Browsing by Author "Chen, Yuqiu"
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Item A color prediction model for mending materials of the Yuquan Iron Pagoda in China based on machine learning(Heritage Science, 2024-06-06) Liu, Xuegang; Liu, Yuhang; Wang, Ke; Zhang, Yang; Lei, Yang; An, Hai; Wang, Mingqiang; Chen, YuqiuDuring the restoration of iron cultural relics, the removal of rust from these artifacts is necessary. However, this rust removal process may lead to inconsistent local color on the iron relics. To address this, mending materials are applied to treat the surface, ensuring consistent local color. In the surface treatment of iron cultural relics, a significant challenge lies in modulating the color of these mending materials. The corrosion products of Yuquan Iron Pagoda are mainly Fe3O4, γ-FeO(OH), α-FeO(OH) and α-Fe2O3, with contents of 13.1, 16.1, 40.2 and 30.6%, respectively. Due to their structural stability and suitable color characteristics, Fe3O4 and α-Fe2O3 are selected as the primary raw materials for the repair material. This study employs machine learning methods to predict the color of mending materials corresponding to varying contents of α-Fe2O3, Fe3O4, and epoxy resin. The Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boost Machine (LightGBM) algorithms are utilized to develop the model, and the predictive performance of these three algorithms is compared. XGBoost exhibits the best prediction performance, achieving a square correlation coefficient (R2) of 0.94238 and a mean absolute error (MAE) of 0.68485. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to analyze the most crucial raw material affecting the color of mending materials, which is identified as Fe3O4. The study illustrates the specific process of employing this model by applying it to the surface treatment of the Yuquan Iron Pagoda, demonstrating the practicality of the model. This model can be applied to assist in the surface treatment of other iron cultural relics.Item Ethylene production: process design, techno-economic and life-cycle assessments(Green Chemistry, 2024-01-29) Chen, Yuqiu; Kuo, Mi Jen; Lobo, Raul; Ierapetritou, MarianthiReplacing the steam cracking process with oxidative dehydrogenation for ethylene production offers potential energy and environmental benefits. To evaluate these possibilities, a study combining conceptual process design, techno-economic analysis, and life cycle assessments of the oxidative dehydrogenation of ethane (ODHE) for producing ethylene at an industrial scale is performed. For comparison, the conventional steam cracking process of ethane is also simulated and optimized. The techno-economic analysis results for ODHE with a boron-containing zeolite chabazite (B-CHA) catalyst, as developed in our group, demonstrate that it is economically competitive ($790 per t ethylene production) compared to the steam cracking process ($832 per t ethylene production). However, a “cradle-to-gate” life-cycle assessment shows that the ODHE process emits more greenhouse gases (2.42 kg CO2 equiv. per kg ethylene) compared to the steam cracking counterpart (1.34 kg CO2 equiv. per kg ethylene). The discrepancy between the initial hypothesis and the results arises from the significant refrigerant input required by the ODHE process to recover ethylene from byproducts such as CO, CH4, and unreacted oxygen and ethane. Further scenario analysis reveals that plausible improvements in the C2H6 conversion per pass, the selectivity to ethylene and the ratio of ethane to oxygen in the current ODHE process could render it both economically and environmentally viable as a replacement for the steam cracking process.Item Ionic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization(AIChE Journal, 2024-04-15) Chen, Yuqiu; Ma, Sulei; Lei, Yang; Liang, Xiaodong; Liu, Xinyan; Kontogeorgis, Georgios M.; Gani, RafiqulThis work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)-IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML-based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML-based GC models are sequentially integrated into computer-aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL-IL binary mixtures in practical applications.