Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 441, P. 141043 - 141043

Published: Jan. 31, 2024

Language: Английский

---

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Biofuels Bioproducts and Biorefining, Journal Year: 2024, Volume and Issue: 18(2), P. 567 - 593

Published: Feb. 5, 2024

Abstract Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand sustainable energy. Efficient management systems are needed in order exploit fully of biochar. Modern machine learning (ML) techniques, and particular ensemble approaches explainable AI methods, valuable forecasting properties efficiency biochar properly. Machine‐learning‐based forecasts, optimization, feature selection critical improving techniques. In this research, we explore influences these techniques on accurate yield range sources. We emphasize importance interpretability model, improves human comprehension trust ML predictions. Sensitivity analysis shown be an effective technique finding crucial characteristics that influence synthesis Precision prognostics have far‐reaching ramifications, influencing industries such logistics, technologies, successful use renewable These advances can make substantial contribution greener future encourage development circular biobased economy. This work emphasizes using sophisticated data‐driven methodologies synthesis, usher ecologically friendly energy solutions. breakthroughs hold key more environmentally future.

Language: Английский

---

Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning

Materials Today Sustainability, Journal Year: 2024, Volume and Issue: 27, P. 100900 - 100900

Published: June 29, 2024

Language: Английский

---

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment

Chemosphere, Journal Year: 2023, Volume and Issue: 350, P. 141010 - 141010

Published: Dec. 26, 2023

Language: Английский

---

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 107, P. 105434 - 105434

Published: April 28, 2024

Language: Английский

---

Assessing bioenergy prospects of algal biomass and yard waste using an integrated hydrothermal carbonization and pyrolysis (HTC–PY): A detailed emission–to–ash characterization via diverse hyphenated analytical techniques and modelling strategies

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 492, P. 152335 - 152335

Published: May 18, 2024

Language: Английский

---

Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(5), P. 113435 - 113435

Published: June 26, 2024

Language: Английский

---

4E optimization comparison of different bottoming systems for waste heat recovery of gas turbine cycles, internal combustion engines, and solid oxide fuel cells in power-hydrogen production systems

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 187, P. 549 - 580

Published: May 7, 2024

Gas turbine cycles (GTC), internal combustion engines (ICE), and solid oxide fuel cells (SOFC) are three important sources of waste energy, although some studies have been done about their heat recovery (WHR) systems individually, there is a lack study comparing them to select the best solution. In present research, steam Rankine cycle, CO2 supercritical Brayton cycle (SBC), inverse (IBC), air bottoming used for WHR high-temperature exhausted gas 500 kW natural gas-fueled GTC ICE. Furthermore, organic (ORC), trilateral flash Kalina SBC utilized SOFC-gas (GT). The performance 13 proposed configurations compared through 4E (energy, exergy, exergy-economic, environmental) three-objective optimizations. Considering exergy efficiency, total cost rate, unit products as target functions, SOFC-GT-ORC system has with 64.74%, 92.51 $/h, 19.03 $/GJ. GTC-IBC ICE-IBC in respective categories.

Language: Английский

---

An application of a genetic algorithm in co-optimization of geological CO2 storage based on artificial neural networks

Clean Energy, Journal Year: 2024, Volume and Issue: 8(1), P. 111 - 125

Published: Jan. 10, 2024

Abstract Global warming, driven by human-induced disruptions to the natural carbon dioxide (CO2) cycle, is a pressing concern. To mitigate this, capture and storage has emerged as key strategy that enables continued use of fossil fuels while transitioning cleaner energy sources. Deep saline aquifers are particular interest due their substantial CO2 potential, often located near fuel reservoirs. In this study, deep aquifer model with water production well was constructed develop optimization workflow. Due time-consuming nature each realization numerical simulation, we introduce surrogate derived from extracted data. The novelty our work lies in pioneering simultaneous using machine learning within an integrated framework. Unlike previous studies, which typically focused on single-parameter optimization, research addresses gap performing multi-objective for breakthrough time data-driven model. Our methodology encompasses preprocessing feature selection, identifying eight pivotal parameters. Evaluation metrics include root mean square error (RMSE), absolute percentage (MAPE) R2. predicting values, RMSE, MAPE R2 test data were 2.07%, 1.52% 0.99, respectively, blind data, they 2.5%, 2.05% 0.99. For time, 2.1%, 1.77% 0.93, 2.8%, 2.23% 0.92, respectively. addressing computational demands coupling simulator algorithm, have adopted trained artificial neural network seamlessly genetic algorithm. Within framework, conducted 5000 comprehensive experiments rigorously validate development Pareto front, highlighting depth approach. findings study promise insights into interplay between aquifer-based processes framework based coupled optimization.

Language: Английский

---

A comprehensive review on biomass energy system optimization approaches: Challenges and issues

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 106, P. 1167 - 1183

Published: Feb. 8, 2025

Language: Английский

---