Inference of differential equations by M-MEP for cement hydration modeling

The hydration of cement is a complex process with chemical and physical interaction and it has an important impact on the formation of microstructure and development of strength. In this paper, we designed a collaborative hybrid evolutionary method to infer cement hydration model from observed cement hydration time series data using ordinary differential equations (ODE).The structure of the ODE is inferred by multi-layers multi-expression programming(MMEP) , the ODE´s parameters are optimized through particle swarm optimization (PSO) and the forth-order Runge-Kutta(RK4) is used to solve the differential equation. Numerical experiments showed that the proposed method could acquire the structure of differential equation within short generations. Cement experiment on the degree of hydration model illustrated that the cement hydration model could simulate the process of hydration effectively.