Ethylene production raw material optimization method based on hybrid modeling

Abstract

The invention relates to an ethylene raw material optimization method based on a mechanism and data hybrid modeling and linear programming technology, which utilizes a large-scale linear programming method to carry out ethylene raw material selection optimization in a constraint range of a whole plant so as to maximize profits of the whole plant. The method comprises the following steps: firstly,establishing a mechanism model for an ethylene cracking furnace, simulating and generating massive data samples based on the mechanism model, constructing a neural network data model, and establishinga hybrid model for predicting the cracking product yield; secondly, aiming at the nonlinear relation that the yield of the cracking product changes along with the raw material attributes and the process parameters, adopting a multi-section linear processing mode, and constructing a reference-increment database between the yield and the raw material attribute key parameters and between the yield and the key process parameters through a mixed model; and writing the reference-increment multi-segment linear structure database of the yield into a plan optimization linear programming model in combination with other constraint information, and solving the model in a distribution recursion mode to obtain a raw material selection optimization result. In order to ensure the accuracy of a yield reference-increment structure and the prediction precision of a linear programming model, the hybrid model can automatically correct model parameters and quickly update a yield reference-increment multi-segment linear structure database. The method provided by the invention can provide a quantitative basis for raw material purchasing and planned production scheduling of ethylene enterprises, so that the economic benefits and the raw material utilization rate of the enterprises are improved.

Description

technical field [0001] The invention relates to the research field of ethylene production process optimization, in particular to an ethylene raw material optimization method based on mixed modeling and linear programming. Background technique [0002] Ethylene industry is the leader and core of petrochemical industry. Ethylene products account for more than 75% of petrochemical products. Ethylene production is an important indicator to measure a country's petrochemical development level. The cost of raw materials in ethylene production accounts for 80% to 90%, which directly determines the cost of ethylene production. The structure and properties of ethylene raw materials have a huge impact on product yield and production cost. Generally speaking, the higher the raw material lightening rate and the higher the linear alkane content, the higher the diene yield and the high value-added yield, and the better the corresponding marginal benefit. Domestic ethylene production ente...

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Problems solved by technology

However, this method has the following problems: First, when there are many raw material streams, many types of cracking furnaces, and a large number of cracking furnaces (the ethylene plant above the million-ton level generally contains more than 10 cracking furnaces), it is necessary to simulate the ethylene cracking furnace A large number of multiple calls, the optimization process is cumbersome and time-consuming, resulting in low calculation efficiency; second, in the actual industrial process, the properties of raw materials and the dynamic changes of the cracking furnace operation state, the ethylene cracking furnace model inevitably deviates from the actual process

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