Study and Commercial Test on Technology for Deacidification of Distillate Oils

A new kind of solvent for deacidification of distillate oils was introduced in this paper. After successful laboratory study this technology had been applied in commercial scale successfully. Compared to traditional caustic wash of distillate oils, this technology has a lot of merits, such as the broad range of distillates to be processed, low caustic consumption, recycle of deacidifying agent, absence of waste caustic discharge, and low equipment revamp expenses, which can have promising perspectives for exploitation and application of this technology.

A Real-time Prediction System for Molecular-level Information of Heavy Oil Based on Machine Learning

Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.

Simultaneous optimization of heat-integrated crude oil distillation systems

Crude oil distillation is important in refining industry. Operating variables of distillation process have a critical effect on product output value and energy consumption. However, the objectives of minimum energy consumption and maximum product output value do not coordinate with each other and do not lead to the maximum economic benefit of a refinery. In this paper, a systematic optimization approach is proposed for the maximum annual economic benefit of an existing crude oil distillation system, considering product output value and energy consumption simultaneously. A shortcut model in Aspen Plus is used to describe the crude oil distillation and the pinch analysis is adopted to identify the target of energy recovery. The optimization is a nonlinear programming problem and solved by stochastic algorithm of particle warm optimization.

Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

The modeling and optimization of an industrial-scale crude distillation unit （CDU） are addressed. The main spec- ifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a com- plicated CDU, an improved wavelet neural network （WNN） is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm （LCA）, the supe- rior performance of the proposed approach is verified. Compared with the base operating condition, it is validat- ed that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around （PA2） and third Dump-around （PA3）.

Exergy analysis of multi-stage crude distillation units

This paper aims effect on crude distillation to investigate the multi-stage units （CDUs） in thermody- namics. In this regard, we proposed three-, four-, five-, and six-stage CDU processes with all variables constrained to be almost the same except for the number of stages. We also analyzed the energy and exergy to assess the energy consumed by each process. Because additional distillation units would share the processing load and thus prevent products with low boiling points from overheating, the heat demand of the CDUs decreases with increasing stages and thus reduces the heat supply. Exergy loss is considered as a key parameter to assess these processes. When the exergy losses in heat exchangers are disregarded, the three- and four-stage CDUs have lower exergy losses than the five- and six-stage CDUs. When the overall exergy losses are considered, the optimum number of stages of CDUs depends on the exergy efficiency of heat integration.