Pub Date : 2025-12-05DOI: 10.53941/sce.2025.100007
Zuo-Qian Jihou, Xiaoqi Liu, Hui‐Long Wei, Zheng-Hong Luo
1,4-Butynediol (BYD), an essential intermediate for fine chemicals and polymer production, is primarily synthesized via formaldehyde-acetylene reaction. Kinetic experiments were conducted in a quasi-industrial slurry bed reactor under the conditions of 55–85 °C, 0–10.5 h, and pH 5–9 to obtain the time-resolved yield of 1,4-butynediol and the conversion of formaldehyde. The experimental results revealed that pH was a critical influencing factor on reaction performance, while it can not be directly coupled into mechanistic kinetic models. Therefore, four machine learning models, i.e., random forest (RF), extremely randomized trees (Extra Trees, ET), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) were employed to establish data-driven models that can directly capture the pH influence. The 84 experimental data points were augmented to 1023 samples by interpolation and extrapolation method, then the dataset was split into training, validation, and testing subsets in a 6:2:2 ratio. The training results demonstrated that the XGBoost model exhibited the best generalization ability and stability, achieving the highest average coefficient of determination (R2) for formaldehyde conversion (0.9847 ± 0.0022) and 1,4-butynediol yield (0.9773 ± 0.0035), and the mean absolute error for both targets was less than 0.027. Finally, the XGBoost model was coupled with Bayesian optimization to search the optimal process parameters.
{"title":"Data-Driven Modeling and Bayesian Optimization for the Formaldehyde-Acetylene Reaction in a Slurry Bed Reactor","authors":"Zuo-Qian Jihou, Xiaoqi Liu, Hui‐Long Wei, Zheng-Hong Luo","doi":"10.53941/sce.2025.100007","DOIUrl":"https://doi.org/10.53941/sce.2025.100007","url":null,"abstract":"1,4-Butynediol (BYD), an essential intermediate for fine chemicals and polymer production, is primarily synthesized via formaldehyde-acetylene reaction. Kinetic experiments were conducted in a quasi-industrial slurry bed reactor under the conditions of 55–85 °C, 0–10.5 h, and pH 5–9 to obtain the time-resolved yield of 1,4-butynediol and the conversion of formaldehyde. The experimental results revealed that pH was a critical influencing factor on reaction performance, while it can not be directly coupled into mechanistic kinetic models. Therefore, four machine learning models, i.e., random forest (RF), extremely randomized trees (Extra Trees, ET), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) were employed to establish data-driven models that can directly capture the pH influence. The 84 experimental data points were augmented to 1023 samples by interpolation and extrapolation method, then the dataset was split into training, validation, and testing subsets in a 6:2:2 ratio. The training results demonstrated that the XGBoost model exhibited the best generalization ability and stability, achieving the highest average coefficient of determination (R2) for formaldehyde conversion (0.9847 ± 0.0022) and 1,4-butynediol yield (0.9773 ± 0.0035), and the mean absolute error for both targets was less than 0.027. Finally, the XGBoost model was coupled with Bayesian optimization to search the optimal process parameters.","PeriodicalId":34892,"journal":{"name":"Huagong Xuebao/CIESC Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://media.sciltp.com/articles/2512002419/2512002419.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147333430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.4018/979-8-3693-0003-9.ch016
Phan Minh-Thu
The increasing of industrial waste and soil contamination has raised so many concerns for the environment and the sustainable development of society. Recently, ultrasound (US) technology has emerged as the new tool for enhancing the recovery process of mineral waste and also for the soil washing process, making US highly efficient cleaning soil or extracting pollutants from waste soils. The unique and highly efficient US action is attributed to acoustic cavitation phenomena in ultrasound circumstance, cleaning and extraction can be more efficient than usual one. This chapter reviews the application of high-power US for eliminating pollutants from contaminant soil and US-extracting valuable metals from mineral wastes. Since US washing technology enhances the recovery process from unusual wastes, this also describes an upcycle technology that uses US cleaning to convert them into valuable resources.
{"title":"Extracting Technology Upcycling Toward Useful Metallic Materials From Mineral Wastes and Pollutant Soil by Ultrasound Washing","authors":"Phan Minh-Thu","doi":"10.4018/979-8-3693-0003-9.ch016","DOIUrl":"https://doi.org/10.4018/979-8-3693-0003-9.ch016","url":null,"abstract":"The increasing of industrial waste and soil contamination has raised so many concerns for the environment and the sustainable development of society. Recently, ultrasound (US) technology has emerged as the new tool for enhancing the recovery process of mineral waste and also for the soil washing process, making US highly efficient cleaning soil or extracting pollutants from waste soils. The unique and highly efficient US action is attributed to acoustic cavitation phenomena in ultrasound circumstance, cleaning and extraction can be more efficient than usual one. This chapter reviews the application of high-power US for eliminating pollutants from contaminant soil and US-extracting valuable metals from mineral wastes. Since US washing technology enhances the recovery process from unusual wastes, this also describes an upcycle technology that uses US cleaning to convert them into valuable resources.","PeriodicalId":34892,"journal":{"name":"Huagong Xuebao/CIESC Journal","volume":"1 1","pages":"425-454"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147909049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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