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.