Energy Management And Control System For Iron And Steel Enterprises

Iron and steel enterprises are constantly advancing with the development of science and technology.Enterprises are also improving their energy utilization levels,but byproduct gas,as an important by-product of iron and steel enterprises,still has a large amount of waste,how to increase its utilization rate,reduce production costs and reduce environmental pollution.These aspects still have great research value,so this paper studies the scheduling of by-product gas is very necessary.This paper firstly investigates the actual research system,Baotou Steel Group,and elaborates on its production process,production environment,by-product gas-related equipment,and energy management and control system.Compare the current status of current by-product gas utilization at home and abroad,and summarize the current problems.As the focus of this paper,gas must first obtain data through the energy management system,and then analyze the data to clarify the direction in which the gas needs to be optimized.Moreover,due to unsatisfactory gas dispatching,the gas is released,which wastes resources and destroys the environment.Therefore,through the heat demand of the gas in the steel plant,the reasonable proportion of gas,in order to meet the demand,while achieving the lowest gas consumption costs.Based on these two purposes,the relevant data were consulted,and the genetic algorithm and particle swarm optimization algorithm used in this paper were selected.The two algorithms were used to solve the optimization problem.Using the data in the energy management system,set the constraints on the variables in the optimization algorithm,and then reduce the number of variables according to the actual production requirements,reduce the number of genes in the genetic algorithm,and reduce the particle size in the particle swarm algorithm.,to reduce the difficulty of algorithm optimization.Then,according to the constraint conditions,the fitness function and the constraint violation function are set,and then the actual optimization algorithm is designed and the program is written.In the process of optimization,in order to avoid the genetic algorithm can not produce results due to accidental conditions,the algorithm adopts multiple populations to iteratetogether to reduce accidental errors and improve the accuracy of the results.In order to improve the global search ability and the late convergence ability of the particle swarm algorithm,this paper uses the particle swarm optimization algorithm with inertia weight to solve.Compare the results of the two algorithms and analyze the results.Finally,the value of the gas output rate under actual constraints is solved,and at this rate,the gas is not dissipated and the production cost is the lowest.The gas is not dissipated and the output gas rate meets the heat demand of the entire plant,indicating that the gas system is in equilibrium.Achieved the purpose of the study.