Enhancing Machine Learning models of biogas production in wastewater treatment plants using hybrid Bayesian optimization based on Gaussian process
ガウス過程に基づくハイブリッドベイズ最適化を用いた下水処理場におけるバイオガス生産の機械学習モデル向上 (AI 翻訳)
SADOUNE H, RIHANI R, MARRA FS
🤖 gxceed AI 要約
日本語
本研究は、産業廃水処理プラントにおけるバイオガス生産量予測のために、DNNとXGBoostの機械学習モデルを比較し、ガウス過程を用いたベイズ最適化（BO-GP）でハイパーパラメータを最適化した。DNNがXGBoostを上回る性能を示し（R²=0.78）、学習率が最も影響力の高いハイパーパラメータであることが判明した。この結果は、嫌気性消化プロセスにおけるバイオガス生成のモデリングに高度なデータ駆動技術を適用する可能性を示している。
English
This study compares DNN and XGBoost models for predicting biogas production in an industrial wastewater treatment plant, using Bayesian optimization with Gaussian processes for hyperparameter tuning. DNN outperformed XGBoost (R²=0.78), and learning rate was the most influential hyperparameter. The findings demonstrate the potential of ML for modeling biogas formation in anaerobic digestion, contributing to renewable energy optimization.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の下水処理施設ではエネルギー回収が重要視されており、本研究成果はバイオガス生産の最適化を通じて再生可能エネルギー導入促進に寄与する可能性がある。また、SSBJに基づくエネルギー消費削減やScope1・2排出削減にも間接的に貢献し得る。
In the global GX context
Biogas from wastewater is a key renewable energy source globally. This study demonstrates ML-driven optimization that can improve energy recovery and reduce GHG emissions in wastewater treatment, aligning with energy transition goals under TCFD/ISSB frameworks.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of DNN vs XGBoost with Bayesian optimization for biogas prediction, useful for ML practitioners in renewable energy.
🏢実務担当者:Wastewater treatment plant operators can apply this method to optimize biogas production and reduce operational costs.
📄 Abstract（原文）
<title>Abstract</title> <p>This study aims to compare machine learning (ML) models, particularly Deep Neural Networks (DNN) and Extreme Gradient Boosting (XGBoost), to predict biogas production in an industrial wastewater treatment plant. A Bayesian Optimization approach using Gaussian Processes (BO-GP) was selected to optimize the hyperparameters of these models. The daily operational data from July 2019 to January 2022 were used to train and evaluate the models. The DNN model outperformed XGBoost, achieving R-squared, MAE and RMSE values of 0.7809, 148.0927 and 198.7302, respectively, compared with the values of 0.73, 163.2680 and 216.2166 obtained with XGBoost. The BO-GP approach effectively identified the optimal hyperparameters for both models, with the DNN utilizing a simple architecture of two hidden layers with 91 neurons each. Convergence analysis demonstrated that the optimization process efficiently explored the hyperparameter space, with the DNN showing more significant improvements. For the DNN, learning rate was found to be the most influential hyperparameter. This study demonstrates the potential of machine learning approaches, particularly DNNs optimized using BO-GP, for modeling biogas formation amid anaerobic digestion processes. The findings contribute to applying advanced data-driven techniques in wastewater treatment and renewable energy production.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-9917351/v1first seen 2026-06-20 04:48:59 · last seen 2026-07-02 04:48:37
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