Prediction of IC engine performance and emission parameters using machine learning: A review

Karunamurthy, K; Janvekar, Ayub Ahmed; Palaniappan, P. L.; Adhitya, V.; Lokeswar, T. T. K.; Harish, J.

doi:10.1007/s10973-022-11896-2

Cited by 17 publications

(8 citation statements)

References 118 publications

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“…Machine learning (ML) is a subfield of artificial intelligence that uses algorithms and statistical models to allow computer systems to improve their performance on a given job by learning from data, without being explicitly programmed [74]. The objective of machine learning is to create algorithms that can recognise patterns in data and utilise those patterns to make predictions or conduct actions [75], [76]. ANNs are a type of machine learning algorithm inspired by the anatomy and functioning of the human brain.…”

Section: Ann Structurementioning

confidence: 99%

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Nguyen

Sakthivel

Rudzki

et al. 2023

Polish Maritime Research

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In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

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Section: Ann Structurementioning

confidence: 99%

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Nguyen

Sakthivel

Rudzki

et al. 2023

Polish Maritime Research

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“…Deep learning has emerged in recent years as an influential technique for solving many engineering problems, particularly those involving image and speech recognition. 101,102 AI has been successfully employed in areas such as banking, entertainment, healthcare, transportation, finance, and education and can be used to improve traffic flow and security in transportation. In the field of education, AI can be used to personalize learning and offer students personalized feedback and help.…”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

“…One of the most difficult challenges in AI research is the development of algorithms and systems that are capable of performing complex tasks with high accuracy and reliability, which frequently necessitates the use of large data sets and sophisticated algorithms that are capable of learning from these data and making accurate predictions. Deep learning has emerged in recent years as an influential technique for solving many engineering problems, particularly those involving image and speech recognition. , …”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.

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“…According to Zhang et al [24], the utilization of support vector regression algorithm in numerical simulations has proven to be highly effective in forecasting engine performance and emissions. Karunamurthy et al [25] conducted a comprehensive review of various machine learning methodologies and algorithms, including artificial neural networks, random forest, semi supervised fuzzy and support vector machine, as utilized by multiple researchers. The discussion in this review delved into an in-depth analysis of performance and emission characteristics of ICEs.…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning for Predicting Internal Combustion Engine Performance and Segment-Based CO₂ Emissions Across Urban and Rural Settings

Niroomand,

Bach

2024

IEEE Access

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The assessment of artificial intelligence (AI) application for prediction of internal combustion engine (ICE) performance and its impact on CO2 emissions is conducted in this paper. Three machine learning techniques (Random Forest, Support Vector Regression, and Semi-supervised Deep Fuzzy C-means) are developed to analyze inputs from an engine simulation software package database. By employing these sophisticated mathematical techniques, we successfully assess the influence of engine power range on CO2 emissions in this paper. Moreover, the framework facilitates segment-based analysis, which enables segmentspecific assessment of CO2 emission based on metrics such as average traveled distance and average daily trips in urban and rural settings. The DFCM model seems promising to predict engine performance, with high predictive accuracy and a coefficient of determination (R 2 ) approaching unity. The results indicate that integration of inter-class and intra-class distinctions, along with considering the interquartile range of engine power provides invaluable insights for the formulation of strategies aimed at overhauling the passenger vehicle fleet and advancing decarbonization efforts. By implementing the proposed innovative techniques, we aspire to enrich the precision of ICE emission models, leading to more reliable calculations and an enhanced understanding of the environmental implications associated with vehicles.

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Prediction of IC engine performance and emission parameters using machine learning: A review

Cited by 17 publications

References 118 publications

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Integrating Machine Learning for Predicting Internal Combustion Engine Performance and Segment-Based CO₂ Emissions Across Urban and Rural Settings

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Prediction of IC engine performance and emission parameters using machine learning: A review

Cited by 17 publications

References 118 publications

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Integrating Machine Learning for Predicting Internal Combustion Engine Performance and Segment-Based CO2 Emissions Across Urban and Rural Settings

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Product

Resources

About

Integrating Machine Learning for Predicting Internal Combustion Engine Performance and Segment-Based CO₂ Emissions Across Urban and Rural Settings