Although artificial intelligence (AI) such as machine
learning
(ML) and deep learning (DL) has been recognized as an emerging and
promising tool, its application becomes challenging with incomplete
data collection. Herein, in the absence of the influent phosphorus
load and chemical dosage data for phosphorus removal, we employed
ML/DL models to predict effluent phosphorus using nine-year data from
a small-scale wastewater treatment plant. Attempts were made to select
essential model input features from 42 variables by using Pearson
correlation analysis to reveal internal correlations among variables.
First, five ML regression models were used to predict the effluent
phosphorus load, and a maximum coefficient of determination (R
2) of 0.637 was achieved with the support vector
machine model. Then, the DL model named long short-term memory could
predict phosphorus load in one-day advance with an R
2 value of 0.496. Finally, on the basis of the historical
data, an anomaly alarm design was proposed to minimize the chance
of exceeding the discharge permit and achieved a maximum accuracy
of 79.7% to predict the phosphorus concentration after comparing seven
ML classification models. This study provides an example of applying
AI for process improvement and potential cost reduction with incomplete
data sets.
Volatile fatty acids (VFAs) can be accumulated as a final product of anaerobic digestion via arresting methanogenesis. Herein, hydrogen peroxide (H2O2) was studied to inhibit methanogenesis for enhancing VFA accumulation with glucose as a substrate. The addition of 0.06 wt.% H2O2 significantly reduced methane production and led to a VFAs concentration of 1233.1 ± 55.9 mg L−1, much higher than 429.3 ± 5.6 mg L−1 in the control that did not have H2O2 addition. The dominated VFAs with H2O2 were acetic acid and propionic acid. A low H2O2 dosage of 0.03 wt.% produced 466.3 ± 3.9 mg L−1 more VFAs than that of O2 addition at the similar (theoretical) dosage, but when the dosage was relatively higher, the VFA accumulation with O2 addition became more than that with H2O2 addition, likely because of stronger oxidation of VFAs by the overly added H2O2. A hypothetical mechanism for H2O2 inhibition suggests that at a low H2O2 concentration the inhibition is mainly toward methanogenesis to limit their consumption of VFAs and a high H2O2 concentration starts to inhibit hydrolysis and acidogenesis and/or oxidize VFAs. Those results encourage further exploration of H2O2‐based arresting methanogenesis for VFAs production.
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.
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