Biomass: A Versatile Resource for Biofuel, Industrial, and Environmental Solution

International Journal on Advanced Science Engineering and Information Technology, Journal Year: 2024, Volume and Issue: 14(1), P. 268 - 286

Published: Feb. 24, 2024

Biomass, noted for its adaptability, has various applications in biofuel generation, industrial use, and environmental cleaning. This study looks into the multiple roles of biomass as a renewable energy source, with particular emphasis on vital contribution to production. Through thorough evaluation different conversion routes—thermal, biological, physical—the emphasizes thermochemical processes' efficiency, cost-effectiveness, adaptability. Notably, technologies like gasification quick pyrolysis are thoroughly investigated, followed by in-depth discussions reactor optimization strategies enhance performance output. The complex structure biomass, which is dominated high-molecular-weight polysaccharides such cellulose hemicelluloses, demonstrates significant potential generation. Furthermore, categorizes content, origin, processes, resulting comprehensive inventory available resources. Biomass from agriculture forestry industries, starch, sugar, lignocellulose, organic wastes, rigorously analyzed processing techniques, including thermochemical, biochemical, physicochemical conversions, carefully tested real-world ensure their efficacy viability. Beyond importance production, article underlines biomass' versatility satisfying needs contributing cleanup initiatives. lays groundwork informed decision-making innovative solutions industries providing understanding biomass's benefits applications, provision, ecological restoration.

Language: Английский

---

Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: 21(12), P. 2771 - 2798

Published: March 14, 2024

Examining the game-changing possibilities of explainable machine learning techniques, this study explores fast-growing area biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified enhanced forecasting output composition from various biomass sources. argues that white-box models, which are more open comprehensible, crucial for prediction light increasing suspicion black-box models. Accurate forecasts guaranteed by these AI systems, also give detailed explanations mechanisms generating outcomes. For models to gain confidence processes enable informed decision-making, there must be an emphasis on interpretability openness. comprehensively synthesizes most critical features a rigorous assessment current literature relies authors' own experience. Explainable encourage ecologically responsible decision-making improving forecast accuracy transparency. Biochar is positioned as participant solving global concerns connected soil health climate change, ultimately contributes wider aims environmental sustainability renewable energy consumption.

Language: Английский

---

Functionalized magnetite-biochar with live and dead bacteria for adsorption-biosorption of highly toxic metals: Cd, Hg, and Pb

Next Materials, Journal Year: 2025, Volume and Issue: 6, P. 100487 - 100487

Published: Jan. 1, 2025

Language: Английский

---

Machine learning-driven optimization of pretreatment and enzymatic hydrolysis of sugarcane bagasse: Analytical insights for industrial scale-up

Fuel, Journal Year: 2025, Volume and Issue: 390, P. 134682 - 134682

Published: Feb. 19, 2025

Language: Английский

---

Precision biochar yield forecasting employing random forest and XGBoost with Taylor diagram visualization

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 27, 2025

Abstract Waste-to-energy conversion via pyrolysis has attracted increasing attention recently owing to its multiple uses. Among the products of this process, biochar stands out for versatility, with yield influenced by various factors. Extensive and labor-intensive experimental testing is sometimes necessary properly grasp output distribution from feedstocks. Nonetheless, data-driven predictive models using large-scale historical experiment records can provide insightful analysis projected yields a variety biomass materials, hence overcoming challenges empirical modeling. As such, five modern approaches available in machine learning are employed study develop prediction models. The Lasso regression, Tweedie random forest, XGBoost, Gradient boosting regression were employed. Out these XGBoost was superior training mean squared error (MSE) 1.17 test MSE 2.94. XGBoost-based model shows excellent performance strong accuracy R 2 values as 0.9739 (training) 0.8875 (test). absolute percentage value only 2.14% phase 3.8% phase. Precision prognostic technologies have broad effects on sectors including logistics, technologies, effective utilization renewable energy. Leveraging SHAP based cooperative game theory, that while ash moisture lower yield, FPT, nitrogen, carbon content significantly boost it. Small variables like heating rate volatile matter secondary impact production efficiency.

Language: Английский

---

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

JOIV International Journal on Informatics Visualization, Journal Year: 2024, Volume and Issue: 8(1), P. 55 - 55

Published: March 16, 2024

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, solar power, can revolutionize the industry. Biomass biofuels have benefited significantly from implementing AI ML algorithms that optimize feedstock, enhance resource management, facilitate biofuel production. By applying insight derived data analysis, stakeholders improve entire supply chain - biomass conversion, fuel synthesis, agricultural growth, harvesting to mitigate environmental impacts accelerate transition a low-carbon economy. Furthermore, in combustion systems engines has yielded substantial improvements efficiency, emissions reduction, overall performance. Enhancing engine design control techniques produces cleaner, more efficient minimal impact. This contributes sustainability of power generation transportation. are employed analyze vast quantities photovoltaic systems' design, operation, maintenance. The ultimate goal is increase output system efficiency. Collaboration among academia, industry, policymakers imperative expedite sustainable future harness potential energy. these technologies, it possible establish ecosystem, which would benefit generations.

Language: Английский

---

Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 60, P. 104743 - 104743

Published: June 24, 2024

In this study, eXtreme Gradient Boosting (XGBoost) and Light (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, mass product) dependent (energy consumption size reduction) established. For energy consumption, XGBoost demonstrates superior performance R2 0.9957 during training 0.9971 testing, alongside minimal MSE 0.0034 0.0008 testing phase indicating high predictive accuracy low error rates. Conversely, LGBM shows lower values (0.9061 training, 0.8809 testing) higher 0.0747 0.0337 reflecting poorer performance. Similarly, for shrinkage prediction, outperforms LGBM, evidenced by (0.9887 0.9975 (0.2527 0.4878 testing). comparative statistics showed that regularly outperformed LightGBM. game theory-based Shapley functions revealed temperature types most influential features model. These findings illustrate practical applicability LightGBM models in food operations towards optimizing conditions, improving quality, reducing consumption.

Language: Английский

---

Artificial Intelligence and Machine Learning in Tribology: Selected Case Studies and Overall Potential

Advanced Engineering Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Artificial intelligence (AI) and machine learning (ML) have been the subjects of increased interest in recent years due to their benefits across several fields. One sector that can benefit from these tools is tribology industry, with an emphasis on friction wear prediction. This industry hopes train utilize AI algorithms classify equipment life status forecast component failure, mainly using supervised unsupervised approaches. article examines some methods used accomplish this, such as condition monitoring for predictions material selection, lubrication performance, lubricant formulation. Furthermore, ML support determination tribological characteristics engineering systems, allowing a better fundamental understanding friction, wear, mechanisms. Moreover, study also finds continued use requires access findable, accessible, interoperable, reusable data ensure integrity prediction tools. The advances show considerable promise, providing more accurate extensible than traditional

Language: Английский

---

Design, characterization, and evaluation of biochar: recent advances, applications, and future research directions

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 59 - 73

Published: Jan. 1, 2025

Language: Английский

---

Machine learning–assisted prediction of engineered carbon systems’ capacity to treat textile dyeing wastewater via adsorption technology

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(2)

Published: Feb. 1, 2025

Language: Английский

---