{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T13:31:55Z","timestamp":1764941515920,"version":"3.46.0"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372494"],"award-info":[{"award-number":["62372494"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models\u2014support vector machine (SVM), random forest (RF), and artificial neural network (ANN)\u2014were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m3\/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (&gt;85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (\u201csensor \u2192 prediction \u2192 programmable logic controller (PLC)\/operation \u2192 feedback\u201d), the ANN model was associated with a reduction in gas-yield fluctuation from approximately \u00b118% to \u00b15%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12\u201315 USD\/t lower operating cost, 8\u201310% energy savings, and 5\u20137% CO2 reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation.<\/jats:p>","DOI":"10.3390\/e27121233","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T10:50:38Z","timestamp":1764931838000},"page":"1233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction, Uncertainty Quantification, and ANN-Assisted Operation of Anaerobic Digestion Guided by Entropy Using Machine Learning"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4574-9709","authenticated-orcid":false,"given":"Zhipeng","family":"Zhuang","sequence":"first","affiliation":[{"name":"School of Life Sciences, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3694-5928","authenticated-orcid":false,"given":"Xiaoshan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]},{"given":"Jing","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]},{"given":"Ziwen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]},{"given":"Yanheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]},{"given":"Adriano","family":"Tavares","sequence":"additional","affiliation":[{"name":"Industrial Electronics Department, University of Minho, 4800-058 Guimaraes, Portugal"}]},{"given":"Dalin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhang, Y., and Li, H. 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