Study On Molecular Design, Synthesis And Application Of EVA-based Polymers

Pour point depressant (PPD) additives are very useful to produce diesel fuels with excellent low-temperature flow properties. Components of the paraffin distillate can be partly extended to produce diesel oil by using the PPD additives, which has been practically applied in industrial processes by taking advantages of improving the flexibility and the profits of the product, as well as maintaining the high values of cetane components. Firstly, the synthesis of ethylene-vinyl acetate-based (EVA) PPD were investigated and the synthetic principles for the copolymers were obtained. According to the characterization of the relevant functions, it was suggested that the copolymer of ethylene, vinyl acetate and propylene (EVAP) had the optimal efficiency to decrease the cold filter plug point(CFPP) of the diesel oil. Then the functional mechanism of this kind of PPD was intensively studied by using the inverted microscope, the differential scanning calorimeter, the X-ray diffraction, etc. The mechanism was determined as the function of co-crystallization and adsorption, which provided the basis for the exploring of new PPD through the method of molecular design and optimization. Further it was investigated that the mutual molecules actions between the EVA and the alkanes by using density function theory, semi-empirical quantum chemistry, molecular mechanics and molecular dynamics, etc. Adsorption energy was calculated at the planes of paraffin crystals and the adsorption principles were simulated. It was indicated that the polar groups enhanced the rigidity of the corresponding molecules and the non-polar groups kept good affinity for the alkane molecules, which made it possible for the PPD molecule to invade into the crystal lattice and form co-crystllization complexes. The simulation results suggested that the EVAP showed significant effects on the nucleation and growth of the paraffin crystal when the acetate concentration was around 30 % and there existed one branched methyl group near the acetate group in each block. Based on the above molecular design, the synthesis of the optimal PPD additives were investigated and the operation conditions were optimized. The scale-up effects were studied and the industrial produce of the PPD additives was accomplished. Cooperating with some oil refineries in China, the practical applications of the PPD products obtained effective decrease for CFPP, superior to other commercial PPD additives. Finally, an artificial neural network (ANN) model was established, according to the industrial requests of the oil refinery in the Yanshan Petroleum Chemical Co., to estimate accurately the CFPP of the blended diesel oil with and without the PPD additives. The ANN model consisted of 26 input nodes including the component fraction, viscosity, refraction index, distillate range, and CFPP for five classes of diesel oil, and the PPD additive amounts, together with 50 hidden nodes and one output node of the CFPP of the blended diesel oil. When the mean square error (MSE) was set as 0.5 oC, the ANN model showed the optimal accuracy to estimate the CFPP of the blended diesel oil with and without the PPD additives. The obtained ANN model has promising applications in instructing the industrial process of blended diesel oil manufacture.