Determination of base number in lubricants and additives by ASTM D2896: Substitution of chlorinated solvents and alternate electrolyte study

Dutta, Moumita; Pathiparampil, Annie; Murella, Joshua; Villacarte, Dominique; Hamada, Duane; Hernández, J.; López-Linares, Francisco

doi:10.1016/j.fuel.2021.120701

Fuel

2021

DOI: 10.1016/j.fuel.2021.120701

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Determination of base number in lubricants and additives by ASTM D2896: Substitution of chlorinated solvents and alternate electrolyte study

Moumita Dutta

Annie Pathiparampil

Joshua Murella

et al.

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2023

2024

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Cited by 2 publications

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“…According to the harmonized classification and labeling (ATP09) approved by the European Union, this substance is toxic to aquatic life, with long-lasting effects [91]; therefore, it would be recommended to replace this solvent. Efforts to identify and replace chlorobenzene with other environmentally friendlier solvents are ongoing in different sectors [92], including semiconductor processing [93]. Larsen et al [94] have developed a tool for the identification of green solvents for printed electronics.…”

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Applying the 12 Principles of Green Engineering in Low TRL Electronics: A Case Study of an Energy-Harvesting Platform

Doyle,

Cavero,

Modreanu

2023

Sustainability

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Energy harvesting has been identified as a key enabling technology for the Internet of Things as it allows a battery-less functioning of electronic devices. While the use of ambient sources of energy is commonly seen as sustainable due to their renewable nature, raw material consumption and recyclability need to be assessed to ensure true sustainability. This is especially relevant in electronics, due to their high complexity stemming from the variety of components and materials in their composition. This work presents the case study of the application of the 12 Principles of Green Engineering to an energy-harvesting platform in the early technology development phase. Specifically, the technological areas of design for disassembly, materials for substitution, fabrication efficiency, and manufacturing processes that enable the use of recycled materials have been evaluated. This has allowed us to identify hazardous raw materials and recommend their substitution. Further recommendations include the adoption of mechanical fixtures to fasten lump components. Additional strategies have been identified but their application has been found out of reach of the technology developers, such as the increase in the manufacturing batch size or the inclusion of solvent recycling, which can only be implemented at larger manufacturing scales. Further strategies, such as the use of recycled Si wafers or dry adhesives as fixtures, represent future solutions for the reduction in the environmental impact which require further R&D efforts from different disciplines. This highlights the need for holistic and multidisciplinary research efforts to fully achieve the circular design.

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confidence: 99%

Applying the 12 Principles of Green Engineering in Low TRL Electronics: A Case Study of an Energy-Harvesting Platform

Doyle,

Cavero,

Modreanu

2023

Sustainability

View full text Add to dashboard Cite

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Machine Learning-Based Predictions of Metal and Non-Metal Elements in Engine Oil Using Electrical Properties

Pourramezan,

Rohani,

Abbaspour-Fard

2024

Lubricants

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This study investigates the influence of six metallic and non-metallic elements (Fe, Cr, Pb, Cu, Al, Si) on the quality of engine oil under normal, cautious, and critical conditions. To achieve this, the research employs the Design of Experiments (DoE) approach, specifically the Box–Behnken Design (BBD) method, for designing experiments. The electrical properties of 70 engine oil samples prepared under varying conditions were analyzed. Machine learning models, including RBF, ANFIS, MLP, GPR, and SVM, were utilized to predict the concentrations of the six pollutants in the lubricant oil samples based on their electrical characteristics. The models’ performance was assessed using RMSE and R2 indicators during train, test, and All stages. The results revealed that the Radial Basis Function (RBF) model exhibited the best overall performance (RMSE = 0.01, R2 = 0.99). The study proceeds with optimizing RBF model parameters, such as hidden size (best = 17), spread (best = 0.4 or higher), and training algorithm (best = trainlm), to estimate each pollutant individually. The generalizability of the model was assessed by reducing the training data percentage and increasing the testing data percentage. The results demonstrated the model’s proper performance for all pollutants in various training sizes (RMSE = 0.01, R2 = 0.99). However, as the training data ratio reduced to 60:40 and 50:50, the model’s performance in estimating Cu deteriorated, resulting in increased RMSE values (10.76 or 11.85) and decreased R2 values (0.89 or 0.87) across the All step. This academic research hopes to contribute to the field of applied studies, considering the inherent complexities of lubricants and the challenges in measuring small-scale electrical properties.

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Determination of base number in lubricants and additives by ASTM D2896: Substitution of chlorinated solvents and alternate electrolyte study

Cited by 2 publications

References 15 publications

Applying the 12 Principles of Green Engineering in Low TRL Electronics: A Case Study of an Energy-Harvesting Platform

Applying the 12 Principles of Green Engineering in Low TRL Electronics: A Case Study of an Energy-Harvesting Platform

Machine Learning-Based Predictions of Metal and Non-Metal Elements in Engine Oil Using Electrical Properties

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