Method for optimizing cracking depth of industrial ethane cracking furnace on line

Abstract

The invention relates to a method for optimizing the cracking depth of an industrial ethane cracking furnace on line. A cracking furnace yield neural network multi-model is established by utilizing a mechanism model and practical production data as training samples and combining a cracking raw material clustering result, model input variables comprise raw material density, sum of raw material linear chain alkane contents, ratio of raw material N-alkane content and isoparaffin content, feed flow, gas and hydrocarbon ratio, coil outlet temperature (COT) and coil outlet pressure, and model output variables comprise ethane yield and propylene yield (or triene total yield). A belonged submodel is judged according to a practical measuring value of an oil product, and the cracking depth output by the submodel is corrected on line by a cracked gas online analyzing instrument; an optimal cracking depth value and the gas and hydrocarbon ratio are confirmed by utilizing an SQP method and taking maximal economic benefits as a target through the constraints of the cracking depth and the gas and hydrocarbon ratio, and the cracking depth value is continuously optimized back and forth nearby the current cracking depth value of the cracking furnace. The method is reasonable, reliable, simple and easy, is easy to transplant and has wide adaptability.

Description

technical field [0001] The invention relates to an online optimization method for the cracking depth of an industrial ethylene cracking furnace. Background technique [0002] The ethylene industry is the leader of the petrochemical industry, and the ethylene output is a symbol to measure the development level of a country's petrochemical industry. The cracking furnace is in the leading position in the ethylene plant, and its control not only directly affects the product quality and output of the entire ethylene production plant, but also affects downstream production plants (such as polyethylene, polypropylene, ethylene glycol, etc.) smooth operation. [0003] figure 1 It is a typical cracking furnace process flow and control diagram. After the hydrocarbon raw material F101 enters the cracking furnace BA101, it is firstly preheated in the preheating section, mixed with dilution steam F102, and then further preheated and completely vaporized to make its temperature rise. H...

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Problems solved by technology

However, domestic ethylene production enterprises are generally faced with complex sources of cracking raw materials and large fluctuations in oil properties, which makes the production and operation mode of fixed COT unable to timely reflect the cracking depth and Changes in the yield of cracking products; for this reason, East China University of Science and Technology took the lead in developing and implementing a cracking depth control system (patent number: ZL200510025043.6), such as figure 1 As shown, it includes a cascade control system composed of the cracking depth control system and the COT control system. The measured value of the COT control system comes from the thermocouple TI105 at the outlet of the cracking furnace tube, and its set value is calculated by the output of the cracking depth controller AIC105 It can be obtained that the cracking depth index generally adopts the ratio of propylene to ethylene (mass ratio of propylene to ethylene) or the ratio of methyl to propylene (mass ratio of methane to propylene), and uses the on-site pyrolysis gas composition online analyzer AI105 data as a reference for the measurement of the cracking depth controller AIC105 value

[0005] The cracking depth control system has changed the control method of using COT instead of cracking depth in the traditional industrial field, but the setting value of the cracking depth control system is still manually determined, lacking quantitative guidance, and literature search found that there is still no online optimization of the cracking depth setting value Therefore, it is of great significance to develop an online optimization technology for the cracking depth of industrial ethylene cracking furnaces by making full use of the on-site raw material composition information and cracked gas online analyzer, combined with data mining and optimization technology

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