTL), is particularly well suited for handling complex wet wastes such as the organic materials in municipal solid waste (MSW). The combined action of thermal energy, pressure, and the presence of water molecules can cleave many of the bonds that hold together natural and synthetic polymers. The literature provides reaction engineering models for HTL of whole biomass (e.g., microalgae) and some individual biochemical components (e.g., proteins, polypeptides). These models provide predictions of oil yield, for example, from HTL of biomass, with time, temperature, and biochemical composition of the feedstock as input parameters. This presentation will describe new reaction engineering models for HTL of mixtures of biomass and synthetic polymers. Initial work was with a ternary mixture of cellulose, polycarbonate, and polypropylene. We will present experimental and modeling results that demonstrate the ability of the model to predict outcomes for HTL of the ternary mixture based solely on HTL experiments with the individual components. The ultimate goal is to develop a model that can faithfully predict oil yields and composition from HTL of any combination of any types of biomass and plastics. Such a model would be invaluable for technoeconomic analyses and life-cycle assessments.