Acid Number Prediction Model of Lubricating Oil Based on Mid-Infrared Spectroscopy

Abstract

The monitoring and replacement of lubricating oil has an important impact on mechanical equipment. In this study, based on the infrared spectroscopy monitoring method, an acid value index prediction model is established. The support vector machine regression method is used to quantitatively analyze the acid number of the oil sample, which verifies the stability and predictive ability of the quantitative prediction model, and we provide a theoretical basis and practical examples for the online monitoring of oil indicators. In addition, a support vector machine regression model is established by observing the changing law of the spectral absorption peak and selecting the dominant wavelength, and it is compared against the prediction algorithm of the long- and short-term memory network. By comparing the deviation relationship between the predicted value and the real chemical value, the feasibility of the infrared spectroscopy prediction model is verified. The experimental results show that the correlation coefficient between the predicted value of the model and the actual measured value reaches 0.98. This proves that the prediction effect of the prediction model based on the infrared spectrum data and the support vector machine regression method is better than that of the long- and short-term memory network trend prediction model, and the predicted results are reliable.

Sustainable Biocatalytic Synthesis of a Second-Generation Biolubricant

“…In addition to its good lubricating properties, and according to the literature [6][7][8], this ester also exhibits high oxidative stability owing to its saturated chains and has good cold flow properties due to the presence of branches in both the acid and alcohol moieties.…”

“…In the past, vegetable oils, both natural and synthetic, consisting mainly of esters of linear unsaturated fatty acids were used as first-generation biolubricants [6]. However, they have good cold performance but low oxidative resistance due to unsaturation; therefore, to overcome the latter drawback, unsaturations are replaced by branching, which increases the oxidative stability of the oils and leads to second-generation biolubricants [6,7]. In addition, the introduction of appropriate branching into ester molecules contributes to extending their liquidity to very low temperatures, which improves their cold flow properties.…”

The Effect of Water Content on Engine Oil Monitoring Based on Physical and Chemical Indicators

“…An innovative mechanism has been implemented for wider feature selection based on a genetic algorithm. Zhou [ 19 ] developed an acid value index prediction model based on an infrared spectroscopy monitoring method. The support vector machine regression method has been applied to quantitatively analyze oil sample AN, which verified the stability and prediction ability of the developed quantitative prediction model.…”

Predictive Neural Network Modeling for Diesel Engine Part Wear Assessment via Analysis of Wear Element Concentration in Used Oil

“…At the core of LCM lies the retrieval and examination of vital physical and chemical attributes from lubricants, facilitating informed maintenance decisions. Among the pivotal techniques for diagnosing faults in mechanical equipment within the LCM realm, oil spectrum analysis is highly important [5]- [8]. This approach adeptly detects abrasive elements in oil, assesses additive conditions, and gauges oil pollution levels; this approach is now recognized as one of the most efficacious methods for LCM.…”

“…Zzeyani et al [7] investigated the degradation of synthetic lubricating oil in diesel vehicles using electronic paramagnetic resonance (EPR) and FTIR, emphasizing the efficacy of FTIR in assessing oil quality and degradation rate. Zhou et al [8] developed a model for predicting acid values based on the infrared spectrum monitoring method. The American Society for Materials and Testing (ASTM) has adopted rotating disc electrode atomic emission spectrometry (RED-AES) as the standard test method for determining worn metals and contaminants in lubricants, as outlined in ASTM D6595-17 [9].…”