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The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to less satisfactory recognition for its reliability. Accordingly, this paper aimed to explore the chemical insights of the machine learning models in the fast characterization process. A novel dimensional reduction method with significant physicochemical meanings was thus proposed, where the high loading spectral peaks of BW were selected as input features. Combined with functional groups attribution of these spectral peaks, the machine learning models established based on the dimensionally reduced spectral data could be explained with clear chemical insights. The performance of classification and regression models between the proposed dimensional reduction method and principal component analysis method was compared. The influence mechanism of each functional group on the characterization results were discussed. CH deformation, CC stretch & CO stretch and ketone/aldehyde CO stretch played essential roles in C, H/ LHV and O prediction, respectively. The results of this work demonstrated the theoretical fundamentals of the machine learning and spectroscopy based BW fast characterization method., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L 2,1 -norm named L 2,1 -Extreme Learning Machine (L 2,1 -ELM) has been proposed. The method introduces L 2,1 -norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM., Results: In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L 2,1 -norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM., Conclusions: The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

ObjectivesTo investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.MethodsThe retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsThe SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively.ConclusionThe machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.

Rectangular-hole-type crush initiators have attracted much attention due to their exceptional advantages in improving crashworthiness performance. Unfortunately, undesired deformation buckling modes, i.e. bifurcation buckling, can occur and challenge optimisation processes. This paper aims remedies to this limitation and proposes a machine learning based optimisation method that removes these undesired deformation modes to effectively evaluate the effect of initiators' parameters in improving crush response. First, finite element models of square tubes with different numbers and positions of initiators were built and computed. Second, the complex proportional assessment (COPRAS) method was adopted to rank the crashworthiness characteristics. The results demonstrated that square tubes with two sets of four corner holes along the axial direction were the best choice. Finally, the optimisation method based on machine learning was employed to evaluate the influence of the initiator's size on the crushing behaviours for minimizing the initial peak force (IPF), maximizing the energy absorption (EA), and crushing force efficiency (CFE). The results illustrated that the IPF of the optimised design Pareto front upper limit is about 21.94% lower, with 0.08% and 36.67% higher EA and CFE, respectively, than those of the best choice observed from COPRAS. This study highlights the ability of the proposed machine learning based optimisation method to improve crashworthiness performance. [ABSTRACT FROM AUTHOR]

Aortic dissection (AD) is a life-threatening condition in which the inner layer of the aorta tears. It has been reported that metabolic syndrome (MS) has a close linkage with aortic dissection. However, the inter-relational mechanisms between them were still unclear. This article explored the hub gene signatures and potential molecular mechanisms in AD and MS. We obtained five bulk RNA-seq datasets of AD, one single cell RNA-seq (scRNA-seq) dataset of ascending thoracic aortic aneurysm (ATAA), and one bulk RNA-seq dataset of MS from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and key modules via weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning algorithms (Random Forest and LASSO regression) were used to identify hub genes for diagnosing AD with MS. XGBoost further improved the diagnostic performance of the model. The receiver operating characteristic (ROC) and precision-recall (PR) curves were developed to assess the diagnostic value. Then, immune cell infiltration and metabolism-associated pathways analyses were created to investigate immune cell and metabolism-associated pathway dysregulation in AD and MS. Finally, the scRNA-seq dataset was performed to confirm the expression levels of identified hub genes. 406 common DEGs were identified between the merged AD and MS datasets. Functional enrichment analysis revealed these DEGs were enriched for applicable terms of metabolism, cellular processes, organismal systems, and human diseases. Besides, the positively related key modules of AD and MS were mainly enriched in transcription factor binding and inflammatory response. In contrast, the negatively related modules were significantly associated with adaptive immune response and regulation of nuclease activity. Through machine learning, nine genes with common diagnostic effects were found in AD and MS, including MAD2L2, IMP4, PRPF4, CHSY1, SLC20A1, SLC9A1, TIPRL, DPYD, and MAPKAPK2. In the training set, the AUC of the hub gene on RP and RR curves was 1. In the AD verification set, the AUC of the Hub gene on RP and RR curves were 0.946 and 0.955, respectively. In the MS set, the AUC of the Hub gene on RP and RR curves were 0.978 and 0.98, respectively. scRNA-seq analysis revealed that the SLC20A1 was found to be relevant in fatty acid metabolic pathways and expressed in endothelial cells. Our study revealed the common pathogenesis of AD and MS. These common pathways and hub genes might provide new ideas for further mechanism research. [ABSTRACT FROM AUTHOR]

This study proposed a fast characterization method of bio-oil via the combination of attenuated total reflection flourier transformed infrared spectroscopy (ATR-FTIR) and machine learning models. The input to the model is high-dimensional infrared spectral data. Unsaturated concentration, effective hydrocarbon ratio, low calorific value, C content, H content, and O content are all relevant bio-oil indicators. The model parameters were optimized based on prediction accuracy and correlation coefficient. By comparing the sole support vector regression (SVR) model versus principal component analysis (PCA) preprocessed SVR model, the results showed that PCA preprocessing can significantly improve the overall performance of SVR model towards prediction of bio-oil characteristics. Under optimal parameters, the predicted accuracies for unsaturated concentration, effective hydrocarbon ratio, low calorific value, C content, H content, and O content reached 91.98%, 97.44%, 99.50%, 98.65%, 98.56%, and 97.88%, respectively. The correlation coefficient of sole SVR model was 0.3, and the correlation coefficient of PCA preprocessed SVR model was 0.9. Furthermore, the characteristic peaks of the infrared spectra at the optimal PC were analyzed, and PC6 and PC7 were found to have the most influence on the predicting performance. [Display omitted] [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) has fast learning speed and perfect performance, at the same time, ELM provides a unified learning framework with a widespread type of feature mappings which can be applied in multiclass classification applications directly. These advantages make ELM become one of the best classification algorithms, and ELM has attracted great attention in supervised learning and semi-supervised learning. However, noise and outliers of data are usually existed in the real world, which will affect the performance of ELM. To improve the robustness and classification performance of ELM, we propose the Kernel Risk-Sensitive Mean p-power Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRP-HRELM) method. On the one side, as a nonlinear similarity measure defined in the reproducing kernel space, the kernel risk-sensitive mean p-power loss (KRP) can effectively weaken the negative effects caused by noise and outliers. Therefore, the KRP is introduced into ELM to enhance its robustness. Then, the application of hyper-graph can help the ELM to explore higher-order geometric structures among more sampling points, thereby obtaining more comprehensive data information. In addition, to obtain a more sparsity network model, the L2,1-norm is used to constrain the output weight. On the other side, improving the practical application ability of KRP-HRELM is also the focus of our research, so KRP-HRELM is extended to semi-supervised learning, which is called the semi-supervised KRP-HRELM (SS-KRP-HRELM). Notably, the results of the robustness experiment have proved that our method has extraordinary robustness. At the same time, by using four evaluation measures such as accuracy, recall, precision, and F1-measure, to evaluate the classification results, we can find that our method has obtained better classification performance than other advanced methods. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery. Methods: The retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively. Conclusion: The machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sorting strategies based on source, physical and chemical properties were compared. • The gasification efficiency of sorted RMSW was 40.89% higher than that of unsorted. • The yield of syngas can be increased by 37.14% after waste sorting. • The emission of CO 2 can be reduced by 81 t·per year after waste sorting. • Elemental composition based sorting method benefited to efficient gasification. Gasification is a promising way to convert residual municipal solid waste (RMSW) to high-valued syngas. Waste sorting is helpful to alleviate the heterogeneity problem of waste on gasification. This paper aimed to search for an optimal sorting strategy and evaluate the influence of waste sorting on downstream gasification. A sorting strategy based on elemental composition was proposed and compared with common source and density based strategies. The results validated the enhancement of waste sorting on RMSW gasification, where the elemental composition based strategy showed optimal gasification. The yield of syngas, cold gas efficiency, the lower heating value of syngas and H 2 /CO were increased by 37.14 %, 38.95 %, 2.18 % and 8.81 %, respectively. It also showed a good effect on the improvement of air gasification compared with the literature, especially for H 2 /CO. This paper also discussed the economic and environmental potential of the elemental composition based sorting method. The waste sorting can help increase 29,250 RMB/year and reduce CO 2 emission by 81 t/year. This method has potential for industrial application. It is suggested that further fast elemental composition characterization technologies and intelligent sorting equipment can be developed. Limitations and future perspectives were discussed from gasification simulation models, more energy utilization technologies, the underlying logic of waste sorting and sustainability evaluation. It is hoped that this work can provide insightful fundamentals to their applications in RMSW sorting, thus benefiting resource recovery and circular economy. [ABSTRACT FROM AUTHOR]

Co-pyrolysis is an advanced technique used to optimize biochar by combining different biomass feedstock and adjusting process conditions. The H/N ratio and yield of biochar, along with factors such as nutrient content and specific surface area, affect the adsorption and pore structure of soil amendments. This study proposed a modeling framework to predict these indicators by the feedstock properties and process conditions. Data enhancement method was used to solve the limited data size problem, which improved the average accuracy from 7.3% to 13.3%. Grid search method was utilized to determine the optimal parameters in three typical machine learning models, namely artificial neural networks, support vector machine, and random forest. The results revealed that the random forest model achieved the highest performance, with an accuracy rate of 92.8% and an average R2 of 0.95. Sensitivity analysis indicated that the reaction temperature and specific elements of the biomass feedstock were the primary factors influencing the biochar prediction model. Consequently, the random forest model was integrated into a heuristic algorithm to find the optimal H/N ratio and yield conditions. The optimal values of H/N ratio and yield obtained were 7.14 and 29.7%. The prediction and optimization of yield and H/N ratio will provide new visual insights into the use of biochar in soil improvement. [Display omitted] • Data enhancement improved model accuracies from an average of 7.3%–13.3%. • Interpretable machine learning was used to predict co-pyrolysis biochar properties. • The optimal RF model achieved the highest accuracy of 92.8%, outgoing SVM and ANN. • The RF model integrates into PSO to find optimal production conditions for biochar. [ABSTRACT FROM AUTHOR]

The use of smart wireless remote water meters has become popular due to their efficiency in avoiding manual labor and automate meter reading. The current implementation of these measurement instruments often requires expensive external power sources and separate data aggregators, posing challenges for circuit safety and the goal of reducing carbon emissions in aging communities. In response, a novel wireless system has been developed to accurately measure water consumption in buildings. This system incorporates advanced machine learning techniques and utilizes the potential of water-based energy in a seamless manner. This novel methodology utilizes optical sensors to acquire visual data of water gauges and exploits machine learning techniques, specifically the AVSM model also called Advanced Visual Sensing Model (AVSM). This methodology employs a rigorous approach to achieve accurate metric determinations, thereby eliminating the necessity for additional hardware or excessive power configurations. Moreover, an integrated micro water generator has been seamlessly implemented, demonstrating the capability to effectively transfer energy to a lithium battery for the purpose of storage. This implementation guarantees consistent functionality and a significant decrease in reliance on external power supplies. The data augmentation procedure involves the stochastic manipulation of image luminance, resulting in encouraging results when used in conjunction with the AVSM model. Significantly, the dimensions of the model have been significantly decreased, while maintaining a commendable mean average precision (mAP). By leveraging this method and visual-based features, the system can contribute to overall building energy efficiency and reduce the burdens of manual meter reading while supporting CO 2 emissions reduction as a feasible and cost-effective solution for building management, promoting sustainability, and advancing energy-efficient practices in old communities. This new wireless system for measuring water usage provides a practical and affordable way to enhance energy efficiency in buildings, lessen the workload of manual meter reading, and help meet carbon emissions reduction goals. As well as optimizing resource and energy use, this method also aligns with eco-friendly solutions to combat global warming and climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • A fast characterization method for biodiesel using ATR-FTIR and ML was proposed. • The optimal accuracy of the characterization method could reach as high as 100%. • Data downscaling improved the correlation of the biodiesel model to approx. 0.92. • PC4 and PC5 in the data downscaling had the highest weight in the biodiesel model. • This study is promising for the development of the biodiesel production industry. This study proposed a fast characterization method for biodiesel based on attenuated total reflection flourier-transform infrared spectroscopy and machine learning models. The concerning characteristics of biodiesel include unsaturated group content, O content, and contents of four representative esters. A total of 71 biodiesel samples were produced from a lab-scale reactor. Their spectral data and characteristics were collected and used as training data for machine learning models. The established model framework consisted of two data compression sections, a classification section, and a regression section, all of which use machine learning models, such as principal component analysis, support vector machine, artificial neural network, and random forest. The accuracy, correlation, and sensitivity of the proposed method were evaluated and optimized. Furthermore, the interpretation of the models was discussed. The results showed that the principal component analysis model was a satisfactory preprocessing procedure for the downstream classification and regression models. Under the optimal model parameters, the integrated framework could reach an average accuracy rate of 93.18% and a Pearson correlation coefficient of 0.92. Principal component number 4 and 5 showed the highest sensitivity towards the predicting results, implying that their highly weighted wavenumber ranges and the correlated functional groups played the most important role in the predicting process. The findings of this study could lead to a simple and efficient approach for characterizing the properties of biodiesel, which in turn could promote the development of similar biomass-derived liquid fuels. [ABSTRACT FROM AUTHOR]

• Hyperspectral data contain enough but obscure information for MSW characterization. • Machine learning helps utilizing the full range of hyperspectral pattern. • Accuracy for organic/inorganic components classification reached nearly 100%. • Predicting accuracies of C/H/O contents and LHV reached 92.6%, 86.9%, 80.4%, 90.5%. • This work provides promising fundamentals for downstream treatment of MSW. Determining thermochemical properties and eliminating inorganic components of municipal solid waste (MSW) are crucial to its thermochemical treatment. Traditional characterization and classification technologies have shortcomings including long duration, complex operation, and inevitable sample consumption. This study proposed a hyperspectral imaging and machine learning models based method to solve these problems. Under the optimal parameter conditions, the identification accuracy of inorganic components by F1 scoring reached nearly 100% in MSW, and the prediction accuracy of carbon, hydrogen, oxygen, nitrogen contents and low heating value (LHV)of organic components by mean relative error value reached 92.6%, 86.9%, 80.4%, 54.7% and 90.5%, respectively. The results validated the hypothesis that combination of hyperspectral imaging and machine learning models are promising to accomplish fast characterization and classification of components in MSW, where principal component analysis was capable to abstract crucial information from the spectral pattern, and artificial neural network presented satisfactory classification and regression performance. [Display omitted] [ABSTRACT FROM AUTHOR]

• A LIBS and machine learning based system was proposed to characterize residual wastes. • The optimal classification and characterization accuracy could reach 95%. • The working mechanism, robustness and features of the system were demonstrated. • The potential application scene and limitations of the system were discussed. • The system is promising to enhance downstream utilization of residual wastes. Elemental composition and heating value are essential properties of residual wastes (RW) for its energy utilization. This paper proposed a highly efficient approach to distinguish inorganic components and characterize organic compounds in RW via laser-induced breakdown spectroscopy (LIBS) and machine learning (ML) models. LIBS data of various RW samples were collected to train and test the hybrid model, which includes a data pretreatment module, a classification module and a regression module. Impacts of different ML model categories and parameters were investigated and discussed. Under optimal conditions, the accuracy for predicting C content, H content, O content and lower heating value reached 96.70%, 92.21%, 87.11% and 94.28%, respectively. The robustness of this system was validated. The future application of the model and their limitation were also discussed. This method provides innovative technical ideas for the identification and characterization of RW, and has important potential value for the energy treatment and utilization of RW. [Display omitted] [ABSTRACT FROM AUTHOR]

Kernel Risk-Sensitive Loss (KRSL) is a nonlinear similarity measure defined in kernel space, which enable the gradient based method to achieve higher accuracy while effectively weakening the negative effects caused by noise and outliers. Defined as a kernel function expectation between two random variables, KRSL has been successfully applied in robust machine learning and signal processing. Extreme Learning Machine, as one of the most popular methods of machine learning, has attracted great attention in supervised learning and semi-supervised learning. However, when the data contains noise and outliers, the manifold structure of the data is not considered or the neural network structure is too complex, the performance of traditional ELM methods will decline. Therefore, based on KRSL, hyper-graph regularization and L 2,1 -norm, we first propose a more robust ELM method named Kernel Risk-Sensitive Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRSL-HRELM). In KRSL-HRELM, KRSL is introduced into ELM to enhance its ability to handle noise and outliers. Moreover, the hyper-graph regularization is integrated into the method to learn the higher-order geometric structure information between the data. In addition, the L 2,1 -norm is introduced to constrain the output weight matrix to obtain a sparse network model. Inspired by other semi-supervised ELM methods, we extend KRSL-HRELM to semi-supervised learning and propose its semi-supervised version semi-supervised KRSL-HRELM (SS- KRSL-HRELM). Empirical studies on a large number of real-world datasets show that the proposed methods are competitive with other advanced supervised or semi-supervised learning methods in terms of robustness and efficiency. [ABSTRACT FROM AUTHOR]

• A new method is proposed to characterize chemical features of biomass and waste. • The method is based on infrared spectroscopy and machine learning models. • The method is fast and the optimal accuracy could reach as high as 95.54%. • The robustness of this method is validated, and its properties are discussed. • The method can enhance sorting of biomass and waste for downstream utilization. Heterogeneity is a most serious obstacle for treatment and utilization of biomass and waste (BW). This paper proposed a fast characterization method based on infrared spectroscopy and machine learning models, thus to roughly predict the elemental composition and heating value of BW. The fast characterization results could be used to sort different BW components by their suitable downstream utilization techniques. The infrared spectra based hybrid model contained a feature compression section to extract core information from raw infrared spectra, a classification section to distinguish inorganic dilution, and a regression section to generate the elemental composition and heating value results. By parameters optimization, the accuracy of this hybrid model reached 95.54%, 85.53%, 92.40%, and 92.49% for C content, H content, O content, and low heating value prediction, respectively. The robustness analysis was conducted by completely rearranging the training and test sets, and it further validated the hypothesis that the infrared spectra contains enough qualifying and quantifying information to characterize these properties of BW. Compared with previous literature, the C–H, C–O, and O–H correlations in BW were also well kept in the predicted results. This work is hoped to enhance upstream sorting system design for treatment and utilization of BW. [ABSTRACT FROM AUTHOR]

No observed adverse effect level (NOAEL) is an identified dose level which used as a point of departure to infer a safe exposure limit of chemicals, especially in food additives and cosmetics. Recently, in silico approaches have been employed as effective alternatives to determine the toxicity endpoints of chemicals instead of animal experiments. Several acceptable models have been reported, yet assessing the risk of repeated-dose toxicity remains inadequate. This study established robust machine learning predictive models for NOAEL at different exposure durations by constructing high-quality datasets and comparing different kinds of molecular representations and algorithms. The features of molecular structures affecting NOAEL were explored using advanced cheminformatics methods, and predictive models also communicated the NOAEL between different species and exposure durations. In addition, a NOAEL prediction tool for chemical risk assessment is provided (available at: https://github.com/ifyoungnet/NOAEL). We hope this study will help researchers easily screen and evaluate the subacute and sub-chronic toxicity of disparate compounds in the development of food additives in the future. [Display omitted] • Proposed a comprehensive in silico NOAEL prediction scheme. • Datasets of NOAEL under different species and exposure durations were constructed. • Established successful models by comparing different algorithms and descriptors. • Identified important sub-structures and indications for NOAEL. • Provided an easy-to-use pipeline for virtual screening based on KNIME software. [ABSTRACT FROM AUTHOR]

[Display omitted] • AD-GS process was simulated using combined M-ADM1 and T -ANN models. • Carbon emission reduction performance of AD-GS, namely FEAG, was optimized. • FEAG increased rapidly before AD of 15 days, but decreased smoothly after 15 days. • The optimal FEAG on day 15 is attributed to a mass flow balance towards fuel gas and fertilizer. • Products control after day 15 could be achieved without significant FEAG change. In this study, the anaerobic digestion model M-ADM1 was integrated with the gasification model T -ANN to form a set of integrated models that can efficiently simulate the biomass AD-GS integration technology. Biogas slurry is used as feedstocks to prepare biogas slurry fertilizer. Solid residue is used feedstocks for gasification reactions. Biogas and syngas from the gasification of solid residue are used for energy. In this process, carbon emission is regarded as an important index for the comprehensive evaluation and optimization of AD-GS integration process. This study found that when the anaerobic digestion duration was 0 to 15 days, the carbon emission reduction increased rapidly. The amount of carbon emission reduction peaks on day 15. The value of carbon emission reduction is 0.1828 gCO 2 eq. In addition, when FEAG reached the maximum value at 15 days of anaerobic digestion, the decreasing trend of FEAG rate change value started to become significant. [ABSTRACT FROM AUTHOR]