Synthesis and use of TMP ester biolubricant derived from cottonseed oil in SI engine

Khan, Muhammad Bilal; Zahid, Rehan; Kazim, Ali Hussain; Javed, Khalid

doi:10.1108/ilt-05-2021-0158

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…TMPTO (trimethylolpropane trioleate) was chosen as the base oil due to its significant rheological properties and long-chain fatty acid content [6,7,32,33]. Three additives were obtained from the local market to formulate the blend.…”

Section: Lubricantsmentioning

confidence: 99%

Prediction of Thrust Force and Torque for High-Speed Drilling of AL6061 with TMPTO-Based Bio-Lubricants Using Machine Learning

Kathmore,

Bachchhav,

Nandi

et al. 2023

Lubricants

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This study was designed to examine the effects of a trimethylolpropane trioleate (TMPTO)-based lubricant on thrust force and torque under the high-speed drilling of Al-6061 as an effective environmentally friendly cutting fluid. The tribological performance of three lubricant blends was evaluated based on ASTM standards. TMPTO base oil, notably enhances load-carrying capacity under extreme pressure conditions, with a seizer load of 7848 N. The best-performing oil was further optimized using a Taguchi-based design experiment to investigate the effect of different additive concentrations on thrust force and torque under actual contact conditions. Experiments were conducted using three critical machining parameters: additive concentration, spindle speed, and feed rate. The results of the ANOVA analysis reveal that spindle speed contributes most substantially (62.99%) to torque, with feed rate (23.72%) and additive concentration (7.74%) also showing significant impacts. On the other hand, thrust force is primarily influenced by feed rate (73.52%), followed by spindle speed (16.82%), and additive concentration (6.28%). Furthermore, a machine learning model was developed to predict and compare a few significant aspects of high-speed drilling machinability, including thrust force and torque. Three different error metrics were utilized in order to assess the performance of the predicted values, namely the coefficient of determination (R2), mean absolute percentage error (MAPE) and mean square error (MSE), which are all based on the coefficient of determination. Compared to other models, decision tree produces more accurate prediction values for cutting forces. The present study provides a novel approach for evaluating the most promising biodegradable lube oils and predicting cutting forces by formulating a perfect blend.

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Section: Lubricantsmentioning

confidence: 99%

Prediction of Thrust Force and Torque for High-Speed Drilling of AL6061 with TMPTO-Based Bio-Lubricants Using Machine Learning

Kathmore,

Bachchhav,

Nandi

et al. 2023

Lubricants

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“…The major drawback of vegetable oil-based lubricants is their lower thermo-oxidative stability. TMP esterification of vegetable oils tends to improve their thermo-oxidative stability (Owuna et al , 2020; Khan et al , 2021). Also, TMP esters of vegetable oil are found to perform well with respect to reducing friction and wear (Hussain et al , 2021b).…”

Section: Introductionmentioning

confidence: 99%

Tribological study of sunflower TMP ester and silica nanoparticles additives for hydrodynamic journal bearing application under boundary lubrication condition

Hussain

Gaval

Pratap

et al. 2022

ILT

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Purpose This study aims to study the tribological performance of sunflower TMP ester and silica nanoparticles additives as a biolubricant alternative to the conventional lubricants for hydrodynamic journal bearing applications. Design/methodology/approach Nanolubricants were synthesized using an ultrasonicator and a homogenizer. A pin-on-disk tribometer was used to simulate the boundary lubrication condition for hydrodynamic journal bearing application in the presence of the studied lubricants. Surface analysis of the pin (bearing material) was done using scanning electron microscopy and energy dispersive X-ray spectroscopy. Findings The sunflower TMP ester performed well in terms of the coefficient of friction compared to commercial lubricants, but its wear performance was poor. The silica nanoparticles improved the wear and friction performance of the sunflower TMP ester. With the addition of 1% silica nanoparticles to sunflower TMP ester, the reduction in the coefficient of friction was 27.92% and the reduction in specific wear rate was 54.79%, making it the best lubricant out of all studied lubricants. Originality/value Although there are various available studies on vegetable oil-based lubricants for hydrodynamic journal bearing applications, the studies on the use of vegetable oil-based TMP esters for hydrodynamic journal bearing applications are limited. Also, the effect of silica nanoparticles on the tribological performance of TMP esters under boundary lubrication condition has not been studied extensively in the available literature.

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A comparative physicochemical property assessment and techno-economic analysis of biolubricants produced using chemical modification and additive-based routes

Khan

Das

Quadir

et al. 2022

Science of The Total Environment

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Synthesis and use of TMP ester biolubricant derived from cottonseed oil in SI engine

Cited by 3 publications

References 41 publications

Prediction of Thrust Force and Torque for High-Speed Drilling of AL6061 with TMPTO-Based Bio-Lubricants Using Machine Learning

Prediction of Thrust Force and Torque for High-Speed Drilling of AL6061 with TMPTO-Based Bio-Lubricants Using Machine Learning

Tribological study of sunflower TMP ester and silica nanoparticles additives for hydrodynamic journal bearing application under boundary lubrication condition

A comparative physicochemical property assessment and techno-economic analysis of biolubricants produced using chemical modification and additive-based routes

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