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Valorization of agricultural and food waste digestates is crucial for sustainable waste management to reduce environmental impacts and improve the economics of commercial farms. Hydrothermal liquefaction (HTL) of anaerobic digestates was evaluated to recover resources by converting them into carbon-dense biocrude oil and a nutrient-rich HTL aqueous phase (HTL-AP) coproduct. The effects of HTL temperature (280–360 °C), reaction time (10–50 min), feedstock pH (2.5–8.5), digestate salt content (1–5 wt%), and digestate cellulose-to-lignin ratio (0.2–1.8) on energy and nutrient recovery were systematically investigated in a set of well-designed experiments following a half-fractional central composite protocol. Response surface analysis combined with HTL product characterization and comparative literature study produced a comprehensive reaction pathway for HTL of anaerobic digestates. Moreover, this analysis revealed the importance of acidic feedstocks (pH 3.00–5.53), high reaction temperatures (337–360 °C), and reaction times <45 or 45–50 min for digestates with Cel/Lig >1 or <1, for maximizing the energy recovered in biocrude (high carbon yield and low heteroatom content) and the amounts of P, NH 3 –N, and Mg distributed in the HTL-AP. Acidic conditions catalyzed biocrude production, inhibited the Maillard reaction (lowering the nitrogen content in biocrude), and partitioned nutrients into the HTL-AP. Higher reaction temperatures coupled with longer reaction times activated hydro-denitrogenation and deoxygenation reactions to improve biocrude quality. This work provides not only validated methods to achieve targeted resource recovery for specific feedstock compositions using HTL, but also a comprehensive mechanistic understanding of the HTL of biomass waste for controlling target product characteristics.
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One techno-economic analysis to rule them all: Instant prediction of hydrothermal liquefaction economic performance with a machine learned analytic equation
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.
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- Award ID(s):
- 2021871
- PAR ID:
- 10620866
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Energy Conversion and Management: X
- Volume:
- 24
- Issue:
- C
- ISSN:
- 2590-1745
- Page Range / eLocation ID:
- 100756
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Its ability to upconvert myriad wet carbonaceous wastes into biofuels and platform chemicals makes Hydrothermal Liquefaction (HTL) an attractive process to incorporate into a future bioeconomy. However, while HTL is well suited to process feedstocks with high moisture content, it generates a carbon-laden process water (PW). There is considerable research on the state-of-the-field of HTL; the impact of feedstocks and process conditions on products is well established, as are methods to upgrade recovered biocrudes. However, methods to efficiently separate, recover, and utilize the fugitive carbon in PW are less well understood. We believe this is because of the intrinsic thermodynamic limitations imposed by the PW; PW is a solutropic solution for which liquid-liquid extraction is, depending on the solvent, of minimal utility. Aqueous phase processing and electrocatalytic oxidation could produce high-value products like H2 for biocrude upgrading, though issues of catalyst stability and electrode fouling, along with selectivity and efficiency, plague these nascent technologies. The literature is replete with conflicting opinions on the potential to recycle PW in the reactor (some authors find enhancement of hydrochar or biocrude yield, others no change or a negative impact). The current Edisonian approach to biological treatment (e.g. grow one bacteria on one PW) leaves the field without a clear understanding of the HTL PW compounds that inhibit or promote growth beyond broad classifications. Through this review, we hope to encourage the HTL field to move beyond the current norm of processing singular feedstocks to assess the biocrude produced and consider the carbon balance of the entire system to develop recovery and valorization pathways for the carbon present in HTL PW.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ecmx.2024.100756
