Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques

Given the importance of process control in the petrochemical industry, there is a need to determine the behavior of the fluids inside the pipes. In this work a methodology is developed for the identification of flow patterns in vertical pipes with diameters between 0.01 m and 0.10 m, from the implementation of artificial intelligence techniques, for a liquid combination of two phases composed of oil with viscosity in the range of 792 Kg/m3 to 1823 Kg/m3 and water at room temperature. The predictive models generated in the structuring of the methodology were trained with 70% of data based on viscosity parameters, pipe diameter, volume fraction and surface velocities of the working fluids stored in a database. The remaining information, equivalent to 30% of the total, was used to develop the automatic model validation. The flow patterns identified by the intelligent system for oil and water flow, without considering the predominant substance, are churning, dispersed, very fine dispersion, transition flow, intermittent, and annular

Numerical Simulation of Failure Analysis of Storage Tank with Partition Plate and Structure Optimization

Storage tanks with partition plates are widely used in the petrochemical industry. However, relevant standards do not propose corresponding design criteria and methods for this type of structure, and theoretical design formulas cannot be applied to ensure the reliability of its structure. Therefore, it is necessary to analyze and design the storage tank with a partition plate by using finite elements. This paper studies the problem of buckling depression and cracks in the welded parts of the S-shaped tank with a partition plate during its operation. We used the finite element software ANSYS to analyze the overall strength and stability of the structure and obtain the larger stress area. Based on this, a safe and economical optimization plan is proposed: under the condition of strictly controlling the liquid level difference on both sides of the partition, the tank structure is optimized by adding stiffeners and tie rods. The study revealed that the measure effectively improves the overall rigidity of the tank body and reduces the maximum stress of the structure and enhances the safety performance of storage tank. Additionally, it provides a reference for the structural strength design of storage tanks with partition plates.

Evaluation and Prediction on the Effect of Ionic Properties of Solvent Extraction Performance of Oily Sludge Using Machine Learning

Oily sludge produced in the process of petroleum exploitation and utilization is a kind of hazardous waste that needs to be urgently dealt with in the petrochemical industry. The oil content of oily sludge is generally between 15–50% and has a great potential for oil resource utilization. However, its composition is complex, in which asphaltene is of high viscosity and difficult to separate. In this study, The oily sludge was extracted with toluene as solvent, supplemented by three kinds of ionic liquids (1-ethyl-3-methylimidazole tetrafluoroborate ([EMIM] [BF4]), 1-ethyl-3-methylimidazole trifluoro-acetate ([EMIM] [TA]), 1-ethyl-3-methylimidazole Dicyandiamide ([EMIM] [N(CN)2])) and three kinds of deep eutectic solutions (choline chloride/urea (ChCl/U), choline chloride / ethylene glycol (ChCl/EG), and choline chloride/malonic acid (ChCl/MA)). This experiment investigates the effect of physicochemical properties of the solvents on oil recovery and three machine learning methods (ridge regression, multilayer perceptron, and support vector regression) are used to predict the association between them. Depending on the linear correlation of variables, it is found that the conductivity of ionic liquid is the key characteristic affecting the extraction treatment in this system.

Butadiene Rubber in the Petrochemical Industry

The Butadiene is a raw material used in the petrochemical industry. The use of Butadiene has risen with petrochemical market growth. The Global market is forecasting a demand growth for butadiene applications, especially for rubber materials. The estimated synthetic rubber market is $19.1 billion in 2021 and forecasted to reach $23.2 billion in five years. The dynamic growth in butadiene applications will introduce new products used in many things from the food industry to sports and goods. Also, the rubber materials have different applications in the automotive industry, oil and gas, medical products, and plastics. Companies’ strategic planning to increase the production of synthetic rubber for the global market. The demand increased as new applications were introduced to the market. The stability of oil prices will have the rubber market steady which always leads to optimal pricing. The diver for Butadiene rubber applications is to maximize production by having different kind of materials that applied for several products. The global business development indicated the ability to increases the synthetic rubber market rubber and capacities, which will enhance the chemical process techniques, new technology design, and efficiency that will maximize production and minimize product cost. Looking into the price difference between synthetic and natural rubber, many fluctuation variables were introduced in the price of each type. For example, synthetic rubber price is high, depending on crude oil, natural gasoline and naphtha prices, since those feedstocks are fed to the cracking units, as C4 is one of the cracking products. Therefore, any change in the oil prices will influence the butadiene price, which is the feed for most rubber plants. In addition, the utilities required for those plants to operate have a major impact on overall price. On the other hand, Natural rubber is an agricultural product and dependent on soil type and weather.

HAZOP Ontology Semantic Similarity Algorithm Based on ACO-GRNN

Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve the problems of semantic comparison and matching, and lays a good foundation for subsequent knowledge retrieval and reuse.