Lubricants are influenced over their lifetimes by various factors such as temperature, oxygen, water contamination, etc. which affect the chemical structure and implicitly their specific characteristics. If the degradation of the oils is significant and the characteristic values exceed certain limits that are defined for the safe operation, the lubricants should be replaced. The main purpose of the present study is to offer an efficient and predictive method for an adequate quality control of lubricant oils in service and implicitly for their adequate and safe operation. The method developed in this study consists in a numerical algorithm (the multiple regression) and is based on the monitoring of lubricant oils� representative characteristics in time and allows estimating the evolution of the oils characteristics during the service period as well as the prediction of the life-time of the oils.

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

In the oil industry, crude oil emulsions appear very frequently in almost all activities, starting with drilling and continuing with completion, production, transportation and processing. They are usually formed naturally or during oil production and their presence can have a strong impact on oil production and facilities. In this paper we addressed the problem of oil emulsions present in a reservoir with unfavorable flow properties. It is known that the presence of emulsions in a reservoir can influence both flow capacity and the quality of its crude oil, especially when they are associated with porous medium�s low values of permeability. Considering this, we have introduced a new procedure for selecting a special fluid of fracture. This fluid has two main roles: to create new flow paths from the reservoir rock to wells; to produce emulsion breaking of emulsified oil from pore of rocks. Best fracturing fluid performance was determined by laboratory tests. Selected fluid was then used to stimulate an oil well located on an oil field from Romania. In the final section of this paper,we are presenting a short analysis of the efficiency of the operation of hydraulic fracturing stimulation probe associated with the crude oil emulsion breaking process.

The present study aimed to estimate the physical properties of the diesel-diesel-biodiesel ternary blends, using artificial neural networks, also known as ANNs. The input data used to estimate the properties was the percentage in which each component was used to obtain the blend. Using two hydrofined diesel fuels from a local refinery and three biodiesel samples synthesized in the university laboratory, a total of 114 blends, both binary and ternary, were obtained. The ANN training database was comprised of exclusively 96 binary blends, from the total of 114. The predictions were made on the remaining 18 ternary blends. All the predictions were within the error mentioned in the standard, concluding the fact that the created ANNs had a rate of 100% accuracy.

The paper presents the experimental research results which the Romanian crude oils type B were subjected to. Tests conducted in laboratory environment aimed determination of crude oils rheological parameters and also identification of rheological behaviour models. Experiments, conducted at different temperatures, led to rheological behaviour models characteristic of non-Newtonian fluids. The established rheological relations help to better understanding of tested fluids so we can know either their behaviour or how we could impose their behaviour according to our needs.

The first part of this study analyzed how the blending type of the components used in biodiesel synthesis through transesterification influences yield and the physical properties of the biodiesel. The second part of this study emphasized the way the added proportions of the synthesized biodiesel samples influenced the final diesel-biodiesel blend. This study concluded that the ultrasonic mixing method could replace classical mechanical mixing by being similar both in terms of yield and physical properties.

The purpose of this paper is to compare two different optimization methods, used in acquiring diesel-biodiesel blends. There were used five types of samples in order to enable the optimization of the final blend: there were chosen two types of hydrofined diesel fuel and there were synthesized three original types of biodiesel. The first optimization method used, dual simplex, is a classical method being used in solving linear programming problems. The second optimization method, the genetic algorithms, falls in the type of artificial intelligence algorithms, being an evolutionary method used when the problem requires searching an optimal solution in a great variety of valid solutions.