Effect of Mixed Commercial Cold Flow Improvers on Flow Properties of Biodiesel from Waste Cooking Oil

The uniform design method was used to screen the solidifying point depressing effects of 18 traditional diesel cold flow improvers on biodiesel derived from waste cooking oil. The cold flow improvers with good effects were selected for orthogonal optimization. Finally, the mixed cold flow improver (CFI) with the best depressing effect was selected to explore its depressing mechanism for biodiesel. The results show that the typical CFIs such as A132, A146, 10-320, 10-330, A-4, CS-1, AH-BSFH, Haote, T1804D, and HL21 all have a certain solidifying point depressing effect on biodiesel, while other cold flow improvers had no obvious effect. Amongst them, 10-330 (PMA polymer) and AH-BSFH (EVA polymer) had better solidifying point depressing effects over others, both of which reduced the solidifying point (SP) of biodiesel by 4 °C and the cold filter plugging point (CFPP) by 2 °C and 3 °C, respectively. From the orthogonal mixing experiment, it can be seen that the combination of 10-330 and AH-BSFH at a mass ratio of 1:8 had the best depressing effect, reducing the solidifying point and cold filter plugging point of biodiesel by 5 °C and 3 °C, respectively. Orthogonal analysis showed that when used in combination, AH-BSFH had a greater impact on the solidifying point, while the ratio of the combination had a greater impact on the cold filter plugging point.

Synthesis, Acidity and Catalytic of the Rare Earth Ce Loaded on the Composite Pore Zeolite Catalyst for Hydrogenation Cracking

Composite molecular sieve Y/SBA-15(C-Y) was prepared by microwave method, while Ce was loaded by ion exchange method to the composite molecular sieves (Ce-Y/SBA-15 (C-X)). Productive-type middle distillate hydrocracking catalyst was prepared from C-X and C-Y loaded. FI-IR, XPS, Pyridine IR, and TG-DTG had been used to characterize the C-X’s and C-Y’s structure and acidity. The results showed that Ce loaded not only had not broken the original structure of C-Y, but also improved silica alumina ratio of C-X, furthermore improved its total acid content. Through polarization and entrainment, Ce increased the skeleton and hydroxyl silicon aluminum hydroxy on electronic probability of migration to the cage, thus enhance the C-X’s B acid strength, make it more suitable for heavy oil processing. As compared with C-Y, the selectivity and yield of middle distillates over C-X was 0.7 % and 1.8 % higher, respectively. C-X have the greatest relief wax oil viscosity index, best once cracking selectivity, and lowest levels of diesel oil solidifying point in the three catalysts.

Synthesis and Performance Evaluation of Pour Point Depressants for Lubricating Oil

The paper describes synthesis and evaluation of polymeric additives for improving the flow properties of lubricating oils. The polymer (AAV) was prepared by the free-radical initiated polymerization of methacryl esters (A14) with acrylamide and vinyl acetate. A14 and AAV were characterized by infrared spectroscopy (IR). Three lubricating oils were selected as the test oil samples and the effect of solidification point depressant (ΔSP) with different mass fractions and other physicochemical characteristics of the samples with and without AAV added were investigated. In order to analyze the effect of AAV on the viscosity of lubricating oils, viscosity-temperature curves were plotted. The results showed that AAV not only had a good effect on dropping solidifying point for Yanshan lubricating oils, but also had a good effect on dropping viscosity; what’s more, other physicochemical characteristics of lubricating oil have little changes before and after AAV added.

Catalytic Cracking Light Cycle Oil Solidifying Point Soft Sensor Based on SVM

Genetic Algorithms and Support Vector Machines are introduced first in this paper. A mathematic model for predicting the solidifying point of light cycle oil of catalytic cracking unit is developed on the basis of the practical data. Results of on-line calculation show that the deviation between the predicted value and is fit to width.This model by way of the soft meter is used to optimize real time unit operation.

Soft Sensing Based on EMD and Improved PSO-SVM

A kind of soft sensing is proposed by combining empirical mode decomposition(EMD) with support vector machine optimized by improved particle swarm optimization (IPSO-SVM). EMD is a highly adaptive decomposition and can decompose any complicated signal into so called Intrinsic Mode Functions (IMF), which not only has excellent performance of feature extraction but also can reduce the dimension of the model input data space. we can extracts IMF energy feature as the input feature vectors of IPSO-SVM. Support vector machine (SVM) has been successfully employed to solve regression problem but it is difficult to select appropriate SVM parameters. A new SVM model based on adaptive particle swarm optimization (APSO) for parameter optimization is proposed which not only has strong global search capability, but also is very easy to implement. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that IPSO-SVM approach has a better prediction and generalization.

Soft Sensing Based on Kernel Isomap

A novel method of soft sensing is propsed combined Kernel Isomap (KIsomap) with Least squares support vector machines (LS-SVM). KIsomap is an improved Isomap and has a generalization property by utilizing kernel trick. It is a kind of novelly promoted nonlinear methods for dimension reduction, and can effectively find out the intrinsic low dimensional structure from high dimensional data. The KIsomap is used to feature extraction and reduce dimensions of sample. The LSSVM is applied to proceed regression modelling, which can not only reduce the complexity of modeling but also improve the generalization ability.The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that KIsomap-LSSVM approach is effective and correct.

Experimental Report of Producing Artificial Diesel Oil and Artificial Glycol from Large Proportion Water

The producing of artificial diesel oil and artificial glycol from 75% and 60% water and the rest were commercial diesel or glycol respectively and a little same special additive are reported. The main contents in both liquid were measured. The test results show that the density and the calorific value of the artificial diesel oil were close to the added original diesel oil, but its solidifying point decreased a lot. The elements of the artificial diesel oil are mainly carbon and hydrogen. Infrared spectrum diagrams also showed that there was no water in it. As a whole, the artificial diesel oil is not an oil-water emulsion, but a hydrocarbon liquid even after depositing for 13 years. In the artificial glycol the contents of hydrogen and carbide are closed to the theoretical value but it contained 0.45% water. The above mentioned two important facts show that the new chemical engineering utilizing water will be a very promising area in the near future.

Predicting Cold Flow Properties of Diesel by Terahertz Time-Domain Spectroscopy

The frequency-dependent absorption characteristics of conventional diesel fuel have been researched in the spectral range of 0.2–1.5 THz by the terahertz time-domain spectroscopy (THz-TDS). The absorption coefficient increased monotonically with the solidifying point of diesel. A nonlinear regression model was established, and the cold flow properties of fuel were presented quantitatively. The results made the solidifying point prediction possible by THz-TDS technology and indicated the bright future in practical application.