Chen-hui Chen

Mr. Chen-hui Chen

orcid

Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 September 2025 in Preprints.org
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CCChen-hui Chen
Chen-hui Chen
WLWay Long
Way Long
TWTung-Lin Wu
Tung-Lin Wu
JTJeng-Wei Tsai
Jeng-Wei Tsai
YLYanJia Liou
YanJia Liou
CCChuan-Chi Chien
Chuan-Chi Chien
T he objective of this study was to investigate the chlorine-related challenges in Automotive Shredder Residue (ASR) by developing a de-chlorination strategy and formulating Solid Recovered Fuel (SRF) pellets with improved environmental and fuel quality. A ternary blending approach was employed using Fe–Ca-based inorganic dechlorinating agents and thorny bamboo biomass as co-materials with ASR. The de-chlorination efficiency, calorific value, and ash content of the resulting SRF were evaluated. Results indicated that the optimal dechlorinating formulation reduced the chlorine content of PVC from 43.26 wt% to 0.59 wt%, achieving a de-chlorination efficiency of 97.23%. A second-order polynomial regression model ( η_DeCl = –1.5277x² + 2.5519x – 0.0225 , R² = 0.9347 ) was developed to predict the de-chlorination performance based on the blending ratio of dechlorinating agent to ASR, demonstrating behavior consistent with first-order reaction kinetics observed in pyrolytic de-chlorination. The final ternary formulation—comprising 55% thorny bamboo, 37.5% ASR, and 7.5% dechlorinating agent—produced SRF pellets with improved overall quality, demonstrating effective chlorine control, reasonable ash content, and enhanced thermal properties suitable for regulatory compliance and practical application. S uch findings meet the criteria set by EN ISO 21640:2021 (Class 2), JIS Z7311 (Grade A), and forthcoming Taiwanese SRF regulations. B ased on the findings in this work it can be stated that the high de-chlorination potential of Fe–Ca-based additives for chlorine-rich waste and introduces a predictive formulation model that supports both resource circularity and clean fuel production.