Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 23, P. 100669 - 100669

Published: July 1, 2024

One of the main limitations to economic sustainability biodiesel production remains high feedstock cost. Modeling and optimization are crucial steps determine if processes (esterification transesterification) involved in economically viable. Phenomenological or mechanistic models can simulate processes. These methods have been used manage processes, but their broad use has constrained by computational complexity numerical difficulties. Therefore, it is necessary quick, effective, accurate, resilient modeling methodologies regulate such complex systems. Data-driven machine-learning (ML) techniques offer a potential replacement for conventional deal with nonlinear, unpredictable, complex, multivariate nature Artificial neural networks (ANN) adaptive neuro-fuzzy inference systems (ANFIS) most often utilized ML tools research. To effectively attain maximum yield, suitable based on nature-inspired algorithms need be integrated these obtain best possible combination various operating variables. Future research should focus utilizing approaches monitoring managing increase effectiveness promote commercial feasibility. Thus, review discusses optimizing

Language: Английский

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Advances in Sand Cat Swarm Optimization: A Comprehensive Study

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

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Time optimal trajectory planning of robotic arm based on improved sand cat swarm optimization algorithm

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(5)

Published: Jan. 15, 2025

Language: Английский

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GOHBA: Improved Honey Badger Algorithm for Global Optimization

Biomimetics, Journal Year: 2025, Volume and Issue: 10(2), P. 92 - 92

Published: Feb. 6, 2025

Aiming at the problem that honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a optimization (Global Optimization HBA) (GOHBA), which improves ability of population, with better to jump out optimum, faster stability. The introduction Tent chaotic mapping initialization enhances population diversity initializes quality HBA. Replacing density factor range in entire solution space avoids premature optimum. addition golden sine strategy capability HBA accelerates speed. Compared seven algorithms, GOHBA achieves optimal mean value on 14 23 tested functions. On two real-world engineering design problems, was optimal. three path planning had higher accuracy convergence. above experimental results show performance is indeed excellent.

Language: Английский

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Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm

Energies, Journal Year: 2024, Volume and Issue: 17(6), P. 1479 - 1479

Published: March 20, 2024

To accurately predict reservoir porosity, a method based on bi-directional long short-term memory with attention mechanism (BiLSTM-AM) optimized by the improved pelican optimization algorithm (IPOA) is proposed. Firstly, nonlinear inertia weight factor, Cauchy mutation, and sparrow warning are introduced to improve (POA). Secondly, superiority of IPOA verified using CEC–2022 benchmark test functions. In addition, Wilcoxon applied evaluate experimental results, which proves against other popular algorithms. Finally, BiLSTM-AM IPOA, IPOA-BiLSTM-AM used for porosity prediction in Midlands basin. The results show that has smallest error verification set samples (RMSE MAE were 0.5736 0.4313, respectively), verifies its excellent performance.

Language: Английский

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CMGWO: Grey wolf optimizer for fusion cell-like P systems

Heliyon, Journal Year: 2024, Volume and Issue: 10(14), P. e34496 - e34496

Published: July 1, 2024

The grey wolf optimizer is a widely used parametric optimization algorithm. It affected by the structure and rank of wolves prone to falling into local optimum. In this study, we propose for fusion cell-like P systems. Cell-like systems can parallelize computation communicate from cell membrane membrane, which help jump out Design new convergence factors use different in other membranes balance overall exploration utilization capabilities At same time, dynamic weights are introduced accelerate speed Experiments performed on 24 test functions verify their global performance. Meanwhile, support vector machine model optimized has been developed tested six benchmark datasets. Finally, optimizing ability constrained problems verified three real engineering design problems. Compared with algorithms, obtains higher accuracy faster function, at it find better parameter set stably parameters, addition being more competitive results show that improves searching population, optimum, speed, stability.

Language: Английский

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Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 492 - 492

Published: Oct. 18, 2023

The sand cat is a creature suitable for living in the desert. Sand swarm optimization (SCSO) biomimetic intelligence algorithm, which inspired by lifestyle of cat. Although SCSO has achieved good results, it still drawbacks, such as being prone to falling into local optima, low search efficiency, and limited accuracy due limitations some innate biological conditions. To address corresponding shortcomings, this paper proposes three improved strategies: novel opposition-based learning strategy, exploration mechanism, elimination update mechanism. Based on original SCSO, multi-strategy (MSCSO) proposed. verify effectiveness proposed MSCSO algorithm applied two types problems: global feature selection. includes twenty non-fixed dimensional functions (Dim = 30, 100, 500) ten fixed functions, while selection comprises 24 datasets. By analyzing comparing mathematical statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, show that ability can adapt wide range problems.

Language: Английский

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An intelligent decision support system for warranty claims forecasting: Merits of social media and quality function deployment

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 201, P. 123268 - 123268

Published: Feb. 15, 2024

Language: Английский

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Enhancing Myocarditis Diagnosis Through Deep Learning and Data Augmentation: A Novel Framework Addressing Imbalance and Initialization Sensitivity

Web Intelligence, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Myocarditis poses a serious public health risk, with the potential to cause heart failure and sudden death. Traditionally, diagnosing myocarditis relies on non-invasive imaging, particularly cardiac magnetic resonance imaging (MRI), though MRI results can be vulnerable operator bias. Our research addresses this by introducing an innovative deep-learning framework tackle challenges frequently overlooked in past studies, including class imbalance, sensitivity initial weight settings, generalizability. model leverages convolutional neural networks (CNNs) extract detailed feature vectors for highly precise classifying of myocarditis. Since imbalance problem is frequent many training datasets, we will adopt reinforcement learning (RL) strategy shift more emphasis underrepresented classes balanced learning. Additionally, our involves mutual learning-based artificial bee colony (ML-ABC) algorithm efficient pretraining weights. Improve data diversity volume further using online augmentation improved version generative adversarial network (GAN). We enhance performance generator considering information provided features produced discriminator which base its output making it realistic, hence increasing accuracy generator. model, when applied Z-Alizadeh Sani dataset, reaches 90.8%, outperforming previously reported techniques reiterating feasibility clinical purposes. These significantly advance early detection open new avenues enhanced treatment strategies.

Language: Английский

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A proximal policy optimisation algorithm-based algorithm for cardiovascular disorders detection

Journal of Medical Engineering & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: March 11, 2025

Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises mutual learning-based bee colony (ML-ABC) algorithm set initial weights proximal policy optimisation (PPO) address imbalanced classification. ML-ABC uses learning enhance process by updating positions of food sources with respect best fitness outcomes two randomly selected individuals. PPO makes updates ANN stable efficient improve model's reliability. Our approach formulates classification problem as series decision-making processes, rewarding every act higher rewards for correctly identifying instances minority class, hence handling class imbalance. We evaluated performance on diversified medical dataset including 26,002 who were examined within Polyclinic Occupational Health Sports Zagreb, further validated NCAA NHANES datasets verify generalisability. findings indicate that our outperforms existing models accuracies 0.88, 0.86 0.82 respective datasets. These results clinical application advance cardiovascular disorder detection methodologies.

Language: Английский

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