To create an effective and inexpensive modifier of road petroleum bitumen the authors continued to study the production and application of coumarone-indene-carbazole resins (CICR). In previous researches the influence of various factors on the process of obtaining CICR had been investigated and analyzed. However, the complex influence of process factors was not established. Therefore, the aim of this research was to create an adequateexperimental-statistical and kinetic models. According to them, optimal process conditions were proposed, which iclude: a carbazole content in the raw material of 19.5 wt. % relative to the resin-forming components (styrene, coumarone, indene), a catalyst content of 8.0 wt. % relative to the resin-forming components, a process temperature of 115 °C, and a process duration of 46 min. The determined effective activation energy of the CICR synthesis was 21.2 kJ/mol. The obtained results will allow us to extrapolate the conditions for gaining CICR in industrial processes and receive an effective bitumen modifier under optimal conditions.
{"title":"Creation of experimental-statistical and kinetic models of the coumarone-indene-carbazole resin production process","authors":"Serhiy Pyshyev , Yuriy Prysiazhnyi , Halyna Bilushchak , Bohdan Korchak , Iryna Pochapska , Oleg Yavorskyi","doi":"10.1016/j.rineng.2024.103689","DOIUrl":"10.1016/j.rineng.2024.103689","url":null,"abstract":"<div><div>To create an effective and inexpensive modifier of road petroleum bitumen the authors continued to study the production and application of coumarone-indene-carbazole resins (CICR). In previous researches the influence of various factors on the process of obtaining CICR had been investigated and analyzed. However, the complex influence of process factors was not established. Therefore, the aim of this research was to create an adequateexperimental-statistical and kinetic models. According to them, optimal process conditions were proposed, which iclude: a carbazole content in the raw material of 19.5 wt. % relative to the resin-forming components (styrene, coumarone, indene), a catalyst content of 8.0 wt. % relative to the resin-forming components, a process temperature of 115 °C, and a process duration of 46 min. The determined effective activation energy of the CICR synthesis was 21.2 kJ/mol. The obtained results will allow us to extrapolate the conditions for gaining CICR in industrial processes and receive an effective bitumen modifier under optimal conditions.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103689"},"PeriodicalIF":6.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166825","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 : 2024-12-09DOI: 10.1016/j.rineng.2024.103704
Seyedeh Nasrin Sharifi
Horizontal step surfaces are the traditional design of stepped spillways, intended for certain discharges. However, they do not perform satisfactorily at larger discharges. To improve this, layouts with shaped step surfaces, both downward and upward, are proposed. To examine and improve the flow properties of the skimming flow, CFD modeling in 2D was conducted. The analysis emphasizes the complicated interaction between geometric design, fluid dynamics, and structural integrity in spillway systems, which could lead to superior performance in spillway applications. Through an understanding of vortex formation and flow behavior, engineers and designers can create structures that are more effective and resilient, catering to specific operational needs. To validate these theoretical insights and investigate the implications in various conditions, such as variable water quality and discharges, additional empirical research is necessary. The findings ultimately show the significance of customized engineering solutions that put performance and safety first in hydrodynamic environments.
{"title":"Enhancing performance of stepped spillways: CFD analysis of shaped step surfaces for improved flow dynamic","authors":"Seyedeh Nasrin Sharifi","doi":"10.1016/j.rineng.2024.103704","DOIUrl":"10.1016/j.rineng.2024.103704","url":null,"abstract":"<div><div>Horizontal step surfaces are the traditional design of stepped spillways, intended for certain discharges. However, they do not perform satisfactorily at larger discharges. To improve this, layouts with shaped step surfaces, both downward and upward, are proposed. To examine and improve the flow properties of the skimming flow, CFD modeling in 2D was conducted. The analysis emphasizes the complicated interaction between geometric design, fluid dynamics, and structural integrity in spillway systems, which could lead to superior performance in spillway applications. Through an understanding of vortex formation and flow behavior, engineers and designers can create structures that are more effective and resilient, catering to specific operational needs. To validate these theoretical insights and investigate the implications in various conditions, such as variable water quality and discharges, additional empirical research is necessary. The findings ultimately show the significance of customized engineering solutions that put performance and safety first in hydrodynamic environments.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103704"},"PeriodicalIF":6.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167380","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}
An experimental and computational research was performed to explore the augmentation of turbulent convection in a solar receiver channel by utilizing louver-punched V-type winglets (LPVWs) that were fixed to the absorber plate. The simulation utilized the realizable k-ε turbulent model, and the predicated outputs were verified by the relevant measured data. At a fixed attack angle (α) of 45°, the LPVW components were mounted on the absorber with the V-tip facing downstream. Using the LPVW, the newly developed absorber is intended to boost thermal performance by generating multiple flows of longitudinal vortices that induce impinging air streams onto the absorber, thereby enhancing heat transmission. The louvered hole on the winglet serves to reduce pressure loss while preserving the primary vortices. In the current investigation, the winglet parameters consisted of a single relative winglet height (BR = 0.4), four louver size ratios (RL = e1/b = 0.9, 0.7, 0.5, and 0.3), and five louver-flapped angles (β = 90°, 60°, 45°, 30°, and 0°). The LPVW with β > 0° substantially reduced the solid-winglet (β = 0°) friction loss, whereas the heat transmission was slightly declined, as indicated by the results. The solid winglet (β = 0°) exhibited the largest frictional loss and heat transmission, with values approximately 6.3 and 48.2 times the smooth flat duct, respectively. The optimal performance of the LPVW was roughly 2.58, at RL = 0.9 and β = 45° Furthermore, empirical correlations for heat transmission and frictional loss were established for this solar receiver duct system. To investigate the heat transmission and flow patterns, a 3-dimensional numerical simulation was implemented, and the predictions were verified against the measured data. The findings were in good accord between the numerical and measured data. For greater thermal performance, the LPVW is reconfigured by altering the locations of the louver holes. The revised LPVW exhibits a peak TEF of 2.7 at β = 35°, l2/l1 = 0.15, l3/l1 = -0.15 and RL = 0.9, about 4.65 % superior than the initial analysis.
{"title":"Enhanced thermal performance in solar receiver duct with louver-punched V-type winglets: Numerical and experimental study","authors":"Maturose Suchatawat , Somchai Sripattanapipat , Pitak Promthaisong , Sompol Skullong , Pongjet Promvonge","doi":"10.1016/j.rineng.2024.103702","DOIUrl":"10.1016/j.rineng.2024.103702","url":null,"abstract":"<div><div>An experimental and computational research was performed to explore the augmentation of turbulent convection in a solar receiver channel by utilizing louver-punched V-type winglets (LPVWs) that were fixed to the absorber plate. The simulation utilized the realizable k-ε turbulent model, and the predicated outputs were verified by the relevant measured data. At a fixed attack angle (<em>α</em>) of 45°, the LPVW components were mounted on the absorber with the V-tip facing downstream. Using the LPVW, the newly developed absorber is intended to boost thermal performance by generating multiple flows of longitudinal vortices that induce impinging air streams onto the absorber, thereby enhancing heat transmission. The louvered hole on the winglet serves to reduce pressure loss while preserving the primary vortices. In the current investigation, the winglet parameters consisted of a single relative winglet height (B<sub>R</sub> = 0.4), four louver size ratios (R<sub>L</sub> = <em>e</em><sub>1</sub>/<em>b</em> = 0.9, 0.7, 0.5, and 0.3), and five louver-flapped angles (<em>β</em> = 90°, 60°, 45°, 30°, and 0°). The LPVW with <em>β</em> > 0° substantially reduced the solid-winglet (<em>β</em> = 0°) friction loss, whereas the heat transmission was slightly declined, as indicated by the results. The solid winglet (<em>β</em> = 0°) exhibited the largest frictional loss and heat transmission, with values approximately 6.3 and 48.2 times the smooth flat duct, respectively. The optimal performance of the LPVW was roughly 2.58, at R<sub>L</sub> = 0.9 and <em>β</em> = 45° Furthermore, empirical correlations for heat transmission and frictional loss were established for this solar receiver duct system. To investigate the heat transmission and flow patterns, a 3-dimensional numerical simulation was implemented, and the predictions were verified against the measured data. The findings were in good accord between the numerical and measured data. For greater thermal performance, the LPVW is reconfigured by altering the locations of the louver holes. The revised LPVW exhibits a peak TEF of 2.7 at <em>β</em> = 35°, <em>l</em><sub>2</sub>/<em>l</em><sub>1</sub> = 0.15, <em>l</em><sub>3</sub>/<em>l</em><sub>1</sub> = -0.15 and R<sub>L</sub> = 0.9, about 4.65 % superior than the initial analysis.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103702"},"PeriodicalIF":6.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166853","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 : 2024-12-09DOI: 10.1016/j.rineng.2024.103677
Shijian Lu , Changjun Shen , Miaomiao Liu , Juanjuan Zhang , Mengxiang Fang , Feng Wang , Qingfang Li , Chao Li , Jian Zhang
Organic amine carbon dioxide capture technology is widely used for industrial carbon capture due to its mature technology, however, the emission of absorbent and degradation products can affect the carbon dioxide capture efficiency and increase the environmental health risk. This paper summarizes the types and current status of pollutant emissions from chemical absorption systems, and explores the effects of flue gas condensation nucleation occurring in different salt solutions, carbon dioxide concentration in the flue gas, lean liquid loading, liquid-to-gas ratio, flue gas inlet temperature of the absorber tower, and temperature of the lean liquid on pollutant emissions from carbon dioxide chemical absorption systems by means of a small test experiment with a flue gas treatment design capacity of 3.6 Nm3/h. At the same time, amine escape from a coal-fired power plant with a flue gas treatment capacity of about 1.4 million Nm3/h was also studied, and the control effects of different pollutant emission control measures were compared, and the best pollutant emission control measure was the combined control process of “secondary water washing + 7-meter dry bed”, with a reduction of organic amine absorber loss by 716.5 mg/Nm3, a reduction of 71.65 % compared with the “primary water washing” control process.
{"title":"Research and application analysis on the pollution control of tail gas emissions from CO2 capture absorber","authors":"Shijian Lu , Changjun Shen , Miaomiao Liu , Juanjuan Zhang , Mengxiang Fang , Feng Wang , Qingfang Li , Chao Li , Jian Zhang","doi":"10.1016/j.rineng.2024.103677","DOIUrl":"10.1016/j.rineng.2024.103677","url":null,"abstract":"<div><div>Organic amine carbon dioxide capture technology is widely used for industrial carbon capture due to its mature technology, however, the emission of absorbent and degradation products can affect the carbon dioxide capture efficiency and increase the environmental health risk. This paper summarizes the types and current status of pollutant emissions from chemical absorption systems, and explores the effects of flue gas condensation nucleation occurring in different salt solutions, carbon dioxide concentration in the flue gas, lean liquid loading, liquid-to-gas ratio, flue gas inlet temperature of the absorber tower, and temperature of the lean liquid on pollutant emissions from carbon dioxide chemical absorption systems by means of a small test experiment with a flue gas treatment design capacity of 3.6 Nm<sup>3</sup>/h. At the same time, amine escape from a coal-fired power plant with a flue gas treatment capacity of about 1.4 million Nm<sup>3</sup>/h was also studied, and the control effects of different pollutant emission control measures were compared, and the best pollutant emission control measure was the combined control process of “secondary water washing + 7-meter dry bed”, with a reduction of organic amine absorber loss by 716.5 mg/Nm<sup>3</sup>, a reduction of 71.65 % compared with the “primary water washing” control process.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103677"},"PeriodicalIF":6.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166819","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 : 2024-12-09DOI: 10.1016/j.rineng.2024.103693
Ahmed Wajeh Mushtaha, Wesam Salah Alaloul, Abdullah O. Baarimah, Muhammad Ali Musarat, Khalid Mhmoud Alzubi, Abdul Mateen Khan
Disaster recovery requires urgent but thoughtful decision-making in the face of scarce resources, making it essential to prioritize projects that will strengthen community resilience and functionality. Disasters, defined as sudden, catastrophic events that disrupt the functioning of communities and cause significant human, material, economic, or environmental losses, create a pressing need for effective prioritization. Effective prioritization considers structural integrity, safety, economic viability, social impact, and environmental sustainability. However, a systematic framework to guide resource prioritization in post-disaster recovery is lacking. To address the gap in systematic prioritization, this research aims to develop a clear framework that guides post-disaster rebuilders in the efficient allocation of scarce resources based on resilience and equity principles. Results were achieved by investigating a total of 273 experts in post-disaster reconstruction, with 62 % possessing over ten years of experience. Top findings indicate that structural integrity and functionality are the most important. In addition, safety considerations in terms of immediate access for responders (85.0 %) are crucial, along with social requirements, including the impacts on affected populations (85.9 %) and vulnerable groups (77.7 %).Economic considerations prioritize funding availability (76.8 %) and resource access (70.6 %), while environmental concerns highlight public health impacts (82.6 %) and environmental hazards (81.1 %). The findings emphasize the need for the development of a holistic, evidence-based prioritization smart system that will balance immediate recovery needs with long-term resilience objectives. Those actionable insights aim to support policymakers and practitioners to discussions on how strategic resource allocation may help build resilience and ensure adequate, equitable recovery in disaster-stricken areas.
{"title":"A decision-making framework for prioritizing reconstruction projects in post-disaster recovery","authors":"Ahmed Wajeh Mushtaha, Wesam Salah Alaloul, Abdullah O. Baarimah, Muhammad Ali Musarat, Khalid Mhmoud Alzubi, Abdul Mateen Khan","doi":"10.1016/j.rineng.2024.103693","DOIUrl":"10.1016/j.rineng.2024.103693","url":null,"abstract":"<div><div>Disaster recovery requires urgent but thoughtful decision-making in the face of scarce resources, making it essential to prioritize projects that will strengthen community resilience and functionality. Disasters, defined as sudden, catastrophic events that disrupt the functioning of communities and cause significant human, material, economic, or environmental losses, create a pressing need for effective prioritization. Effective prioritization considers structural integrity, safety, economic viability, social impact, and environmental sustainability. However, a systematic framework to guide resource prioritization in post-disaster recovery is lacking. To address the gap in systematic prioritization, this research aims to develop a clear framework that guides post-disaster rebuilders in the efficient allocation of scarce resources based on resilience and equity principles. Results were achieved by investigating a total of 273 experts in post-disaster reconstruction, with 62 % possessing over ten years of experience. Top findings indicate that structural integrity and functionality are the most important. In addition, safety considerations in terms of immediate access for responders (85.0 %) are crucial, along with social requirements, including the impacts on affected populations (85.9 %) and vulnerable groups (77.7 %).Economic considerations prioritize funding availability (76.8 %) and resource access (70.6 %), while environmental concerns highlight public health impacts (82.6 %) and environmental hazards (81.1 %). The findings emphasize the need for the development of a holistic, evidence-based prioritization smart system that will balance immediate recovery needs with long-term resilience objectives. Those actionable insights aim to support policymakers and practitioners to discussions on how strategic resource allocation may help build resilience and ensure adequate, equitable recovery in disaster-stricken areas.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103693"},"PeriodicalIF":6.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167376","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 : 2024-12-09DOI: 10.1016/j.rineng.2024.103637
Ahmed Mohammed Awad Mohammed , Omayma Husain , Muyideen Abdulkareem , Nor Zurairahetty Mohd Yunus , Nadiah Jamaludin , Elamin Mutaz , Hashim Elshafie , Mosab Hamdan
Weak soil causes significant challenges during infrastructure development, necessitating soil stabilization to enhance its engineering properties. The pozzolanic properties of Ground Granulated Blast Furnace Slag (GGBS) have led to its widespread use as an effective stabilizer in soil improvement. This study aims to predict the UCS of soft soil stabilized with GGBS using various machine learning models. A database of 200 samples was compiled from the literature, and six ML models—linear regression, decision trees, random forest, artificial neural networks, gradient boosting, and extreme gradient boosting were developed and evaluated. The study highlights the performance of these models and employs SHAP and LIME analysis to evaluate feature importance. The XGB model emerged as the most effective predictor of unconfined compressive strength for soil treated with GGBS, accounting for over 90% of the variance explained by independent factors. The curing period, optimal moisture content, and maximum dry density served as critical variables influencing UCS, demonstrating the model's capacity to recognize underlying patterns and generate precise predictions. In addition to being more appropriate for complicated models, SHAPE is more accurate than LIME. SHAPE suggests that OMC has a detrimental impact on UCS in the current investigation, but LIME suggests the opposite. SHAPE results are in agreement with lab experiment results.
{"title":"Explainable Artificial Intelligence for predicting the compressive strength of soil and ground granulated blast furnace slag mixtures","authors":"Ahmed Mohammed Awad Mohammed , Omayma Husain , Muyideen Abdulkareem , Nor Zurairahetty Mohd Yunus , Nadiah Jamaludin , Elamin Mutaz , Hashim Elshafie , Mosab Hamdan","doi":"10.1016/j.rineng.2024.103637","DOIUrl":"10.1016/j.rineng.2024.103637","url":null,"abstract":"<div><div>Weak soil causes significant challenges during infrastructure development, necessitating soil stabilization to enhance its engineering properties. The pozzolanic properties of Ground Granulated Blast Furnace Slag (GGBS) have led to its widespread use as an effective stabilizer in soil improvement. This study aims to predict the UCS of soft soil stabilized with GGBS using various machine learning models. A database of 200 samples was compiled from the literature, and six ML models—linear regression, decision trees, random forest, artificial neural networks, gradient boosting, and extreme gradient boosting were developed and evaluated. The study highlights the performance of these models and employs SHAP and LIME analysis to evaluate feature importance. The XGB model emerged as the most effective predictor of unconfined compressive strength for soil treated with GGBS, accounting for over 90% of the variance explained by independent factors. The curing period, optimal moisture content, and maximum dry density served as critical variables influencing UCS, demonstrating the model's capacity to recognize underlying patterns and generate precise predictions. In addition to being more appropriate for complicated models, SHAPE is more accurate than LIME. SHAPE suggests that OMC has a detrimental impact on UCS in the current investigation, but LIME suggests the opposite. SHAPE results are in agreement with lab experiment results.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103637"},"PeriodicalIF":6.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167387","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}
The asphalt industry faces increasing pressure to reduce its environmental impact and improve economic efficiency. Warm mix asphalt (WMA) technologies have been developed as a promising solution to reduce the production temperatures of asphalt mixtures, thus improving energy efficiency and minimizing emissions. Besides, incorporating reclaimed asphalt pavement (RAP) material offers significant environmental and economic benefits by reducing the demand for virgin materials. However, high RAP contents present challenges such as binder hardening and poor adhesion among aggregates due to aging, limiting its application. This review delves into the interplay between WMA technologies and RAP, exploring their individual and combined effects on the environmental and mechanical performance of asphalt mixtures. By optimizing the selection and dosage of WMA additives, and balancing RAP content, it is possible to mitigate the challenges associated with high RAP content and achieve desired pavement performance. Future researches should focus on developing advanced WMA additives and techniques to further enhance the integration of high RAP content, ensuring both environmental sustainability and improved pavement performance.
{"title":"Advancing asphalt mixture sustainability: A review of WMA-RAP integration","authors":"Mohsin Alizadeh, Pouria Hajikarimi, Fereidoon Moghadas Nejad","doi":"10.1016/j.rineng.2024.103678","DOIUrl":"10.1016/j.rineng.2024.103678","url":null,"abstract":"<div><div>The asphalt industry faces increasing pressure to reduce its environmental impact and improve economic efficiency. Warm mix asphalt (WMA) technologies have been developed as a promising solution to reduce the production temperatures of asphalt mixtures, thus improving energy efficiency and minimizing emissions. Besides, incorporating reclaimed asphalt pavement (RAP) material offers significant environmental and economic benefits by reducing the demand for virgin materials. However, high RAP contents present challenges such as binder hardening and poor adhesion among aggregates due to aging, limiting its application. This review delves into the interplay between WMA technologies and RAP, exploring their individual and combined effects on the environmental and mechanical performance of asphalt mixtures. By optimizing the selection and dosage of WMA additives, and balancing RAP content, it is possible to mitigate the challenges associated with high RAP content and achieve desired pavement performance. Future researches should focus on developing advanced WMA additives and techniques to further enhance the integration of high RAP content, ensuring both environmental sustainability and improved pavement performance.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103678"},"PeriodicalIF":6.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166768","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 : 2024-12-08DOI: 10.1016/j.rineng.2024.103684
Yang Wang, Mi Zhou
With the persistent focus on quality of life, the detrimental effects of air pollution have emerged as a significant factor influencing labor mobility globally. This study utilizes panel data from 31 provinces in China spanning the years 2012 to 2021 to examine the relationship between air pollution and labor mobility. The research results indicate that air pollution has a positive impact on labor mobility, with a labor loss of 0.125 % for every 1 % increase in PM2.5 concentration. Education, healthcare, urbanization, and cultural factors also affect labor mobility to different degrees within regions. Notably, the influence of air pollution on labor mobility exhibits significant heterogeneity across diverse regions. While economically developed areas display lower sensitivity to air pollution, regions with relatively underdeveloped economies witness an intensification of human capital mobility in response to rising pollution levels. This study contributes to the existing literature on the repercussions of air pollution on labor mobility, furnishing valuable empirical insights for the formulation of environmental governance and labor mobility-related policies worldwide.
{"title":"Heterogeneity in the impact of air pollution on labor mobility: Insights from panel data analysis in China","authors":"Yang Wang, Mi Zhou","doi":"10.1016/j.rineng.2024.103684","DOIUrl":"10.1016/j.rineng.2024.103684","url":null,"abstract":"<div><div>With the persistent focus on quality of life, the detrimental effects of air pollution have emerged as a significant factor influencing labor mobility globally. This study utilizes panel data from 31 provinces in China spanning the years 2012 to 2021 to examine the relationship between air pollution and labor mobility. The research results indicate that air pollution has a positive impact on labor mobility, with a labor loss of 0.125 % for every 1 % increase in PM2.5 concentration. Education, healthcare, urbanization, and cultural factors also affect labor mobility to different degrees within regions. Notably, the influence of air pollution on labor mobility exhibits significant heterogeneity across diverse regions. While economically developed areas display lower sensitivity to air pollution, regions with relatively underdeveloped economies witness an intensification of human capital mobility in response to rising pollution levels. This study contributes to the existing literature on the repercussions of air pollution on labor mobility, furnishing valuable empirical insights for the formulation of environmental governance and labor mobility-related policies worldwide.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103684"},"PeriodicalIF":6.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166823","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 : 2024-12-08DOI: 10.1016/j.rineng.2024.103676
Sirajudeen Inayathullah, Raviteja Buddala
The necessity to produce intricate components results in considerable progress in manufacturing methods. Additive manufacturing (AM) is a disruptive technology that allows intricate and custom-tailored components to be fabricated with great precision and efficiency. It is applied in advanced sectors like aerospace, healthcare, automotive industries, and it starts having their interest in many other areas. Machine learning (ML) has become a powerful tool for overcoming problems in AM, offering process efficiency, defect detection, quality assurance, and predictive modelling of mechanical properties. This review discusses how ML transforms AM by providing design evaluation, process optimization, and production control innovation. The approach taken in the study is systematic, examining the current literature and case studies of ML application to AM. Hybrid data collection techniques that combine machine settings with physics aware features and yield robust predictive models are the focus. Additionally, the review evaluates various ML algorithms used to predict mechanical properties, optimize process parameters, and characterize AM processes. The measurements indicate groundbreaking improvements in ML powered solutions, like process monitoring in real time, automatic parameter adaptation, and defect mitigation that offer greater accuracy, ease, and reliability in AM. Yet, data scarcity, computational challenges and a gap between research and industrial applications of ML exist. To realize the full potential of ML in AM it is critical to address these challenges. It closes with the identification of promising research directions including standardization of data improvement, developing new advanced ML algorithms, and building an interdisciplinary research effort to spur additional progress in this field.
{"title":"Review of machine learning applications in additive manufacturing","authors":"Sirajudeen Inayathullah, Raviteja Buddala","doi":"10.1016/j.rineng.2024.103676","DOIUrl":"10.1016/j.rineng.2024.103676","url":null,"abstract":"<div><div>The necessity to produce intricate components results in considerable progress in manufacturing methods. Additive manufacturing (AM) is a disruptive technology that allows intricate and custom-tailored components to be fabricated with great precision and efficiency. It is applied in advanced sectors like aerospace, healthcare, automotive industries, and it starts having their interest in many other areas. Machine learning (ML) has become a powerful tool for overcoming problems in AM, offering process efficiency, defect detection, quality assurance, and predictive modelling of mechanical properties. This review discusses how ML transforms AM by providing design evaluation, process optimization, and production control innovation. The approach taken in the study is systematic, examining the current literature and case studies of ML application to AM. Hybrid data collection techniques that combine machine settings with physics aware features and yield robust predictive models are the focus. Additionally, the review evaluates various ML algorithms used to predict mechanical properties, optimize process parameters, and characterize AM processes. The measurements indicate groundbreaking improvements in ML powered solutions, like process monitoring in real time, automatic parameter adaptation, and defect mitigation that offer greater accuracy, ease, and reliability in AM. Yet, data scarcity, computational challenges and a gap between research and industrial applications of ML exist. To realize the full potential of ML in AM it is critical to address these challenges. It closes with the identification of promising research directions including standardization of data improvement, developing new advanced ML algorithms, and building an interdisciplinary research effort to spur additional progress in this field.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103676"},"PeriodicalIF":6.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166769","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}
Effective management of wet refuse-derived fuel (RDF) through biodrying is crucial for advancing the circular economy and sustainable waste management. This study aims to optimize the biodrying process of RDF by comparing multiple regression analysis (MRA) and multilevel analysis using generalized linear mixed models (GLMM). Experimental data based on different aeration rates (0.2–0.8 m³/kg feedstock/day) and initial moisture content (MC) (40–60 %) were analyzed to predict the final MC. The results indicate that the interaction models significantly outperformed non-interaction models, with GLMM explaining 86 % of the variance (R² = 0.86) and reducing the prediction accuracy by 11 %. The GLMM framework effectively captured batch-to-batch variability, leading to an optimal final MC reduction from 60 % to 30 %. Advanced statistical techniques can thus refine waste enhancement processes, providing important insights into biodrying optimization. Improving energy recovery from waste may contribute to establishing more sustainable waste management practices.
{"title":"Predictive modeling and advanced statistical approaches for enhancing biodrying efficiency in wet refuse-derived fuel","authors":"Abhisit Bhatsada , Sirintornthep Towprayoon , Chart Chiemchaisri , Tanik Itsarathorn , Komsilp Wangyao","doi":"10.1016/j.rineng.2024.103682","DOIUrl":"10.1016/j.rineng.2024.103682","url":null,"abstract":"<div><div>Effective management of wet refuse-derived fuel (RDF) through biodrying is crucial for advancing the circular economy and sustainable waste management. This study aims to optimize the biodrying process of RDF by comparing multiple regression analysis (MRA) and multilevel analysis using generalized linear mixed models (GLMM). Experimental data based on different aeration rates (0.2–0.8 m³/kg feedstock/day) and initial moisture content (MC) (40–60 %) were analyzed to predict the final MC. The results indicate that the interaction models significantly outperformed non-interaction models, with GLMM explaining 86 % of the variance (R² = 0.86) and reducing the prediction accuracy by 11 %. The GLMM framework effectively captured batch-to-batch variability, leading to an optimal final MC reduction from 60 % to 30 %. Advanced statistical techniques can thus refine waste enhancement processes, providing important insights into biodrying optimization. Improving energy recovery from waste may contribute to establishing more sustainable waste management practices.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"25 ","pages":"Article 103682"},"PeriodicalIF":6.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167379","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}
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