Slow pyrolysis as a platform for negative emissions technology: An integration of machine learning models, life cycle assessment, and economic analysis. (1st November 2020)

• Record Type: Journal Article
• Authors: Cheng, Fangwei; Luo, Hongxi; Colosi, Lisa M.
• Is Part Of: Energy conversion and management. Volume 223(2020)
• Publication Date: 2020-11-01
• Subjects: Pyrolysis -- Biochar, Random forest (RF) -- Life cycle assessment (LCA) -- Economic analysis -- Negative emission technologies (NET); 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.2020.113258
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: Machine learning models were developed to predict biochar yield and characteristics from slow pyrolysis. The energy, climate change and economic performance of pyrolysis were evaluated. Lignocellulosic biomass and high temperature of pyrolysis favored both energy and climate change performance. There is a tradeoff between environmental and economic performance. The market price of biochar should be dependent on feedstock properties and pyrolysis temperature. Abstract: Biochar sequestration has gained increasing attention as a negative emissions technology to mitigate climate change. Although pyrolysis is a commercial technology, comprehensive environmental and economic assessments have been difficult to perform since biochar can be produced from a wide range of feedstocks and pyrolysis conditions. Many researchers have evaluated the environmental and economic impacts of biochar-based CO2 sequestration systems. However, most studies either worked on a single type of biomass under varying pyrolysis conditions or multiple feedstocks under the same pyrolysis conditions. To address this knowledge gap, we evaluated the energy, climate change, and economic performance of slow pyrolysis of multiple feedstocks under various processing conditions via the integration of machine learning approaches, life cycle assessment (LCA), and economic analysis. Machine learning models (i.e., random forest) were developed by fitting existing laboratory data. The models were then used to predict the yields and characteristics of biochar produced from slow pyrolysis of different feedstocks under designed processing conditions. The results were further integrated with LCA and economic analysis to compute three important metrics: energy return on investment (EROI), net global warming potential (GWP), and minimum product selling price (MPSP). The results indicate that random forest models offer good prediction accuracy for laboratory-scale (R 2 = 0.78–0.87) and pilot-scale pyrolysis data (R 2 = 0.45–0.65). LCA and economic analyses reveal that feedstock characteristics and pyrolysis temperature affect energy, climate change, and financial performance. Our results demonstrate slow pyrolysis of crop residues and woody wastes holds promise as an energy-producing negative emissions technology, with EROI values from 1.9 to 3.6 (without substitution) and 2.4 to 4.3 (with substitution), and GWP values from −470 kg CO2 eq/t to −200 kg CO2 eq/t (without substitution) and −1050 kg CO2 eq/t to −770 kg CO2 eq/t (with substitution). The MSPS values evaluated in this study range from $774–1256/t, depending on temperature and feedstocks. A tradeoff between environmental and economic performance is observed. The best overall energy and climate change performances are achieved via pyrolysis of lignocellulosic biomass at high temperature, while the best MPSP is achieved with the pyrolysis of sludge at low temperature.