Base oil distillation equipment Full-time Job

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.

Base Oil Categories

Lubricants can be categorized in many different ways. One of the most common classifications is by the constituent base oil: mineral, synthetic or vegetable. Mineral oil, which is derived from crude oil, can be produced to a range of qualities associated with the oil’s refining process. Synthetics are man-made through a synthesizing process and come in a number of formulations with unique properties for their intended purpose. Vegetable base oils, which are derived from plant oils, represent a very small percentage of lubricants and are used primarily for renewable and environmental interests.

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.

Some of the most important base oil properties include the viscosity limitations and viscosity index, pour point, volatility, oxidation and thermal stability, aniline point (a measure of the base oil’s solvency toward other materials including additives), and hydrolytic stability (the lubricant’s resistance to chemical decomposition in the presence of water).

The 20th century saw a number of improvements in the refining process used for mineral oils along with the introduction of a variety of synthetics. By the early 1990s, the American Petroleum Institute (API) had categorized all base oils into five groups, with the first three groups dedicated to mineral oils and the remaining two groups predominantly synthetic base oils.

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.

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 quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of 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%.