Anaerobic digestion of a curious VFA complex feed for biomethane production; A study on ANN modeling optimized with genetic algorithm DOI Creative Commons
Armin Rahimieh, Mohsen Nosrati, Seyed Morteza Zamir

et al.

Desalination and Water Treatment, Journal Year: 2024, Volume and Issue: 317, P. 100257 - 100257

Published: Jan. 1, 2024

Anaerobic digestion is a complex biological process widely used for organic waste treatment and biogas production. Understanding the intermediate stages biochemicals essential effective management. This study uses ANN modeling as well genetic algorithm optimization to explore predict how these intermediates behave. By scrutinizing interactions between VFAs CH4 production, within context of our VFA Complex Feed characterized by unique concentrations, this model underscores paramount significance three VFAs: acetate, propionate, butyrate. Notably, in distinctive study, contrary prior research, acetate manifests deleterious influence on production (CI = -1.92), whereas propionate +1.22) butyrate +1.14) exhibit favorable impact. exerts most substantial absolute (AAS +4.7) when juxtaposed with other VFAs. These results support supporting its validity. combining machine learning theoretical knowledge, advances comprehension anaerobic offers valuable insights optimizing process.

Language: Английский

Applying machine learning to anaerobic fermentation of waste sludge using two targeted modeling strategies DOI

Shixin Zhai,

Kai Chen, Lisha Yang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 916, P. 170232 - 170232

Published: Jan. 24, 2024

Language: Английский

Citations

7
Machine learning modeling for optimization of sulfur compounds separation from fuels: Process optimization for reduction of environmental pollution DOI Creative Commons
Ali E. Anqi

Case Studies in Thermal Engineering, Journal Year: 2023, Volume and Issue: 45, P. 102989 - 102989

Published: April 13, 2023

Development of theoretical models for reduction sulfur emission and also the material consumption is great importance petroleum refinery to obtain high-quality fuels. The latter can be done by employing advanced optimization techniques. In this study, we have developed a modeling methodology in fuel production. Some measured data been collected computational optimization. Each point comprised four input characteristics: reactor pressure (bar), temperature (°C), initial concentration (ppm), dose (g). Outputs include (%), HDS cost ($). For modeling, Adaboost ensemble model applied on top three fundamental Linear Regression, Gaussian Process Bayesian Ridge. On available dataset, are tweaked using grasshopper algorithm (GOA) method, then optimal combination parameters selected each output. content characteristics, ADA-GPR most accurate; however, ADA-BRR performs best calculating cost. Using these models, R2-score outputs 0.970, 0.950, 0.999, respectively concentration, percentage SO2,

Language: Английский

Citations

14
A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion DOI Creative Commons
Harvey Rutland, Jiseon You, Haixia Liu

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(12), P. 1410 - 1410

Published: Dec. 11, 2023

The use of machine learning (ML) in anaerobic digestion (AD) is growing popularity and improves the interpretation complex system parameters for better operation optimisation. This systematic literature review aims to explore how ML currently employed AD, with particular attention challenges implementation benefits integrating techniques. While both lab industry-scale datasets have been used model training, arise from varied designs different monitoring equipment used. Traditional machine-learning techniques, predominantly artificial neural networks (ANN), are most commonly but face difficulties scalability interpretability. Specifically, models trained on lab-scale data often struggle generalize full-scale, real-world operations due complexity variability bacterial communities operations. In practical scenarios, can be real-time predictive modelling, ensuring stability maintained, resulting improved efficiency biogas production waste treatment processes. Through reviewing techniques wider applied domains, potential future research opportunities addressing these identified.

Language: Английский

Citations

11
Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study DOI
Neslihan Manav Demır,

Huseyin Baran Gelgor,

Ersoy Öz

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119899 - 119899

Published: Dec. 30, 2023

Language: Английский

Citations

11
Anaerobic digestion of a curious VFA complex feed for biomethane production; A study on ANN modeling optimized with genetic algorithm DOI Creative Commons
Armin Rahimieh, Mohsen Nosrati, Seyed Morteza Zamir

et al.

Desalination and Water Treatment, Journal Year: 2024, Volume and Issue: 317, P. 100257 - 100257

Published: Jan. 1, 2024

Anaerobic digestion is a complex biological process widely used for organic waste treatment and biogas production. Understanding the intermediate stages biochemicals essential effective management. This study uses ANN modeling as well genetic algorithm optimization to explore predict how these intermediates behave. By scrutinizing interactions between VFAs CH4 production, within context of our VFA Complex Feed characterized by unique concentrations, this model underscores paramount significance three VFAs: acetate, propionate, butyrate. Notably, in distinctive study, contrary prior research, acetate manifests deleterious influence on production (CI = -1.92), whereas propionate +1.22) butyrate +1.14) exhibit favorable impact. exerts most substantial absolute (AAS +4.7) when juxtaposed with other VFAs. These results support supporting its validity. combining machine learning theoretical knowledge, advances comprehension anaerobic offers valuable insights optimizing process.

Language: Английский

Citations

4