Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology?. (1st January 2020)

• Record Type: Journal Article
• Authors: Cheng, Fangwei; Porter, Michael D.; Colosi, Lisa M.
• Is Part Of: Energy conversion and management. Volume 203(2020)
• Publication Date: 2020-01-01
• Subjects: Hydrothermal treatment -- Machine learning -- Life cycle assessment (LCA) -- Energy return on investment (EROI) -- Bioenergy with carbon capture and storage (BECCS); Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.112252
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: Machine learning models were developed to predict products information from HTT. Random forest models give best prediction accuracy out of evaluated approaches. Machine learning outputs were fed into LCA to assess HTT-CCS system. HTT-CCS is energy-producing and net carbon-sequestering under some conditions. Lignocellulosic biomass and low reaction temperature are the most favorable for HTT-CCS. 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.