Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards

Case Studies in Thermal Engineering, Journal Year: 2023, Volume and Issue: 51, P. 103554 - 103554

Published: Sept. 30, 2023

In this work, experiments were carried out in line with Design of Experiments (DOE) standards to assess the performance and emission features 5% graphene nanoparticles added linseed biodiesel. The engine was operated blends B10, B20, B30 nano additives (designated as B10G5, B20G5, B30G5). To find parameter's optimum values, Desirability Function approach (DFA), Swarm Salp single objective, Multi Objective Bat algorithm (MOBA), Response surface methodology (RSM) D-optimal design employed. Advanced machine learning (ML) techniques employed anticipate these characteristics. It found that B20G5 had a better brake thermal efficiency (BTE), when compared other samples (and around 11% higher than diesel fuel at full load). emissions Carbon monoxide (CO) Hydrocarbon (HC) lower for blended (Around 23.52% diesel). comparison (RSM), overall coefficient determination (R2) value using Artificial Neural Network (ANN) high. As result, it revealed ANN typically RSM forecasting various factors affecting performance. outcomes achieved by objective (Salp algorithm) multi-objective algorithms. According algorithm, additive biodiesel mix its maximum Brake power (BP) produced highest BTE lowest Nitrogen Oxides (NOx) emissions. shows can be used easily without making any modifications engines.

Language: Английский

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Evaluation of machine learning algorithms on hydrogen boosted homogeneous charge compression ignition engine operation for performance and emission prediction

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Evaluation of microalgae biodiesel for carbon neutrality based on the waste treatment by the autotrophic and heterotrophic combination

Energy, Journal Year: 2024, Volume and Issue: 291, P. 130314 - 130314

Published: Jan. 11, 2024

Language: Английский

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Impact of injection pressure on a dual-fuel engine using acetylene gas and microalgae blends of chlorella protothecoides

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 60, P. 104653 - 104653

Published: June 7, 2024

The amount of fossil fuel usage in compression ignition (CI) engines is greatly reduced when biodiesel used. primary disadvantage using that, due to its high viscosity, which causes fuels remain unburned during the premixed combustion stage, leads lower brake thermal efficiency (BTE). Gaseous predominantly reducing emissions CI complete burning without leaving any carbon traces. Fuel injection pressure (FIP) one factors influencing phase because they are used optimize particle atomization. current study examines engine parameters for a dual that runs on blends made from 20 % methyl ester chlorella protothecoides micro algae (B20MEOA) and acetylene gas under variable ranging 200 bar 240 with 10-bar steps. According experimental results, 3 LPM supplied along intake air B20MEOA at FIP bar, such as smoke opacity, hydrocarbon (HC), monoxide (CO) by 16.9 %, 8.3 15.4 respectively, whereas oxides nitrogen (NOx) increases approximately 7 compared alone operation.

Language: Английский

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A glass-box approach for predictive modeling based on experimental data for a waste biomass derived producer gas-powered dual-fuel engine

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 58, P. 1122 - 1137

Published: Jan. 31, 2024

Language: Английский

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Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil

Eksploatacja i Niezawodnosc - Maintenance and Reliability, Journal Year: 2023, Volume and Issue: 25(4)

Published: Nov. 2, 2023

The reliability of internal combustion engines becomes an important aspect when traditional fuels with biofuels. Therefore, the development prognostic models very for evaluating and predicting replacement biofuels in engines. have been made to model AVL 5402 engine emission, vibration, sound pressure parameters using a three-stage statistical regression models. fifteen might be accurately predicted by single statistic presented here. Both fuel type (diesel HVO) that can adjusted were considered, since this analysis followed symmetry methods. data process included three distinct steps symmetric testing was performed. algorithm examined effectiveness various settings. Finally, optimal fixed parameter used construct ANCOVA model. improved accuracy prediction all missing parameters.

Language: Английский

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Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(22), P. 32449 - 32463

Published: April 23, 2024

This article presents the outcomes of a research study focused on optimizing performance soybean biofuel blends derived from seeds specifically for urban medium-duty commercial vehicles. The took into consideration elements such as production capacity, economics and assumed engine characteristics. For purpose predicting performance, combustion emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, brake thermal efficiency (BTE) dropped speed increased diesel fuel mixes, but brake-specific consumption (BSFC) increased. BSFC by 11.9% when compared to with blends. mixes cut both maximum cylinder pressure

Language: Английский

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Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 187, P. 1076 - 1096

Published: May 10, 2024

Language: Английский

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Significant Research on Sustainable Oxygenated Fuel for Compression Ignition Engines with Controlled Emissions and Optimum Performance Prediction Using Artificial Neural Network

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 788 - 788

Published: Jan. 20, 2025

The present work compares the performance and emissions of a compression ignition (CI) engine using dual-mode LPG at varying flow rates an oxygenated biodiesel mix (B20). experimental investigation is carried out on (0.1, 0.3, 0.5 kg/h) replacing diesel with B20, affecting under various load circumstances while maintaining speed. study demonstrates potential artificial neural network (ANN) in accurately forecasting emission characteristics across different operating conditions. ANN model’s high accuracy correlating results predicted outcomes underscores its as dependable instrument for optimizing fuel parameters. show that B20 balance emissions, making CI functionality sustainable. A blend containing diethyl ether (B20 + 2%DEE) exhibits slightly reduced brake thermal efficiency (BTE) lower power (BP); however, it advantages higher BP, contributing to improved quality. analysis indicates average NOx 2%DEE 0.1 kg/h, 0.3 kg/h are 29.33%, 28.89%, 48.05%, 37.48%, respectively. Consequently, selecting appropriate regulating rate critical enhancing dual-fuel engine.

Language: Английский

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Artificial intelligence applications in solar energy

JOIV International Journal on Informatics Visualization, Journal Year: 2024, Volume and Issue: 8(2), P. 826 - 826

Published: May 31, 2024

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and pressing need reduce greenhouse gas emissions. Solar stands out among these sources due its abundance global accessibility. However, weather-dependent cyclical nature add inherent risks, making effective planning management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning optimization techniques are gaining popularity solar industry. The current literature highlights growing use soft technologies, emphasizing their potential address difficult challenges systems. To effectively reap benefits, strategies must be seamlessly connected with emerging like Internet Things (IoT), big data analytics, cloud computing. This integration provides a unique opportunity improve scalability, flexibility, efficiency Researchers can synergies create intelligent, linked ecosystems capable real-time production, delivery, consumption. These have transform renewable environment, allowing more resilient sustainable infrastructures. Furthermore, as improve, there is demand trained experts associated cybersecurity problems, assuring integrity security sophisticated may pave road energy-efficient future by working collaboratively using interdisciplinary methodologies.

Language: Английский

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