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Machine Learning for Predicting and Optimizing the Performance of a Commercial-Scale Anaerobic Digester with Diverse Feedstocks and Operating Conditions

https://doi.org/10.2139/ssrn.5253169
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Anaerobic digestion technology in livestock manure treatment for biogas production: A review
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Why do sulfate-reducing bacteria outcompete methanogenic bacteria for substrates?
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Effects of short-term temperature increases on the mesophilic anaerobic breakdown of sulfate containing synthetic wastewater
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Modeling of enhanced VFAs production from waste activated sludge by modified ADM1 with improved particle swarm optimization for parameters estimation
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A hybrid approach of anaerobic digestion model no. 1 and machine learning to model and optimize continuous anaerobic digestion processes
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Competitive reaction kinetics of sulfate-reducing bacteria and methanogenic bacteria in anaerobic filters
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Anaerobic digestion of food waste – Challenges and opportunities
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Hydrogen sulfide formation control and microbial competition in batch anaerobic digestion of slaughterhouse wastewater sludge: Effect of initial sludge pH
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Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms
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Process stability in anaerobic Digestion: Unveiling microbial signatures of full-scale reactor performance
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Impact of hydrogen sulfide on biochar in stimulating the methane oxidation capacity and microbial communities of landfill cover soil
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Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms
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Anaerobic digestion for global warming control and energy generation—An overview
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Application and development of biogas technology for the treatment of waste in China
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Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion
inferredcites →https://doi.org/10.1016/j.soilbio.2015.02.006openalex-snapshot
Manure-based biogas fermentation residues – Friend or foe of soil fertility?
inferredcites →https://doi.org/10.1016/j.wasman.2008.02.005openalex-snapshot
Effect of seasonal changes in quantities of biowaste on full scale anaerobic digester performance
inferredcites →https://doi.org/10.1016/j.watres.2005.02.001openalex-snapshot
Anaerobic digestion of the organic fraction of municipal solid waste: Influence of co-digestion with manure
inferredcites →https://doi.org/10.1021/acs.est.7b01413openalex-snapshot
Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks
inferredcites →https://doi.org/10.1080/10643389.2015.1010427openalex-snapshot
Emissions and Control of Hydrogen Sulfide at Landfills: A Review
inferredcites →https://doi.org/10.1109/34.709601openalex-snapshot
The random subspace method for constructing decision forests
inferredcites →https://doi.org/10.1126/science.220.4598.671openalex-snapshot
Optimization by Simulated Annealing
inferredcites →https://doi.org/10.1515/med-2019-0010openalex-snapshot
Analysis of pH dose-dependent growth of sulfate-reducing bacteria
inferredcites →https://doi.org/10.2139/ssrn.4142240openalex-snapshot
Modification of Anaerobic Digestion Model No.1 with Machine Learning Models Towards Applicable and Accurate Simulation of Biomass Anaerobic Digestion
inferredcites →https://doi.org/10.2139/ssrn.4271084openalex-snapshot
Prediction and Optimization of Medium-Chain Carboxylic Acids Production from Food Waste Using Machine Learning Models
inferredcites →https://doi.org/10.3389/fmicb.2022.856355openalex-snapshot
Combined Application of Manure and Chemical Fertilizers Alters Soil Environmental Variables and Improves Soil Fungal Community Composition and Rice Grain Yield
inferredcites →https://doi.org/10.3390/fermentation8020065openalex-snapshot
Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process
inferredcites →https://doi.org/10.48550/arxiv.1603.02754openalex-snapshot
XGBoost: A Scalable Tree Boosting System
inferredcites →https://doi.org/10.7717/peerj-cs.623openalex-snapshot
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
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