Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants

This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhou, Yongyue [verfasserIn] Ren, Yangmin [verfasserIn] Cui, Mingcan [verfasserIn] Guo, Fengshi [verfasserIn] Sun, Shiyu [verfasserIn] Ma, Junjun [verfasserIn] Han, Zhengchang [verfasserIn] Khim, Jeehyeong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence

Übergeordnetes Werk:	Enthalten in: The chemical engineering journal - Amsterdam : Elsevier, 1997, 478
Übergeordnetes Werk:	volume:478

DOI / URN:	10.1016/j.cej.2023.147266

Katalog-ID:	ELV065907655

Internformat


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245	1	0	\|a Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
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520			\|a This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload.
650		4	\|a Sonoelectrochemical
650		4	\|a Design parameters
650		4	\|a Machine learning
650		4	\|a SHapley Additive exPlanations
650		4	\|a Chemiluminescence
700	1		\|a Ren, Yangmin \|e verfasserin \|4 aut
700	1		\|a Cui, Mingcan \|e verfasserin \|0 (orcid)0000-0001-9619-154X \|4 aut
700	1		\|a Guo, Fengshi \|e verfasserin \|4 aut
700	1		\|a Sun, Shiyu \|e verfasserin \|4 aut
700	1		\|a Ma, Junjun \|e verfasserin \|4 aut
700	1		\|a Han, Zhengchang \|e verfasserin \|4 aut
700	1		\|a Khim, Jeehyeong \|e verfasserin \|4 aut
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allfields	10.1016/j.cej.2023.147266 doi (DE-627)ELV065907655 (ELSEVIER)S1385-8947(23)05997-1 DE-627 ger DE-627 rda eng 660 VZ 660 VZ 58.10 bkl Zhou, Yongyue verfasserin aut Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload. Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence Ren, Yangmin verfasserin aut Cui, Mingcan verfasserin (orcid)0000-0001-9619-154X aut Guo, Fengshi verfasserin aut Sun, Shiyu verfasserin aut Ma, Junjun verfasserin aut Han, Zhengchang verfasserin aut Khim, Jeehyeong verfasserin aut Enthalten in The chemical engineering journal Amsterdam : Elsevier, 1997 478 Online-Ressource (DE-627)320500322 (DE-600)2012137-4 (DE-576)098330152 1873-3212 nnns volume:478 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 478
spelling	10.1016/j.cej.2023.147266 doi (DE-627)ELV065907655 (ELSEVIER)S1385-8947(23)05997-1 DE-627 ger DE-627 rda eng 660 VZ 660 VZ 58.10 bkl Zhou, Yongyue verfasserin aut Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload. Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence Ren, Yangmin verfasserin aut Cui, Mingcan verfasserin (orcid)0000-0001-9619-154X aut Guo, Fengshi verfasserin aut Sun, Shiyu verfasserin aut Ma, Junjun verfasserin aut Han, Zhengchang verfasserin aut Khim, Jeehyeong verfasserin aut Enthalten in The chemical engineering journal Amsterdam : Elsevier, 1997 478 Online-Ressource (DE-627)320500322 (DE-600)2012137-4 (DE-576)098330152 1873-3212 nnns volume:478 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 478
allfields_unstemmed	10.1016/j.cej.2023.147266 doi (DE-627)ELV065907655 (ELSEVIER)S1385-8947(23)05997-1 DE-627 ger DE-627 rda eng 660 VZ 660 VZ 58.10 bkl Zhou, Yongyue verfasserin aut Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload. Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence Ren, Yangmin verfasserin aut Cui, Mingcan verfasserin (orcid)0000-0001-9619-154X aut Guo, Fengshi verfasserin aut Sun, Shiyu verfasserin aut Ma, Junjun verfasserin aut Han, Zhengchang verfasserin aut Khim, Jeehyeong verfasserin aut Enthalten in The chemical engineering journal Amsterdam : Elsevier, 1997 478 Online-Ressource (DE-627)320500322 (DE-600)2012137-4 (DE-576)098330152 1873-3212 nnns volume:478 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 478
allfieldsGer	10.1016/j.cej.2023.147266 doi (DE-627)ELV065907655 (ELSEVIER)S1385-8947(23)05997-1 DE-627 ger DE-627 rda eng 660 VZ 660 VZ 58.10 bkl Zhou, Yongyue verfasserin aut Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload. Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence Ren, Yangmin verfasserin aut Cui, Mingcan verfasserin (orcid)0000-0001-9619-154X aut Guo, Fengshi verfasserin aut Sun, Shiyu verfasserin aut Ma, Junjun verfasserin aut Han, Zhengchang verfasserin aut Khim, Jeehyeong verfasserin aut Enthalten in The chemical engineering journal Amsterdam : Elsevier, 1997 478 Online-Ressource (DE-627)320500322 (DE-600)2012137-4 (DE-576)098330152 1873-3212 nnns volume:478 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 478
allfieldsSound	10.1016/j.cej.2023.147266 doi (DE-627)ELV065907655 (ELSEVIER)S1385-8947(23)05997-1 DE-627 ger DE-627 rda eng 660 VZ 660 VZ 58.10 bkl Zhou, Yongyue verfasserin aut Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload. Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence Ren, Yangmin verfasserin aut Cui, Mingcan verfasserin (orcid)0000-0001-9619-154X aut Guo, Fengshi verfasserin aut Sun, Shiyu verfasserin aut Ma, Junjun verfasserin aut Han, Zhengchang verfasserin aut Khim, Jeehyeong verfasserin aut Enthalten in The chemical engineering journal Amsterdam : Elsevier, 1997 478 Online-Ressource (DE-627)320500322 (DE-600)2012137-4 (DE-576)098330152 1873-3212 nnns volume:478 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 478
language	English
source	Enthalten in The chemical engineering journal 478 volume:478
sourceStr	Enthalten in The chemical engineering journal 478 volume:478
format_phy_str_mv	Article
bklname	Verfahrenstechnik: Allgemeines
institution	findex.gbv.de
topic_facet	Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence
dewey-raw	660
isfreeaccess_bool	false
container_title	The chemical engineering journal
authorswithroles_txt_mv	Zhou, Yongyue @@aut@@ Ren, Yangmin @@aut@@ Cui, Mingcan @@aut@@ Guo, Fengshi @@aut@@ Sun, Shiyu @@aut@@ Ma, Junjun @@aut@@ Han, Zhengchang @@aut@@ Khim, Jeehyeong @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320500322
dewey-sort	3660
id	ELV065907655
language_de	englisch
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Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. 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author	Zhou, Yongyue
spellingShingle	Zhou, Yongyue ddc 660 bkl 58.10 misc Sonoelectrochemical misc Design parameters misc Machine learning misc SHapley Additive exPlanations misc Chemiluminescence Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
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topic_title	660 VZ 58.10 bkl Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants Sonoelectrochemical Design parameters Machine learning SHapley Additive exPlanations Chemiluminescence
topic	ddc 660 bkl 58.10 misc Sonoelectrochemical misc Design parameters misc Machine learning misc SHapley Additive exPlanations misc Chemiluminescence
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title	Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
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title_full	Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
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author_browse	Zhou, Yongyue Ren, Yangmin Cui, Mingcan Guo, Fengshi Sun, Shiyu Ma, Junjun Han, Zhengchang Khim, Jeehyeong
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title_sort	sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
title_auth	Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
abstract	This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload.
abstractGer	This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload.
abstract_unstemmed	This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload.
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title_short	Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
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author2	Ren, Yangmin Cui, Mingcan Guo, Fengshi Sun, Shiyu Ma, Junjun Han, Zhengchang Khim, Jeehyeong
author2Str	Ren, Yangmin Cui, Mingcan Guo, Fengshi Sun, Shiyu Ma, Junjun Han, Zhengchang Khim, Jeehyeong
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doi_str	10.1016/j.cej.2023.147266
up_date	2024-07-07T00:39:53.511Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV065907655</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231220093243.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231201s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cej.2023.147266</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV065907655</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1385-8947(23)05997-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.10</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Yongyue</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O 4 · - are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sonoelectrochemical</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Design parameters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SHapley Additive exPlanations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chemiluminescence</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield 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Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants

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