The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment.The results showed that pH 8 was considered to be the most suitable condition for acid production,especial...The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment.The results showed that pH 8 was considered to be the most suitable condition for acid production,especially for the formation of acetic acid and propionic acid.The average value of total volatile fatty acid at pH 8 was 8814 mg COD/L,1.5 times of that under blank condition.The average yield of acetic acid and propionic acid was 3302 mg COD/L and 2891 mg COD/L,respectively.The activities of key functional enzymes such as phosphotransacetylase,acetokinase,oxaloacetate transcarboxylase and succinylcoA transferase were all enhanced.To further explore the regulatory mechanisms within the system,the distribution of microorganisms at different levels in the fermentation system was obtained by microbial sequencing,results indicating that the relative abundances of Clostridiales,Bacteroidales,Chloroflexi,Clostridium,Bacteroidetes and Propionibacteriales,which were great contributors for the hydrolysis and acidification,increased rapidly at pH 8 compared with the blank group.Besides,the proportion of genes encoding key enzymes was generally increased,which further verified the mechanism of hydrolytic acidification and acetic acid production of organic matter under pH regulation.展开更多
Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective...Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective way of sorting.Owing to significant domain gaps between natural images and kitchen waste images,it is difficult to reflect the characteristics of diverse scales and dense distribution in kitchen waste based on an ImageNet pre-trained model,leading to poor generalisation.In this article,the authors propose the first pre-trained model for kitchen waste sorting called KitWaSor,which combines both contrastive learning(CL)and masked image modelling(MIM)through self-supervised learning(SSL).First,to address the issue of diverse scales,the authors propose a mixed masking strategy by introducing an incomplete masking branch based on the original random masking branch.It prevents the complete loss of small-scale objects while avoiding excessive leakage of large-scale object pixels.Second,to address the issue of dense distribution,the authors introduce semantic consistency constraints on the basis of the mixed masking strategy.That is,object semantic reasoning is performed through semantic consistency constraints to compensate for the lack of contextual information.To train KitWaSor,the authors construct the first million-level kitchen waste dataset across seasonal and regional distributions,named KWD-Million.Extensive experiments show that KitWaSor achieves state-of-the-art(SOTA)performance on the two most relevant downstream tasks for kitchen waste sorting(i.e.image classification and object detection),demonstrating the effectiveness of the proposed KitWaSor.展开更多
Additives could improve composting performance and reduce gaseous emission,but few studies have explored the synergistic of additives on H_(2)S emission and compost maturity.This research aims to make an investigation...Additives could improve composting performance and reduce gaseous emission,but few studies have explored the synergistic of additives on H_(2)S emission and compost maturity.This research aims to make an investigation about the effects of chemical additives and mature compost on H_(2)S emission and compost maturity of kitchen waste composting.The results showed that additives increased the germination index value and H_(2)S emission reduction over 15 days and the treatment with both chemical additives and mature compost achieved highest germination index value and H_(2)S emission reduction(85%).Except for the treatment with only chemical additives,the total sulfur content increased during the kitchen waste composting.The proportion of effective sulfur was higher with the addition of chemical additives,compared with other groups.The relative abundance of H_(2)S-formation bacterial(Desulfovibrio)was reduced and the relative abundance of bacterial(Pseudomonas and Paracoccus),which could convert sulfur-containing substances and H_(2)S to sulfate was improved with additives.In the composting process with both chemical additives and mature compost,the relative abundance of Desulfovibrio was lowest,while the relative abundance of Pseudomonas and Paracoccus was highest.Taken together,the chemical additives and mature compost achieved H_(2)S emission reduction by regulating the dynamics of microbial community.展开更多
基金supported by the National Key Research and Development Program of China(No.2019YFC1906304).
文摘The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment.The results showed that pH 8 was considered to be the most suitable condition for acid production,especially for the formation of acetic acid and propionic acid.The average value of total volatile fatty acid at pH 8 was 8814 mg COD/L,1.5 times of that under blank condition.The average yield of acetic acid and propionic acid was 3302 mg COD/L and 2891 mg COD/L,respectively.The activities of key functional enzymes such as phosphotransacetylase,acetokinase,oxaloacetate transcarboxylase and succinylcoA transferase were all enhanced.To further explore the regulatory mechanisms within the system,the distribution of microorganisms at different levels in the fermentation system was obtained by microbial sequencing,results indicating that the relative abundances of Clostridiales,Bacteroidales,Chloroflexi,Clostridium,Bacteroidetes and Propionibacteriales,which were great contributors for the hydrolysis and acidification,increased rapidly at pH 8 compared with the blank group.Besides,the proportion of genes encoding key enzymes was generally increased,which further verified the mechanism of hydrolytic acidification and acetic acid production of organic matter under pH regulation.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFC1910402。
文摘Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective way of sorting.Owing to significant domain gaps between natural images and kitchen waste images,it is difficult to reflect the characteristics of diverse scales and dense distribution in kitchen waste based on an ImageNet pre-trained model,leading to poor generalisation.In this article,the authors propose the first pre-trained model for kitchen waste sorting called KitWaSor,which combines both contrastive learning(CL)and masked image modelling(MIM)through self-supervised learning(SSL).First,to address the issue of diverse scales,the authors propose a mixed masking strategy by introducing an incomplete masking branch based on the original random masking branch.It prevents the complete loss of small-scale objects while avoiding excessive leakage of large-scale object pixels.Second,to address the issue of dense distribution,the authors introduce semantic consistency constraints on the basis of the mixed masking strategy.That is,object semantic reasoning is performed through semantic consistency constraints to compensate for the lack of contextual information.To train KitWaSor,the authors construct the first million-level kitchen waste dataset across seasonal and regional distributions,named KWD-Million.Extensive experiments show that KitWaSor achieves state-of-the-art(SOTA)performance on the two most relevant downstream tasks for kitchen waste sorting(i.e.image classification and object detection),demonstrating the effectiveness of the proposed KitWaSor.
基金supported by the National Natural Science Foundation of China(Nos.32071552,42007031,31960013,and 31800378)the Open Research Fund from the Key Laboratory of Forest Ecology in Tibet Plateau(Tibet Agriculture&Animal Husbandry University),Ministry of Education,China(No.XZAJYBSYS-2020-02)+2 种基金the Independent Research Project of Science and Technology Innovation Base in Tibet Autonomous Region(No.XZ2022JR0007G)Suzhou Science and Technology Plan Project(No.SS20200)Ministry of Urban-Rural Development and Housing Technology Demonstration Project(No.S20220395)。
文摘Additives could improve composting performance and reduce gaseous emission,but few studies have explored the synergistic of additives on H_(2)S emission and compost maturity.This research aims to make an investigation about the effects of chemical additives and mature compost on H_(2)S emission and compost maturity of kitchen waste composting.The results showed that additives increased the germination index value and H_(2)S emission reduction over 15 days and the treatment with both chemical additives and mature compost achieved highest germination index value and H_(2)S emission reduction(85%).Except for the treatment with only chemical additives,the total sulfur content increased during the kitchen waste composting.The proportion of effective sulfur was higher with the addition of chemical additives,compared with other groups.The relative abundance of H_(2)S-formation bacterial(Desulfovibrio)was reduced and the relative abundance of bacterial(Pseudomonas and Paracoccus),which could convert sulfur-containing substances and H_(2)S to sulfate was improved with additives.In the composting process with both chemical additives and mature compost,the relative abundance of Desulfovibrio was lowest,while the relative abundance of Pseudomonas and Paracoccus was highest.Taken together,the chemical additives and mature compost achieved H_(2)S emission reduction by regulating the dynamics of microbial community.
