Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models

Jeong, Kwanho; Abbas, Ather; Shin, Jingyeong; Son, Moon; Kim, Young Mo; Cho, Kyung Hwa

doi:10.1016/j.watres.2021.117697

Cited by 55 publications

(32 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detailed information on the sampling frequency and distribution of the data can be found in the supporting information. However, the key parameters that represent the composition of AD feedstock, such as BOD, COD, and TN (Jeong et al, 2021; Karamichailidou et al, 2022; Wang et al, 2021), were not measured at the SIUE WWTP. In this study, if a parameter was measured periodically, the measured value was used to represent the entire duration.…”

Section: Methodsmentioning

confidence: 99%

“…There are very few works to predict biogas production at full-scale WWTPs using ML algorithms such as Random Forest (RF) models, eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) regression (de Clercq et al, 2019(de Clercq et al, , 2020Wang et al, 2021). A success story in this regard is the development of a deep learning model that predicted biogas production from anaerobic codigestion processes in a full-scale WWTP based on 17 process features collected over 2 years, including biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in the primary and secondary sludges as well as the sludge feeding into AD operator (Jeong et al, 2021). Such a success is built on the quality set of data, which as mentioned earlier may not be the case in many small WWTPs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

Sun

Nairat

et al. 2023

Water Environment Research

View full text Add to dashboard Cite

Anaerobic digestion (AD) of sludge is a key approach to recover useful bioenergy from wastewater treatment and its stable operation is important to a wastewater treatment plant (WWTP). Because of various biochemical processes that are not fully understood, AD operation can be affected by many parameters and thus modeling AD processes becomes a useful tool for monitoring and controlling their operation. In this case study, a robust AD model for predicting biogas production was developed using ensembled machine learning (ML) model based on the data from a full-scale WWTP. Eight ML models were examined for predicting biogas production and three of them were selected as metamodels to create a voting model. This voting model had a coefficient of determination (R 2 ) at 0.778 and a root mean square error (RMSE) of 0.306, outperformed individual ML models. The Shapley additive explanation (SHAP) analysis revealed that returning activated sludge and temperature of wastewater influent were important features, although they affected biogas production in different ways. The results of this study have demonstrated the feasibility of using ML models for predicting biogas production in the absence of high-quality data input and improving model prediction through assembling a voting model. Practitioner Points• Machine learning is applied to model biogas production from anaerobic digesters at a full-scale wastewater treatment plant.• A voting model is created from selected individual models and exhibits better performance of predication.• In the absence of high quality data, indirect features are identified to be important to predicting biogas production. K E Y W O R D Sanaerobic digestion, ensembled model, sensitivity analysis, wastewater treatment plant Both Jianpeng Zhou and Zhen He are WEF members.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

Sun

Nairat

et al. 2023

Water Environment Research

View full text Add to dashboard Cite

show abstract

“…While the linear regression models have simple and understandable inputs, the co-digestion process likely contained nonlinearities that were not captured by simple models. ML models have the drawback that they provide no insight into mechanistic connections between independent variables and observations; however, they are more robust to co-correlated variables, such as those found in time-series problems such as this one and have been frequently applied for biogas prediction. ,, …”

Section: Methodsmentioning

confidence: 99%

“…ML models have the drawback that they provide no insight into mechanistic connections between independent variables and observations; however, they are more robust to co-correlated variables, such as those found in time-series problems such as this one and have been frequently applied for biogas prediction. 7,10,13 We used an automated ML (AutoML) tool to create a robust ML pipeline without biases for model selection, feature standardization, and hyperparameter optimization. To do so, we used the Tree-based Pipeline Optimization Tool (tpot library in Python), which selects feature preprocessing operators, conducts feature selection, combines optimal models, and conducts hyperparameter optimization using genetic programming to compile an ML pipeline.…”

Section: Tree-based Pipeline Optimizationmentioning

confidence: 99%

“…Other studies that used ML models to predict biogas production were lacking a sufficient number of samples (<250) for accurate or generalizable performance, and are reviewed elsewhere. , In summary, small sample sizes, controlled laboratory-scale systems, and some fundamental errors have resulted in studies that are difficult to use for comparison here. There is one paper that we highlight as applicable that used deep learning models on daily co-digestion data from a full-scale facility to predict biogas flow …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feature Engineering and Supervised Machine Learning to Forecast Biogas Production during Municipal Anaerobic Co-Digestion

Schroer,

Just

2023

ACS EST Eng.

View full text Add to dashboard Cite

Municipalities with excess anaerobic digestion capacity accept offsite wastes for co-digestion to meet sustainability goals and create more biogas. Despite the benefits inherent to codigestion, the temporal and compositional heterogeneity of external waste streams creates operational challenges that lead to upsets or conservative co-digestion. Given the complex microbial bioprocesses occurring during anaerobic digestion, prediction and modeling of the outcomes can be challenging, and machine learning has the potential to improve understanding and control of co-digestion processes. Biogas flows are a surrogate for process health, and here, we predicted biogas production from historical data collected by a water resource recovery facility (WRRF) during normal operation. We tested a daily lab and operational data set (n = 1089 after cleaning) and a minute-by-minute supervisory control and data acquisition (SCADA) operational data set (n = 491,761 after cleaning) to determine if forecasting biogas flow for a 24 h time horizon is feasible without collecting additional data. We found that a multilayer perceptron (MLP) neural network model outperformed tree-based and multiple linear regression models. Using a high-resolution SCADA data set for the first time, we showed that MLP neural networks could predict biogas production with an adjusted coefficient of determination (R 2 ) of 0.78 and a mean absolute percentage error of 13.4% on a holdout test set. Adding daily laboratory analyses to the model did not appreciably improve the prediction of biogas flows. Feature engineering was essential to an accurate prediction, and 11 of the 15 most important features in the SCADA model were calculated from raw SCADA outputs. In summary, this paper demonstrates that minute-scale SCADA information collected at a municipal co-digestion facility can forecast biogas production, as a first step toward a digital twin model, without additional data collection.

show abstract

A Machine Learning-Based Framework for Circular and Smart Waste Management

Hala,

Asmaa,

Anass

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models

Cited by 55 publications

References 29 publications

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

Feature Engineering and Supervised Machine Learning to Forecast Biogas Production during Municipal Anaerobic Co-Digestion

A Machine Learning-Based Framework for Circular and Smart Waste Management

Contact Info

Product

Resources

About