Concept of the exhaust system for diesel engines used in underground mining

Lesiak, Krzysztof; Brzeżański, Marek

doi:10.1088/1757-899x/421/4/042045

Cited by 3 publications

(2 citation statements)

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“…14,15 They are used extensively in mining and dock operations and as powerplants in remote areas, where other electricity sources are not available. 16,17…”

Section: Introductionmentioning

confidence: 99%

“…14,15 They are used extensively in mining and dock operations and as powerplants in remote areas, where other electricity sources are not available. 16,17 However, CI engines give rise to pollution because they emit PM, NO x , and several other gaseous pollutants of concern. 18 Diesel PM emissions are comprised of soot, PAHs, and heavy metal traces.…”

Section: Introductionmentioning

confidence: 99%

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Review of morphological and chemical characteristics of particulates from compression ignition engines

Agarwal

Krishnamoorthi

2022

International Journal of Engine Research

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Particulates from compression ignition (CI) engines have received serious attention in the last two decades. CI engines emit higher particulate matter (PM) and nitrogen oxides (NOx) than spark ignition (SI) engines. Both these species are harmful to human health and the environment. Compared to NOx emissions, PM constitutes many more chemical species in solid and liquid phases. This review paper focuses on soot morphology and chemical characterization of PM emissions from CI engines. Effects of different fuels, lubricating oil, and engine operating conditions on particulate characteristics are analyzed exhaustively. The first part of this paper focuses on the effects of particulates on living organisms, the consequences of exposure to diesel particulates, and the composition of diesel particulates. In recent decades, micro and nano-scale characteristics of PM have been exhaustively investigated to understand its structure, formation, and chemical functionalities. Typically, particulates comprise of elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), the soluble organic fraction (SOF), and trace metals. This paper summarizes most aspects of diesel particulate emissions for the benefit of active researchers in the field and underlines the importance of particulate emission reduction from the CI engines. Diesel combustion generates particles with enormous long-chain aggregates of smaller sizes and immature soot particles. Low-temperature combustion (LTC) modes and oxygenated fuels reduce the soot emissions and generate compact/clustered aggregates. Oxygenated fuels in CI engines produce more nucleation mode particles (NMPs) and high-reactivity soot aggregates. Higher trace metal concentrations were observed in diesel origin particulates than biofuel origin particulates. Biodiesel origin particulates possess higher mutagenicity and carcinogenicity because of nitro-PAHs. Transient engine operations cause higher particulates than steady-state engine operations.

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“…14,15 They are used extensively in mining and dock operations and as powerplants in remote areas, where other electricity sources are not available. 16,17…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of morphological and chemical characteristics of particulates from compression ignition engines

Agarwal

Krishnamoorthi

2022

International Journal of Engine Research

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Experimental evaluation of a thermosyphon-based heat exchanger working with a graphene oxide (GO) nanofluid in a cogeneration system

Herrera

Gallego

Cacua

2021

Thermal Science and Engineering Progress

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Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

Pourramezan,

Rohani

2024

SAE Int. J. Fuels Lubr.

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<div>This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies.</div> <section> <h2>Highlights</h2> <div> <ul> <li> <div>Two machine learning models, multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), were employed to predict and classify the performance condition of diesel engines.</div> </li> <li> <div>Among various training algorithms, Levenberg–Marquardt and the Bayesian regularization demonstrated superior classification accuracy, achieving a 95%–96% range.</div> </li> <li> <div>To assess the generalizability of MLP-NN and ANFIS, the training set size was varied from 90% to 10%.</div> </li> <li> <div>The ANFIS model exhibited greater stability than MLP-NN, with a 50% higher performance.</div> </li> </ul> </div> </section> <section> <h2>Graphical Abstract</h2> <div> <img/> </div> </section>

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Concept of the exhaust system for diesel engines used in underground mining

Cited by 3 publications

References 0 publications

Review of morphological and chemical characteristics of particulates from compression ignition engines

Review of morphological and chemical characteristics of particulates from compression ignition engines

Experimental evaluation of a thermosyphon-based heat exchanger working with a graphene oxide (GO) nanofluid in a cogeneration system

Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

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