The University of Tokyo · Engineering
Professor Po-Chih Kuo's research lab specializes in sustainable energy systems, with a focus on advanced waste-to-energy technologies, plasma-based gasification, and carbon capture for negative emissions. The lab investigates the thermodynamic, exergetic, and environmental performance of biomass and waste-derived fuels—particularly through torrefaction and plasma gasification—integrated with fuel cell and combined heat and power systems. Key research directions include optimizing syngas and hydrogen production, enhancing energy efficiency, and developing machine learning models for emissions prediction in power generation. The lab also explores innovative pathways for carbon capture using on-board solid oxide fuel cells in transportation, contributing to climate change mitigation.
Figures are computed from collected data and may differ slightly.
Plasma gasification of raw and torrefied woody, non-woody, and algal biomass using three different gasifying agents (air, steam, and CO<sub>2</sub>) is conducted through a thermodynamic analysis. The impacts of feedstock and reaction atmosphere on various performance indices such as syngas yield, pollutant emissions, plasma energy to syngas production ratio (PSR), and plasma gasification efficiency (PGE) are studied. Results show that CO<sub>2</sub> plasma gasification gives the lowest PSR, ther
In this study, an energy, exergy, and environmental (3E) analyses of a plasma-assisted hydrogen production process from microalgae is investigated. Four different microalgal biomass fuels, namely, raw microalgae (RM) and three torrefied microalgal fuels (TM200, TM250, and TM300), are used as the feedstock for steam plasma gasification to generate syngas and hydrogen. The effects of steam-to-biomass (S/B) ratio on the syngas and hydrogen yields, and energy and exergy efficiencies of plasma gasifi
Waste-to-energy (WTE) conversion technologies for generating renewable energy and solving the environmental problems have an important role in the development of sustainable circular economy. This paper presents a novel high-efficiency WTE power plant using refuse-derived fuel (RDF) as feedstock by integrating torrefaction (T) pretreatment with plasma gasifier (PG), solid oxide fuel cell (SOFC), and combined heat and power (CHP) system. The combined impacts of torrefaction conditions (i.e. tempe
Negative emission technologies have recently received increasing attention due to climate change and global warming. One among them is bioenergy with carbon capture and storage (BECCS), but the capture process is very energy intensive. Here, a novel pathway is introduced, based on second-generation biofuels followed by carbon circulation in an indefinitely closed chain, effectively resulting in a sink. Instead of using an energy-intensive conventional CCS process, the application of an on-board
Because of their compact structure, ease of processing and higher heat transfer coefficient, curved-tube heat exchangers are widely applied in various industry applications, such as nuclear power systems, solar-powered engineering, aircraft engine cooling systems and refrigeration and cryogenic systems. Accurate knowledge about the heat transfer characteristics of the supercritical fluids in the tube is critical to the design and optimization of a curved-tube heat exchanger. The available litera
Four machine learning (ML) models including a deep neural network, a long short‐term memory network, a random forest (RF), and an extreme gradient boosting are implemented to predict CO–NO x emissions from a natural gas power plant. A new feature optimization scheme (FOS) via a sequencing process of feature selection and hyperparameter optimization can intensify the ML models. Through the procedures of training, validation, and testing, reliable ML models need to take high prediction accuracy an
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