Research On Prediction And Scheduling Of Cold Rolling Gas System In Iron And Steel Enterprises

As an important energy source,gas has a direct impact on iron and steel enterprises in terms of its reuse rate and release rate.Gas consumption will fluctuate greatly with different conditions,and sudden excess or shortage will not only affect the production of enterprises,but also increase the pressure on resources and the environment.Therefore,the prediction and dispatch of gas become the difficulty in energy management.Considering the characteristics of non-linearity,instability and strong correlation of gas consumption data,an empirical mode decomposition-long short-term memory gas consumption model is established.Empirical mode decomposition decomposes complex signals into simple components with frequencies from high to low,highlighting their changing trends.The long short-term memory can mine the information in the gas sequence,solve the problem of strong correlation,and finally realize the prediction of gas consumption.The results show that the accuracy of this method can reach 0.957.Considering the problem of gas data mixed with noise,a prediction model combining variational mode decomposition and gated recurrent neural network is established.Variational mode decomposition can solve the model aliasing that occurs during decomposition,and combine with the Pearson correlation coefficient can remove the noise components,and the real gas sequence is obtained.Gated recurrent neural network can solve the long-term dependence problem of sequence,and finally realize gas consumption prediction.The results show that the prediction accuracy can reach 0.979.Considering the different characteristics of gas consumption in iron and steel enterprises under different conditions,based on the gas consumption prediction,a scheduling model based on the hybrid genetic grey wolf algorithm is established.The objective function is to minimize the gas consumption,and satisfy various constraints of gas consumption equipment and rolling production.The results show that the obtained production scheme can schedule the rolling plan and minimize the gas consumption.Figure 29;Table 8;Reference 61...