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Hydrothermal liquefaction (HTL) has remarkable potential for efficient conversion of abundant, decentralized organic wastes into renewable fuels. Because waste is a highly distributed resource with context-dependent economic viability, selection of optimal deployment sites is slowed by the need to develop detailed techno-economic analyses (TEA) for the thousands of potential deployment locations, each with their own unique combinates of scale, proximity to infrastructure/markets, and feedstock properties. An economic modeling framework that requires only easily obtainable inputs for assessing economic performance would therefore allow multiplexed analysis of many thousands of cases, whereas traditional TEA would not be possible for more than a handful of cases. Within such a context, the present study uses machine learning to guide development of a TEA and modeling framework which provides accurate cost predictions using three key inputs – feedstock cost, biocrude yield, and process scale – to estimate the minimum fuel selling price (MFSP) that an HTL process can achieve. The structure of the proposed framework is informed and based on empirical observations of cost projections made by a detailed TEA over a wide range of feedstock costs, biocrude yields, and process scales. A machine learning guided process was used to identify, train, and test a series of models using auto-generated data for training and independently reported data for testing. The most accurate model consists of three terms and requires 6 adjustable parameters to predict independently published values of MFSP (N = 28) to within an average value of ± 20.4%. It is demonstrated that the reduced-order model’s predictions fall within 40% of the corresponding published values 95% of the time, and in the worst case, the associated discrepancy is 45.9%, suggesting that the accuracy of the machine learned model is indeed comparable to the TEAs that were used to build it. Moreover, the terms in the model are physically interpretable, conferring greater reliability to the use of its predictions. The model can be used to predict the dependence of MSFP on biocrude yield, scale, and feedstock cost; interestingly, MFSP is insensitive to biocrude yield and/or scale under many situations of interest and identifying the critical value for a given application is crucial to optimizing economic performance. The proposed model can be also extended to evaluate economic performance of newly developed HTL-based processes, including catalytic HTL, and the methodological framework used in this study is deemed appropriate for the development of machine learned TEA models in cases of other similar waste-to-energy technologies. 

Less than 5% of polystyrene is recycled, motivating a search for energy efficient and economical methods for polystyrene recycling that can be deployed at scale. One option is chemical recycling, consisting of thermal depolymerization and purification to produce monomer-grade styrene (>99%) and other co-products. Thermal depolymerization and distillation are readily scalable, well-established technologies; however, to be considered practical, they must be thermodynamically efficient, economically feasible, and environmentally responsible. Accordingly, mass and energy balances of a pyrolysis reactor for thermal depolymerization and two distillation columns to separate styrene from α-methyl styrene, styrene dimer, toluene, and ethyl benzene co-products, were simulated using ASPEN to evaluate thermodynamic and economic feasibility. These simulations indicate that monomer-grade styrene can be recovered with energy inputs <10MJ/kg, comparable to the energy content of pyrolysis co-products. Thermodynamic sensitivity analysis indicates the scope to reduce these values and enhance the robustness of the predictions. A probabilistic economic analysis of multiple scenarios combined with detailed sensitivity analysis indicates that the cost for recycled styrene is approximately twice the historical market value of fossil-derived styrene when styrene costs are fixed at 15% of the total product cost or less than the historical value when feedstock costs are assumed to be zero. A Monte Carlo and Net Present Value-based economic performance analysis indicates that chemical recycling is economically viable for scenarios assuming realistic feedstock costs. Furthermore, the CO2 abatement cost is roughly $1.5 per ton of averted CO2, relative to a pyrolysis process system to produce fuels. As much as 60% of all polystyrene used today could be replaced by chemically recycled styrene, thus quantifying the potential benefits of this readily scalable approach.