Waste management最新文献

Modelling anaerobic digestion of agricultural waste: From lab to full scale

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-19 DOI: 10.1016/j.wasman.2025.114739

Tatiana Segura , Paul Zanoni , Ulysse Brémond , Constance Lucet--Bérille , Antoine Pradel , Renaud Escudié , Jean-Philippe Steyer

Biogas production through anaerobic digestion offers a promising alternative to address climate change. In this study, the ADM1 model was used to simulate the digestion of four different substrates: a mixture of rye and maize silage, a mixture of cow slurry and maize silage, cow slurry alone, and food waste. Furthermore, the determination of total solids (TS) content was integrated into the model. Based on experimental data from 5 L Continuous Stirred Tank Reactors (CSTR), ADM1 model parameters were calibrated for each substrate, primarily differing in hydrolysis and inhibition constants. These parameters, along with two additional sets of parameters from the literature, were subsequently applied in simulations to assess methane productivity, yield, and TS under increasing organic loading rates (OLR) for each substrate. Among the substrates, food waste showed the highest productivity, yield, and solids removal, while rye and maize silage substrate was the most unstable, with system failure at the lowest OLR (7

kgVS {. m}^{- 3} {. d}^{- 1}

) compared to the other substrates. In addition, co-digestion of cow slurry and maize silage showed synergies between maize silage and cow slurry, achieving a productivity of 2.62

N m^{3} . m^{- 3} . d^{- 1}

. Moreover, the parameters determined for rye and maize silage mixture were further used to simulate a full-scale anaerobic digestion unit fed with rye and maize silage as substrate. A difference in volatile fatty acid accumulation was found between the lab- and full-scale systems, suggesting a possible better microbial adaptation to inhibitory factors in the full-scale system. Further investigation into inhibition effects is recommended to improve the predictive accuracy of the ADM1.

{"title":"Modelling anaerobic digestion of agricultural waste: From lab to full scale","authors":"Tatiana Segura , Paul Zanoni , Ulysse Brémond , Constance Lucet--Bérille , Antoine Pradel , Renaud Escudié , Jean-Philippe Steyer","doi":"10.1016/j.wasman.2025.114739","DOIUrl":"10.1016/j.wasman.2025.114739","url":null,"abstract":"<div><div>Biogas production through anaerobic digestion offers a promising alternative to address climate change. In this study, the ADM1 model was used to simulate the digestion of four different substrates: a mixture of rye and maize silage, a mixture of cow slurry and maize silage, cow slurry alone, and food waste. Furthermore, the determination of total solids (TS) content was integrated into the model. Based on experimental data from 5 L Continuous Stirred Tank Reactors (CSTR), ADM1 model parameters were calibrated for each substrate, primarily differing in hydrolysis and inhibition constants. These parameters, along with two additional sets of parameters from the literature, were subsequently applied in simulations to assess methane productivity, yield, and TS under increasing organic loading rates (OLR) for each substrate. Among the substrates, food waste showed the highest productivity, yield, and solids removal, while rye and maize silage substrate was the most unstable, with system failure at the lowest OLR (7 <span><math><mrow><mi>kgVS</mi><msup><mrow><mo>.</mo><mi>m</mi></mrow><mrow><mo>-</mo><mn>3</mn></mrow></msup><msup><mrow><mo>.</mo><mi>d</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></math></span>) compared to the other substrates. In addition, co-digestion of cow slurry and maize silage showed synergies between maize silage and cow slurry, achieving a productivity of 2.62 <span><math><mrow><mi>N</mi><msup><mrow><mi>m</mi></mrow><mn>3</mn></msup><mo>.</mo><msup><mrow><mi>m</mi></mrow><mrow><mo>-</mo><mn>3</mn></mrow></msup><mo>.</mo><msup><mrow><mi>d</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></math></span>. Moreover, the parameters determined for rye and maize silage mixture were further used to simulate a full-scale anaerobic digestion unit fed with rye and maize silage as substrate. A difference in volatile fatty acid accumulation was found between the lab- and full-scale systems, suggesting a possible better microbial adaptation to inhibitory factors in the full-scale system. Further investigation into inhibition effects is recommended to improve the predictive accuracy of the ADM1.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114739"},"PeriodicalIF":7.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable biomass processing: Optimizing energy efficiency through ash waste heat recovery for fuels dewatering

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-18 DOI: 10.1016/j.wasman.2025.114752

Taikun Yin , Xinya Huang , Yikang Wang , Chao He , Liang Liu , Pengfei Li , Youzhou Jiao , Gang Li

Biomass power generation technologies face challenges in cost competitiveness due to high energy consumption during dewatering. Utilizing waste heat for fuel dewatering is a viable solution to reduce energy consumption and improve efficiency. Herein, spherical heat carrier (SHC) was prepared by encapsulating sand using metal spherical shell, providing a low cost and efficient means for transferring ash heat to dewater biomass. The effects of temperature of heat source (ash or SHC), mixing mass ratio, loading of sand and moisture content of biomass on the recovery and utilization of ash waste heat, and dewatering effect of biomass were optimized by using response surface method. As results, ash temperature and mass ratio were the main factors affecting heat recovery rate and the effective heat transfer coefficient in the heat exchange between SHCs and ash, while SHC temperature and moisture content were the main factors of dehydration rate and the effective heat transfer coefficient in the peanut shells dewatering. The optimal overall thermal efficiency of the continuous process was 36% and the specific energy consumption was 2.13 kWh·kg_water⁻¹. This work provided a technically feasible and efficient way for biomass ash waste heat utilization and biomass fuel dewatering.

{"title":"Sustainable biomass processing: Optimizing energy efficiency through ash waste heat recovery for fuels dewatering","authors":"Taikun Yin , Xinya Huang , Yikang Wang , Chao He , Liang Liu , Pengfei Li , Youzhou Jiao , Gang Li","doi":"10.1016/j.wasman.2025.114752","DOIUrl":"10.1016/j.wasman.2025.114752","url":null,"abstract":"<div><div>Biomass power generation technologies face challenges in cost competitiveness due to high energy consumption during dewatering. Utilizing waste heat for fuel dewatering is a viable solution to reduce energy consumption and improve efficiency. Herein, spherical heat carrier (SHC) was prepared by encapsulating sand using metal spherical shell, providing a low cost and efficient means for transferring ash heat to dewater biomass. The effects of temperature of heat source (ash or SHC), mixing mass ratio, loading of sand and moisture content of biomass on the recovery and utilization of ash waste heat, and dewatering effect of biomass were optimized by using response surface method. As results, ash temperature and mass ratio were the main factors affecting heat recovery rate and the effective heat transfer coefficient in the heat exchange between SHCs and ash, while SHC temperature and moisture content were the main factors of dehydration rate and the effective heat transfer coefficient in the peanut shells dewatering. The optimal overall thermal efficiency of the continuous process was 36% and the specific energy consumption was 2.13 kWh·kg<sub>water</sub><sup>−1</sup>. This work provided a technically feasible and efficient way for biomass ash waste heat utilization and biomass fuel dewatering.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114752"},"PeriodicalIF":7.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing operational efficiency in a voluntary recycling project through data-driven waste collection optimization

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-18 DOI: 10.1016/j.wasman.2025.114741

Sanyapong Petchrompo , Rasita Chitniyom , Naplaifa Peerwantanagul , Wasakorn Laesanklang , Jirachaya Suwanapong , Shuleeporn Borrisuttanakul

Recycling in developing countries is often driven by voluntary initiatives, typically led by the private sector. While commendable, these efforts face significant challenges, particularly in ensuring operational efficiency due to their non-profit nature. The logistics process involves collecting recyclable plastics from collection points and delivering them to a recycling facility. This aspect is crucial, as it represents the largest cost component, making optimization essential. Traditional approaches, such as the Traveling Salesman Problem and its meta-heuristic variants, are time-consuming for practical applications. To address these challenges, we propose a three-step data-driven approach designed to optimize waste collection within the constraints of non-profit projects. The first step uses K-means clustering to group collection points geographically, reducing the complexity of subsequent optimization stages. The optimization models in the second and third steps aim to maximize the amount of recyclable plastic per trip and determine the most efficient collection route. Real-time data on waste volume at each point and live traffic conditions, retrieved via the Intelligent Traffic Information Center, are integrated into these models, making it possible to achieve a high level of practicality and accuracy. The efficacy of this approach is demonstrated through a case study of the Won Project, involving 152 plastic waste collection points. The results show a significant daily increase in the average amount of plastic collected and reduction in the average distance traveled. The proposed method can produce prompt, reliable solutions for daily operations using open-source software, successfully addressing the challenges of voluntary waste collection projects.

{"title":"Enhancing operational efficiency in a voluntary recycling project through data-driven waste collection optimization","authors":"Sanyapong Petchrompo , Rasita Chitniyom , Naplaifa Peerwantanagul , Wasakorn Laesanklang , Jirachaya Suwanapong , Shuleeporn Borrisuttanakul","doi":"10.1016/j.wasman.2025.114741","DOIUrl":"10.1016/j.wasman.2025.114741","url":null,"abstract":"<div><div>Recycling in developing countries is often driven by voluntary initiatives, typically led by the private sector. While commendable, these efforts face significant challenges, particularly in ensuring operational efficiency due to their non-profit nature. The logistics process involves collecting recyclable plastics from collection points and delivering them to a recycling facility. This aspect is crucial, as it represents the largest cost component, making optimization essential. Traditional approaches, such as the Traveling Salesman Problem and its meta-heuristic variants, are time-consuming for practical applications. To address these challenges, we propose a three-step data-driven approach designed to optimize waste collection within the constraints of non-profit projects. The first step uses K-means clustering to group collection points geographically, reducing the complexity of subsequent optimization stages. The optimization models in the second and third steps aim to maximize the amount of recyclable plastic per trip and determine the most efficient collection route. Real-time data on waste volume at each point and live traffic conditions, retrieved via the Intelligent Traffic Information Center, are integrated into these models, making it possible to achieve a high level of practicality and accuracy. The efficacy of this approach is demonstrated through a case study of the Won Project, involving 152 plastic waste collection points. The results show a significant daily increase in the average amount of plastic collected and reduction in the average distance traveled. The proposed method can produce prompt, reliable solutions for daily operations using open-source software, successfully addressing the challenges of voluntary waste collection projects.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114741"},"PeriodicalIF":7.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-18 DOI: 10.1016/j.wasman.2025.114748

Quan Bu , Jianmei Bai , Bufei Wang , Leilei Dai , Hairong Long

A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR) with an R² of 0.72 and a root mean square error (RMSE) of 0.15, while eXtreme Gradient Boosting (XGBoost) demonstrated a superior performance with an R² of 0.90 and an RMSE of 0.08. Therefore, XGBoost was selected as the final prediction model. Results obtained from the machine learning interpretation tool, SHapley Additive exPlanations (SHAP), revealed that the two most influential factors affecting gas yield were the highest co-pyrolysis temperature (HTT) and the blending ratio (BR), contributing 33% and 28% to the model’s predictions, respectively. Besides, the moisture content in biomass (MB) has been found to be one of the critical variables affecting the gaseous products yield. To determine the interaction between these factors and their contributions to gas yield, SHAP partial dependence analysis (SHAP PDA) was conducted. Therefore, this study offers novel insights into predicting gas yields in biomass and plastics co-pyrolysis, aiding in identifying optimal conditions for maximizing gas yield production.

{"title":"Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics","authors":"Quan Bu , Jianmei Bai , Bufei Wang , Leilei Dai , Hairong Long","doi":"10.1016/j.wasman.2025.114748","DOIUrl":"10.1016/j.wasman.2025.114748","url":null,"abstract":"<div><div>A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR) with an R<sup>2</sup> of 0.72 and a root mean square error (RMSE) of 0.15, while eXtreme Gradient Boosting (XGBoost) demonstrated a superior performance with an R<sup>2</sup> of 0.90 and an RMSE of 0.08. Therefore, XGBoost was selected as the final prediction model. Results obtained from the machine learning interpretation tool, SHapley Additive exPlanations (SHAP), revealed that the two most influential factors affecting gas yield were the highest co-pyrolysis temperature (HTT) and the blending ratio (BR), contributing 33% and 28% to the model’s predictions, respectively. Besides, the moisture content in biomass (MB) has been found to be one of the critical variables affecting the gaseous products yield. To determine the interaction between these factors and their contributions to gas yield, SHAP partial dependence analysis (SHAP PDA) was conducted. Therefore, this study offers novel insights into predicting gas yields in biomass and plastics co-pyrolysis, aiding in identifying optimal conditions for maximizing gas yield production.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114748"},"PeriodicalIF":7.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Upcycling textile derived microplastics waste collected from washer and dryers to carbonaceous products using hydrothermal carbonization

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-16 DOI: 10.1016/j.wasman.2025.114740

Silvia Parrilla-Lahoz , Elena Jiménez-Páez , Mateus G. Masteghin , Joel J. Pawlak , Richard A. Venditti , Robert Bird , Paul Servin , Jose Antonio Odriozola , Tomas Ramirez Reina , Melis S. Duyar

Microplastics are an emerging pollutant of concern. Many microplastics in the waters arise from washing synthetic textiles in residential and commercial washing machines. The present research evaluated the upcycling of this waste to carbon materials by hydrothermal carbonization. Real microfiber waste was collected using clothes washer and dryer microfilters. Via temperature and residence time screening (200 °C, 250 °C, 300 °C and 1 h, 4 h, 8 h) two temperatures of interest were determined (250 °C and 300 °C) for hydrothermal carbonization, for a residence time of 4 h. The results obtained in this research demonstrated that by varying the reaction conditions carbon production can be tailored, producing amorphous carbon or graphene/graphite. To this end, Raman spectroscopy results indicated the production of carbon nanomaterials; smaller particle sizes were detected after 250 °C-4h and 300 °C-4h treatments, (29.6 nm and 33.1 nm, respectively). Transforming microfibers into useful carbon nanoparticles via hydrothermal carbonization prolongs their lifecycle and mitigates environmental pollution. This process is an intriguing method of incorporating textile residue (microfibers) into the circular economy, where resources are perpetually recycled, and waste is avoided.

{"title":"Upcycling textile derived microplastics waste collected from washer and dryers to carbonaceous products using hydrothermal carbonization","authors":"Silvia Parrilla-Lahoz , Elena Jiménez-Páez , Mateus G. Masteghin , Joel J. Pawlak , Richard A. Venditti , Robert Bird , Paul Servin , Jose Antonio Odriozola , Tomas Ramirez Reina , Melis S. Duyar","doi":"10.1016/j.wasman.2025.114740","DOIUrl":"10.1016/j.wasman.2025.114740","url":null,"abstract":"<div><div>Microplastics are an emerging pollutant of concern. Many microplastics in the waters arise from washing synthetic textiles in residential and commercial washing machines. The present research evaluated the upcycling of this waste to carbon materials by hydrothermal carbonization. Real microfiber waste was collected using clothes washer and dryer microfilters. Via temperature and residence time screening (200 °C, 250 °C, 300 °C and 1 h, 4 h, 8 h) two temperatures of interest were determined (250 °C and 300 °C) for hydrothermal carbonization, for a residence time of 4 h. The results obtained in this research demonstrated that by varying the reaction conditions carbon production can be tailored, producing amorphous carbon or graphene/graphite. To this end, Raman spectroscopy results indicated the production of carbon nanomaterials; smaller particle sizes were detected after 250 °C-4h and 300 °C-4h treatments, (29.6 nm and 33.1 nm, respectively). Transforming microfibers into useful carbon nanoparticles via hydrothermal carbonization prolongs their lifecycle and mitigates environmental pollution. This process is an intriguing method of incorporating textile residue (microfibers) into the circular economy, where resources are perpetually recycled, and waste is avoided.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114740"},"PeriodicalIF":7.1,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recovery of lithium, cobalt and nickel from the spent NMC Li-ion battery by reduction roasting, selective leaching and precipitation

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-16 DOI: 10.1016/j.wasman.2025.114749

Asal Shoaei, Sadegh Firoozi

Valuable elements from the NMC Li-ion battery black mass were recovered by reduction roasting and selective leaching/precipitation. Active materials were separated from the Al/Cu collector foils by thermal treatment and then subjected to reduction roasting by carbon anode in the temperature range of 600–900 °C for 2 h. The progress of the reactions was studied via XRD, TGA-DTA, and thermodynamic modeling. Ni/Co reduction was completed at T ≥ 800 °C and transformation of Li₂CO₃ to Li₂O occurred at 900 °C. Water leaching was employed to separate lithium from roasted products and about 93 % of Li was selectively extracted from the roasted product. Pure Li₂CO₃ was precipitated from the solution by CO₂ injection without purification or the addition of pH modifiers. Mn was then selectively leached from the Li-depleted powder by citric acid. The effect of citric acid concentration, temperature, and time on the leaching efficiency of Mn was studied. About 95 % Mn was removed from the powder at a citric acid concentration of 0.15 M, T = 75 °C, time = 200 min, and S/L = 20 g/L. The activation energy of the Mn dissolution was measured as 15.5 kJ/mol suggesting a diffusional controlled mechanism. A Ni-Co alloy powder was obtained as the final leaching residue. This study provides an effective and practical approach for recycling valuable metals from spent NMC Li-ion batteries.

{"title":"Recovery of lithium, cobalt and nickel from the spent NMC Li-ion battery by reduction roasting, selective leaching and precipitation","authors":"Asal Shoaei, Sadegh Firoozi","doi":"10.1016/j.wasman.2025.114749","DOIUrl":"10.1016/j.wasman.2025.114749","url":null,"abstract":"<div><div>Valuable elements from the NMC Li-ion battery black mass were recovered by reduction roasting and selective leaching/precipitation. Active materials were separated from the Al/Cu collector foils by thermal treatment and then subjected to reduction roasting by carbon anode in the temperature range of 600–900 °C for 2 h. The progress of the reactions was studied via XRD, TGA-DTA, and thermodynamic modeling. Ni/Co reduction was completed at T ≥ 800 °C and transformation of Li<sub>2</sub>CO<sub>3</sub> to Li<sub>2</sub>O occurred at 900 °C. Water leaching was employed to separate lithium from roasted products and about 93 % of Li was selectively extracted from the roasted product. Pure Li<sub>2</sub>CO<sub>3</sub> was precipitated from the solution by CO<sub>2</sub> injection without purification or the addition of pH modifiers. Mn was then selectively leached from the Li-depleted powder by citric acid. The effect of citric acid concentration, temperature, and time on the leaching efficiency of Mn was studied. About 95 % Mn was removed from the powder at a citric acid concentration of 0.15 M, T = 75 °C, time = 200 min, and S/L = 20 g/L. The activation energy of the Mn dissolution was measured as 15.5 kJ/mol suggesting a diffusional controlled mechanism. A Ni-Co alloy powder was obtained as the final leaching residue. This study provides an effective and practical approach for recycling valuable metals from spent NMC Li-ion batteries.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114749"},"PeriodicalIF":7.1,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insights into nutrients recovery from food waste liquid Digestate: A critical review and systematic analysis

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-15 DOI: 10.1016/j.wasman.2025.114743

Diana Victoria Arellano-Yasaca , Chen-Yeon Chu

This review paper presents a critical analysis of global research on the liquid fraction of food waste (FW) digestate. The study found that research on FW liquid fraction management accounted for 43% of the literature, followed by treatment methods (26%) and physical–chemical characterization (22%). By 2023, China led in scientific production on FW liquid fraction, contributing 46%, followed by Poland with 10% and the USA with 8%. The review emphasizes current technologies for nutrient recovery from the liquid fraction, as well as practical implications and limitations, identifying gaps in the literature. The most used methods for nutrient recovery were biofertilizer production from microalgae and membrane technologies. However, there is a need for further research on nutrient value, circular economy integration, the impact of food additives, ecological problems associated with FW decomposition, pathogen breeding, harmonized legislation to support recovered fertilizer commercialization and innovative nutrient recovery technologies. This approach provides valuable insights for stakeholders, enabling the creation of effective strategies that promote sustainable agricultural practices and circular economy initiatives through nutrient recovery from FW digestate.

{"title":"Insights into nutrients recovery from food waste liquid Digestate: A critical review and systematic analysis","authors":"Diana Victoria Arellano-Yasaca , Chen-Yeon Chu","doi":"10.1016/j.wasman.2025.114743","DOIUrl":"10.1016/j.wasman.2025.114743","url":null,"abstract":"<div><div>This review paper presents a critical analysis of global research on the liquid fraction of food waste (FW) digestate. The study found that research on FW liquid fraction management accounted for 43% of the literature, followed by treatment methods (26%) and physical–chemical characterization (22%). By 2023, China led in scientific production on FW liquid fraction, contributing 46%, followed by Poland with 10% and the USA with 8%. The review emphasizes current technologies for nutrient recovery from the liquid fraction, as well as practical implications and limitations, identifying gaps in the literature. The most used methods for nutrient recovery were biofertilizer production from microalgae and membrane technologies. However, there is a need for further research on nutrient value, circular economy integration, the impact of food additives, ecological problems associated with FW decomposition, pathogen breeding, harmonized legislation to support recovered fertilizer commercialization and innovative nutrient recovery technologies. This approach provides valuable insights for stakeholders, enabling the creation of effective strategies that promote sustainable agricultural practices and circular economy initiatives through nutrient recovery from FW digestate.</div></div>","PeriodicalId":23969,"journal":{"name":"Waste management","volume":"200 ","pages":"Article 114743"},"PeriodicalIF":7.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Competitive adsorption and diffusion of methane and vapor-phase per- and polyfluoroalkyl substances in montmorillonite nano pores: Environmental implications 蒙脱石纳米孔隙中甲烷与气相全氟烷基和多氟烷基物质的竞争性吸附和扩散：对环境的影响

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-14 DOI: 10.1016/j.wasman.2025.114746

Rui Xu , Qiao Wang , Fusheng Zha , Jiawei Wu , Bokade Mrunal Sunil Shobha , Devendra Narain Singh

Vapor-phase perfluoroalkyl and polyfluoroalkyl substances (PFASs), along with methane emissions from landfills has been key contributors of their atmospheric transport and global distribution. Given the persistence, bioaccumulation, and potential health risks associated with PFAS, understanding their transport behavior in landfill gas barrier is of paramount importance. To gain a deeper understanding of the adsorption and diffusion behavior of vapor-phase PFAS in unsaturated, montmorillonite-rich clay barriers, a molecular dynamics simulation was conducted. A 5-nm montmorillonite nanopore incorporating vapor-phase PFAS (Fluorotelomer alcohol, FTOH), methane, and water molecules was modeled considering the interactions between these species. The results indicate that the presence of methane within the montmorillonite system inhibits the diffusion of both water and FTOH. Additionally, methane competes with FTOH for sorption sites, particularly at low moisture content. At 5 % moisture content, the adsorption density peak of methane is 1.5 times greater than that of FTOH due to stronger van der Waals interactions between methane and montmorillonite. However, as moisture content increases, methane adsorption weakens and becomes more dispersed within the montmorillonite pores. In contrast, FTOH retains a distinct adsorption region at 20 % moisture content, exhibiting a density peak of 0.025 g/cm³ that shifts farther from the montmorillonite surface. At high moisture content, FTOH aggregates due to the hydrophobicity of its C-F tail. These findings provide critical insights into the environmental behavior of volatile PFASs and have important implications for the design and optimization of landfill gas barriers.

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引用次数: 0

MedBin: A lightweight End-to-End model-based method for medical waste management MedBin：基于端到端模型的轻量级医疗废物管理方法

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-14 DOI: 10.1016/j.wasman.2025.114742

Xiazhen Xu , Chenyang Wang , Qiufeng Yi , Jiaqi Ye , Xiangfei Kong , Shazad Q Ashraf , Karl D. Dearn , Amir M. Hajiyavand

The surge in medical waste has highlighted the urgent need for cost-effective and advanced management solutions. In this paper, a novel medical waste management approach, “MedBin,” is proposed for automated sorting, reusing, and recycling. A comprehensive medical waste dataset, ”MedBin-Dataset“ is established, comprising 2,119 original images spanning 36 categories, with samples captured in various backgrounds. The lightweight ”MedBin-Net“ model is introduced to enable detection and instance segmentation of medical waste, enhancing waste recognition capabilities. Experimental results demonstrate the effectiveness of the proposed approach, achieving an average precision of 0.91, recall of 0.97, and F1-score of 0.94 across all categories with just 2.51 M parameters (where M stands for million, i.e., 2.51 million parameters), 5.20G FLOPs (where G stands for billion, i.e., 5.20 billion floating-point operations per second), and 0.60 ms inference time. Additionally, the proposed method includes a World Health Organization (WHO) Guideline-Based Classifier that categorizes detected waste into 5 types, each with a corresponding disposal method, following WHO medical waste classification standards. The proposed method, along with the dedicated dataset, offers a promising solution that supports sustainable medical waste management and other related applications. To access the MedBin-Dataset samples, please visit https://universe.roboflow.com/uob-ylti8/medbin_dataset. The source code for MedBin-Net can be found at https://github.com/Wayne3918/MedbinNet.

医疗废物的激增凸显了对具有成本效益的先进管理解决方案的迫切需求。本文提出了一种新型医疗废物管理方法 "MedBin"，用于自动分类、再利用和回收。本文建立了一个全面的医疗废物数据集 "MedBin-Dataset"，由 2,119 张原始图像组成，涵盖 36 个类别，并在不同背景下采集样本。引入轻量级 "MedBin-Net "模型，实现医疗废物的检测和实例分割，提高废物识别能力。实验结果证明了所提方法的有效性，在所有类别中平均精确度为 0.91，召回率为 0.97，F1 分数为 0.94，参数数为 251 万（其中 M 代表百万，即 251 万个参数），浮点运算次数为 5.20G（其中 G 代表十亿，即每秒 52 亿次浮点运算），推理时间为 0.60 毫秒。此外，拟议方法还包括一个基于世界卫生组织（WHO）指南的分类器，该分类器按照世界卫生组织的医疗废物分类标准，将检测到的废物分为 5 种类型，每种类型都有相应的处置方法。建议的方法与专用数据集一起，为支持可持续医疗废物管理和其他相关应用提供了一个前景广阔的解决方案。要访问 MedBin 数据集样本，请访问 https://universe.roboflow.com/uob-ylti8/medbin_dataset。MedBin-Net 的源代码可在 https://github.com/Wayne3918/MedbinNet 上找到。

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引用次数: 0

Transforming urban waste collection inventory: AI-Based container classification and Re-Identification

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-03-12 DOI: 10.1016/j.wasman.2025.02.051

Javier Galán, Miguel González, Paula Moral, Álvaro García-Martín, José M. Martínez

This work lays the groundwork for creating an automated system for the inventory of urban waste elements. Our primary contribution is the development of, to the best of our knowledge, the first re-identification system for urban waste elements that uses Artificial Intelligence and Computer Vision, incorporating information from a classification module and geolocation context to enhance post-processing performance. This re-identification system helps to create and update inventories by determining if a new image matches an existing element in the inventory based on visual similarity or, if not, by adding it as a new identity (new class or new identity of an existing class). Such a system could be highly valuable to local authorities and waste management companies, offering improved facility maintenance, geolocation, and additional applications. This work also addresses the dynamic nature of urban environments and waste management elements by exploring Continual Learning strategies to adapt pretrained systems to new settings with different urban elements. Experimental results show that the proposed system operates effectively across various container types and city layouts. These findings were validated through testing in two different Spanish locations, a “City” and a “Campus”, differing in size, illumination conditions, seasons, urban design and container appearance. For the final re-identification system, the baseline system achieves 53.18 mAP (mean Average Precision) in the simple scenario, compared to 21.54 mAP in the complex scenario, with additional challenging unseen variability. Incorporating the proposed post-processing techniques significantly improved results, reaching 74.14 mAP and 71.75 mAP in the simple and complex scenario respectively.

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引用次数: 0