{"title":"生物炭改性堆肥成熟度综合评价框架","authors":"Jianmei Zou , Yihao Hua , Yushu Cheng , Lingyue Zhang , Huichun Zhang , Fei Shen","doi":"10.1016/j.biortech.2025.132970","DOIUrl":null,"url":null,"abstract":"<div><div>A predictive framework combining machine learning and weighting techniques was established to resolve inconsistencies in maturity evaluation of biochar-amended composting. The results indicated that the nonlinear model showed superior compost maturity prediction accuracy. Specifically, Gradient boosting (GB), extra trees (ET, used for both GI and NO<sub>3</sub><sup>–</sup>-N), and extreme gradient boosting (XGB) achieved the highest R<sup>2</sup> values for C/N ratio (0.84), GI (0.64), NO<sub>3</sub><sup>–</sup>-N (0.77), and NH<sub>4</sub><sup>+</sup>-N (0.81), respectively. SHAP analysis identified composting process parameters such as moisture content (MC_P), temperature (TEMP_P), and pH (pH_P) as key drivers of enzymatic activity and microbial succession, significantly affecting maturity. The model’s applicability and predictive capability were validated through cosine similarity and real-world composting experiments. An integrated maturity score, based on weighted predicted indicators, highlighted GI as the most influential factor (47.62 %). This framework enhances intelligent composting, safer agriculture, and environmental management through predictive accuracy and systematic evaluation.</div></div>","PeriodicalId":258,"journal":{"name":"Bioresource Technology","volume":"436 ","pages":"Article 132970"},"PeriodicalIF":9.7000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive evaluation framework for compost maturity with biochar amendment\",\"authors\":\"Jianmei Zou , Yihao Hua , Yushu Cheng , Lingyue Zhang , Huichun Zhang , Fei Shen\",\"doi\":\"10.1016/j.biortech.2025.132970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>A predictive framework combining machine learning and weighting techniques was established to resolve inconsistencies in maturity evaluation of biochar-amended composting. The results indicated that the nonlinear model showed superior compost maturity prediction accuracy. Specifically, Gradient boosting (GB), extra trees (ET, used for both GI and NO<sub>3</sub><sup>–</sup>-N), and extreme gradient boosting (XGB) achieved the highest R<sup>2</sup> values for C/N ratio (0.84), GI (0.64), NO<sub>3</sub><sup>–</sup>-N (0.77), and NH<sub>4</sub><sup>+</sup>-N (0.81), respectively. SHAP analysis identified composting process parameters such as moisture content (MC_P), temperature (TEMP_P), and pH (pH_P) as key drivers of enzymatic activity and microbial succession, significantly affecting maturity. The model’s applicability and predictive capability were validated through cosine similarity and real-world composting experiments. An integrated maturity score, based on weighted predicted indicators, highlighted GI as the most influential factor (47.62 %). This framework enhances intelligent composting, safer agriculture, and environmental management through predictive accuracy and systematic evaluation.</div></div>\",\"PeriodicalId\":258,\"journal\":{\"name\":\"Bioresource Technology\",\"volume\":\"436 \",\"pages\":\"Article 132970\"},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2025-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioresource Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0960852425009368\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioresource Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960852425009368","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
Comprehensive evaluation framework for compost maturity with biochar amendment
A predictive framework combining machine learning and weighting techniques was established to resolve inconsistencies in maturity evaluation of biochar-amended composting. The results indicated that the nonlinear model showed superior compost maturity prediction accuracy. Specifically, Gradient boosting (GB), extra trees (ET, used for both GI and NO3–-N), and extreme gradient boosting (XGB) achieved the highest R2 values for C/N ratio (0.84), GI (0.64), NO3–-N (0.77), and NH4+-N (0.81), respectively. SHAP analysis identified composting process parameters such as moisture content (MC_P), temperature (TEMP_P), and pH (pH_P) as key drivers of enzymatic activity and microbial succession, significantly affecting maturity. The model’s applicability and predictive capability were validated through cosine similarity and real-world composting experiments. An integrated maturity score, based on weighted predicted indicators, highlighted GI as the most influential factor (47.62 %). This framework enhances intelligent composting, safer agriculture, and environmental management through predictive accuracy and systematic evaluation.
期刊介绍:
Bioresource Technology publishes original articles, review articles, case studies, and short communications covering the fundamentals, applications, and management of bioresource technology. The journal seeks to advance and disseminate knowledge across various areas related to biomass, biological waste treatment, bioenergy, biotransformations, bioresource systems analysis, and associated conversion or production technologies.
Topics include:
• Biofuels: liquid and gaseous biofuels production, modeling and economics
• Bioprocesses and bioproducts: biocatalysis and fermentations
• Biomass and feedstocks utilization: bioconversion of agro-industrial residues
• Environmental protection: biological waste treatment
• Thermochemical conversion of biomass: combustion, pyrolysis, gasification, catalysis.