Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion

Bioresource Technology, Год журнала: 2023, Номер 374, С. 128746 - 128746

Опубликована: Фев. 20, 2023

Язык: Английский

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Machine learning-based optimization of catalytic hydrodeoxygenation of biomass pyrolysis oil

Journal of Cleaner Production, Год журнала: 2024, Номер 437, С. 140738 - 140738

Опубликована: Янв. 1, 2024

Язык: Английский

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Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 9, 2025

The rapid advancement of computational intelligence (CI) techniques has enabled the development highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, Deep Learning-based models. These aim to address challenges by improving convergence rates, solution accuracy, efficiency. In context a framework was successfully used predict optimal treatment plans cancer patients, achieving 92% accuracy rate in classification tasks. proposed demonstrate potential addressing broad spectrum problems, from resource allocation smart grids dynamic scheduling manufacturing integration cutting-edge CI methods offers promising future optimizing performance real-world wide range industries.

Язык: Английский

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Two-Stage Anaerobic Digestion for Green Energy Production: A Review

Processes, Год журнала: 2025, Номер 13(2), С. 294 - 294

Опубликована: Янв. 21, 2025

Anaerobic digestion (AD) is a biotechnological process in which the microorganisms degrade complex organic matter to simpler components under anaerobic conditions produce biogas and fertilizer. This has many environmental benefits, such as green energy production, waste treatment, protection, greenhouse gas emissions reduction. It long been known that two main species (acidogenic bacteria methanogenic archaea) community of AD differ aspects, optimal for their growth development are different. Therefore, if performed single bioreactor (single-phase process), selected taking into account slow-growing methanogens at expense fast-growing acidogens, affecting efficiency whole process. led two-stage (TSAD) recent years, where processes divided cascade separate bioreactors (BRs). division consecutive BRs leads significantly higher yields two-phase system (H2 + CH4) compared traditional single-stage CH4 production review presents state art, advantages disadvantages, some perspectives (based on more than 210 references from 2002 2024 our own studies), including all aspects TSAD—different parameters’ influences, types bioreactors, microbiology, mathematical modeling, automatic control, energetical considerations TSAD processes.

Язык: Английский

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ALPOA: Adaptive Learning Path Optimization Algorithm for Personalized E-Learning Experiences

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 5, 2025

In this study, we propose the Adaptive Learning Path Optimization Algorithm (ALPOA) to enhance personalized e-learning experiences by tailoring content delivery based on individual learner profiles. ALPOA employs a hybrid optimization framework combining Genetic (GA) and Particle Swarm (PSO) dynamically adjust learning paths. The algorithm considers multiple factors such as proficiency, speed, engagement level, difficulty. Experimental results demonstrate that outperforms traditional static models, achieving 25% improvement in efficiency, 30% increase engagement, 20% reduction redundancy. model was tested dataset of 1,500 learners, showing 97% accuracy predicting optimal paths 15% higher knowledge retention rate compared benchmark algorithms. ALPOA’s scalability adaptability make it promising solution for education systems, fostering improved outcomes satisfaction. Future work will focus integrating real-time feedback mechanisms expanding support diverse environments.

Язык: Английский

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Artificial intelligence and machine learning for smart bioprocesses

Bioresource Technology, Год журнала: 2023, Номер 375, С. 128826 - 128826

Опубликована: Март 5, 2023

Язык: Английский

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Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Bioresource Technology, Год журнала: 2023, Номер 385, С. 129444 - 129444

Опубликована: Июль 1, 2023

Язык: Английский

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Machine learning approach for determining and optimizing influential factors of biogas production from lignocellulosic biomass

Bioresource Technology, Год журнала: 2023, Номер 383, С. 129235 - 129235

Опубликована: Май 26, 2023

Язык: Английский

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Auto-tuning data-driven model for biogas yield prediction from anaerobic digestion of sewage sludge at the south-tehran wastewater treatment plant: Feature selection and hyperparameter population-based optimization

Renewable Energy, Год журнала: 2024, Номер 227, С. 120554 - 120554

Опубликована: Апрель 23, 2024

Язык: Английский

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

The Science of The Total Environment, Год журнала: 2024, Номер 916, С. 170232 - 170232

Опубликована: Янв. 24, 2024

Язык: Английский

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