Abstract This paper evaluates the feasibility of hydrothermal treatment (HTT) with carbon capture and storage (CCS) as an energy producing negative emissions technology (NET) and compares such system with a conventional bioenergy with carbon capture and sequestration (BECCS) system. Machine learning models were developed to predict product yields and characteristics from HTT of various feedstocks. The model results were then integrated into a life cycle assessment (LCA) model to compute two metrics: energy return on investment (EROI) and net global warming potential (GWP). Results showed random forest models had better prediction accuracy than regression tree and multiple linear regression to model HTT of feedstocks (e.g., microalgae, crops/forest residues, energy crops, and biodegradable organic wastes) and predicted the mass yields of multiple products (biocrude, hydrochar, gas, and aqueous co product) as well as the energy and carbon contents of biocrude and hydrochar. LCA results revealed that the proposed HTT-CCS system constituted a net-energy producing NET for some combinations of feedstock characteristics and reaction conditions. Best overall energy and GWP performance was achieved for HTT-CCS of lignocellulosic biomass at low temperature. Compared with the conventional BECCS system, HTT-CCS generally exhibited higher EROI but higher net GWP, depending on processing conditions and the feedstock types.
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Climate change Research Articles published between Oct 11, 2021 to Oct 17, 2021
Oct 18, 2021
Articles Included: 5
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