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+--- Thread: Understanding the Differences Between Base Oil Formulations (/showthread.php?tid=1317)
Understanding the Differences Between Base Oil Formulations - koipdd62 - 02-25-2022
All lubricants contain a base oil. It serves as the foundation of the lubricant before it is blended with additives or a thickener in the case of a grease. But how do you know which base oil is best? Trying to choose between mineral oils and synthetics can be confusing. This article will break down the complexity between base oil distillation equipment formulations so you can make the right decision for each application.
All base oils have characteristics that determine how they will hold up against a variety of lubrication challenges. For a mineral oil, the goal of the refining process is to optimize the resulting properties to produce a superior lubricant. For synthetically generated oils, the objective of the various formulations is to create a lubricant with properties that may not be achievable in a mineral oil. Whether mineral-based or synthetic-based, each waste engine oil to base oil machine is designed to have a specific application.
When comparing properties among the waste motor oil to base oil machine groups, you typically will see greater benefits with those that are more highly refined, including those with enhanced oxidation stability, thermal stability, viscosity index, pour point and higher operating temperatures. Of course, as the oil becomes more refined, some key weaknesses also occur, which can affect additive solubility and biodegradability.
While PAOs are ideal for applications like engine oils, gear oils, bearing oils and other applications, mineral oil remains the predominant oil of choice due to its lower cost and reasonable service capabilities. With more than 90 percent usage in the industrial and automotive markets, mineral oil has solidified its place as the most common diesel distillation equipment in the majority of applications.
Paraffinic mineral oil, which is represented in Groups I, II and III, can offer a higher viscosity index and a higher flash point in comparison to naphthenic mineral oils, which have lower pour points and better additive solvency. Even though naphthenic oil is mineral-based, it is considered a Group V oil because it does not satisfy the API’s qualifications for Group I, II and III. The unique characteristics of naphthenic mineral oils have often made them good lubricants for locomotive engine oils, refrigerant oils, compressor oils, transformer oils and process oils. Nevertheless, paraffinic oils continue to be the preferred option for high-temperature applications and when longer lubricant life is required.
Ester-based synthetics, such as diesters and polyolesters, have advantages when it comes to biodegradability and miscibility with other oils. In fact, it is common for diesters and polyolesters to be mixed with PAOs during additive blending to help accept more significant additive packages. Diesters and polyolesters are often deployed as the waste oil filtration equipment for compressor fluids, high-temperature grease applications and even bearing or gear oils. Because they are known to perform well at higher temperatures, polyolesters have also been widely used for jet engine oils.
the turnbine oil filtration machine product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%.