Effects of incineration leachate on anaerobic digestion of excess sludge and the related mechanisms

Gao, Meng; Yang, Jiahui; Li, Siqi; Liu, Sinan; Xu, Xiaofeng; Liu, Feng; Gu, Li

doi:10.1016/j.jenvman.2022.114831

Cited by 14 publications

(1 citation statement)

References 64 publications

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“…For incineration leachate, the variety and quantity of microorganisms in each process are quite important, as they result in different treatment effects of waste incineration plants [14]. The relationship between headspace gas and microorganisms in leachate is noteworthy and complicated [15].…”

Section: Introductionmentioning

confidence: 99%

Efficient Monitoring of Microbial Communities and Chemical Characteristics in Incineration Leachate with Electronic Nose and Data Mining Techniques

et al. 2023

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Incineration leachate is a hazardous liquid waste that requires careful management due to its high levels of organic and inorganic pollutants, and it can have serious environmental and health implications if not properly treated and monitored. This study applied a novel electronic nose to monitor the microbial communities and chemical characteristics of incineration leachate. The e-nose data were aggregated using principal component analysis (PCA) and T-distributed stochastic neighbor embedding (TSNE). Random forest (RF) and gradient-boosted decision tree (GBDT) algorithms were employed to establish relationships between the e-nose signals and the chemical characteristics (such as pH, chemical oxygen demand, and ammonia nitrogen) and microbial communities (including Proteobacteria, Firmicutes, and Bacteroidetes) of the incineration leachate. The PCA-GBDT models performed well in recognizing leachate samples, achieving 100% accuracy for the training set and 98.92% accuracy for the testing data without overfitting. The GBDT models based on the original data performed exceptionally well in predicting changes in chemical parameters, with R2 values exceeding 0.99 for the training set and 0.86 for the testing set. The PCA-GBDT models also demonstrated superior performance in predicting microbial community composition, achieving R2 values above 0.99 and MSE values below 0.0003 for the training set and R2 values exceeding 0.86 and MSE values below 0.015 for the testing set. This research provides an efficient monitoring method for the effective enforcement and implementation of monitoring programs by utilizing e-noses combined with data mining to provide more valuable insights compared with traditional instrumental measurements.

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Section: Introductionmentioning

confidence: 99%