Po‐Chih Kuo 교수

Plasma gasification of raw and torrefied woody, non-woody, and algal biomass using three different gasifying agents (air, steam, and CO₂) 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 CO₂ 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