Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process

Kim, Moonil; Park, Chul; Kim, Wan; Cui, Fenghao

doi:10.1016/j.chemosphere.2022.134444

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…COD, carbohydrate, protein solubility and VFA concentration have considerable positive loading on the first component, while pH has a substantial negative loading on the second component. The angle between the parameters in the loading plot indicates their correlation (Kim et al, 2022). The smaller angle between the biogas yield and pH determines their close correlation.…”

Section: Discussionmentioning

confidence: 99%

Biogas enhancement in the anaerobic digestion of thermo-chemically pretreated sludge by stimulating direct interspecies electron transfer by biochar and graphene

Almegbl,

Munshi,

Khursheed

2024

Front. Chem. Eng.

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It is necessary to pretreat waste-activated sludge (WAS) to disintegrate the sludge matrix and amend its anaerobic digestion (AD) with carbon-based materials (CMs) to accelerate direct interspecies electron transfer (DIET) in order to realize the maximum biogas potential of abundant and habitat-threatening organic WAS. The AD of WAS pretreated thermo-chemically at 0.5% NaOH (g/g dry sludge) and 125 °C microwave irradiation was amended by biochar doses of 0–40 g/L and graphene doses of 50–1,000 mg/L in the batch operation mode. Hybrid pretreatment of WAS deteriorated dewaterability but solubilized 38% of total chemical oxygen demand (COD). AD amended with 20 g/L biochar and 100 mg/L graphene had the optimum accumulative methane yield of 183.6 and 153.8 mL/gVS, respectively, which correspond to 42.8% and 24.8% increases compared to an unamended control assay with maximum methane content of 70.3% and 71.9%, respectively. The digestate of biochar- and graphene-amended assays resulted in higher TS% and alkalinity, reduced sCOD, VFA, and turbidity, and increased particle size distribution compared to control. Biochar-amended digestate had improved dewaterability, while digestate of graphene-amended assays resulted in worse dewaterability than control. The t-test showed a significant difference between the biochar and graphene amended batch assays, while principal component analysis (PCA) indicated that biogas yield was closely correlated with pH. CM-amended batch assays demonstrated superb fitting with modified Gompertz, logistic, and first-order models with a coefficient of determination above 0.97. Microbial community abundance and diversity were affected by CMs amendment, resulting in increased acetoclastic methanogen growth and transformed methanogenic metabolic pathways. An extended pilot-scale study and techno-economic and life cycle assessments are required to investigate environmental impacts and feasibility.

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Section: Discussionmentioning

confidence: 99%

Biogas enhancement in the anaerobic digestion of thermo-chemically pretreated sludge by stimulating direct interspecies electron transfer by biochar and graphene

Almegbl,

Munshi,

Khursheed

2024

Front. Chem. Eng.

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Section: Comprehensive Indicators Based On Pcamentioning

confidence: 99%

“…The eigenvalues reflect the extent of variance in the data along the principal axes identified by PCA, and eigenvectors indicate the direction of this variance [20,21]. When PCA was applied to the monitored data from the anaerobic digester, the eigenvalues rapidly fluctuated over time (Figure 4a), which might reflect comprehensive changes in the state of anaerobic digestion [18,21]. The first principal component (PC1) explained up to 85.6% of the total variance in the state and performance data, while the second principal component (PC2) only accounted for 14.1% of the total variance.…”

Section: Comprehensive Indicators Based On Pcamentioning

confidence: 99%

“…The state of anaerobic digestion can be better assessed across various operating modes by analyzing all monitored data simultaneously. Principal component analysis (PCA) is a statistical technique that can be used for simplifying complex multidimensional data while uncovering underlying patterns and structures within the data [20][21][22]. So, PCA can be a useful tool for the comprehensive diagnosis of state changes within complex systems such as anaerobic digestion.…”

Section: Introductionmentioning

confidence: 99%

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A New Comprehensive Indicator for Monitoring Anaerobic Digestion: A Principal Component Analysis Approach

Jia,

Song,

et al. 2023

Processes

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This paper has proposed a comprehensive indicator based on principal component analysis (PCA) for diagnosing the state of anaerobic digestion. Various state and performance variables were monitored under different operational modes, including start-up, interruption and resumption of substrate supply, and impulse organic loading rates. While these individual variables are useful for estimating the state of anaerobic digestion, they must be interpreted by experts. Coupled indicators combine these variables with the effect of offering more detailed insights, but they are limited in their universal applicability. Time-series eigenvalues reflected the anaerobic digestion process occurring in response to operational changes: Stable states were identified by eigenvalue peaks below 1.0, and they had an average below 0.2. Slightly perturbed states were identified by a consistent decrease in eigenvalue peaks from a value of below 4.0 or by observing isolated peaks below 3.0. Disturbed states were identified by repeated eigenvalue peaks over 3.0, and they had an average above 0.6. The long-term persistence of these peaks signals an increasing kinetic imbalance, which could lead to process failure. Ultimately, this study demonstrates that time-series eigenvalue analysis is an effective comprehensive indicator for identifying kinetic imbalances in anaerobic digestion.

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“…Using the Matplotlib and Seaborn libraries, we plot the distributions of the volume of solid organic substances (TS, At the next stage, a description of the data preparation process is carried out, with the detection of gaps and the filling of missing values. Data preparation is an important process of any machine learning method [37][38][39][40][41][42]. It includes a number of tasks such as cleaning, standardizing, scaling, and sampling data.…”

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confidence: 99%

Prediction of Biogas Production Volumes from Household Organic Waste Based on Machine Learning

Tryhuba,

Hutsol

et al. 2024

Energies

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The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, “Random Forest Regressor” and “Gradient Boosting Regressor”, show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the “Random Forest Regressor” and “Gradient Boosting Regressor” models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the “Random Forest Regressor” model on the training data set is 7.14 times smaller than that of the “Gradient Boosting Regressor” model. At the same time, MAE is 2.67 times smaller in the “Random Forest Regressor” model than in the “Gradient Boosting Regressor” model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the “Random Forest Regressor” algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the “Random Forest Regressor” model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages.

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Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process

Cited by 11 publications

References 18 publications

Biogas enhancement in the anaerobic digestion of thermo-chemically pretreated sludge by stimulating direct interspecies electron transfer by biochar and graphene

Biogas enhancement in the anaerobic digestion of thermo-chemically pretreated sludge by stimulating direct interspecies electron transfer by biochar and graphene

A New Comprehensive Indicator for Monitoring Anaerobic Digestion: A Principal Component Analysis Approach

Prediction of Biogas Production Volumes from Household Organic Waste Based on Machine Learning

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