Research On Energy Scheduling And Production Planning And Scheduling Optimization In Iron And Steel Enterprises Based On System Optimization

The steel industry is an essential component of China’s economic development.However,its high energy consumption and emissions have hindered sustainable growth.To address this issue,green and intelligent transformations in steel production processes are necessary.Energy scheduling and production planning and scheduling optimization are key technologies for achieving this transformation and are essential for energy savings.This dissertation focuses on the "blast furnaceconverter-continuous casting-hot rolling" process in the steel industry,employing concepts and methods from metallurgical process engineering and systems engineering,and utilizing adjacency matrices to analyze material flow and energy flow in the steel manufacturing process.Operating characteristics of the flows are characterized,and a theoretical model for system optimization is proposed.Based on this,energy scheduling and production planning and scheduling for steel enterprises are optimized using the system optimization theoretical model,and a simulation optimization platform for production and energy scheduling in iron and steel enterprises is constructed.To further verify the collaboration between material flow and energy flow,a synergy evaluation model and an offline test are employed.The specific research contents are as follows:Addressing the operational representation of material flow and energy flow in the steel processing industry,the graph theory was employed to create directed graphs.Adjacency matrices were used for the first time to analyze the structural and operational characteristics of material flow and energy flow.The concepts of node degree were used to quantitatively characterize and study the input-output and conservation relationships of material flow and energy flow in the steel manufacturing process.A theoretical model was proposed for optimizing the steel process,taking into account multiple criteria including efficiency(P),quality(Q),cost(C),resource consumption(R),and environmental impact(E).This model provides a foundation for subsequent optimization efforts.This study addressed the energy scheduling optimization problem for iron and steel enterprises by utilizing a system optimization theoretical model to establish static and dynamic energy optimal scheduling models.These models consider energy utilization efficiency and equipment operation status to achieve the optimal distribution of gas,steam,and electric multi-energy media.A static energy optimal scheduling model was constructed to minimize system energy costs and maximize energy utilization efficiency.Multi-objective optimization using a step-by-step approach produced Pareto solution sets and identified the best trade-off between cost and efficiency.The resulting energy balance showed a 22.81%reduction in energy costs and a 7.71%increase in energy utilization efficiency after the target enterprise adopted the model optimization.These improvements led to better energy cost and utilization efficiency for the entire system.A dynamic energy optimal scheduling model was constructed that took into account equipment load fluctuations and aimed to minimize energy operation costs as well as startup and shutdown costs for energy conversion equipment.The results showed that,under the same energy supply and demand conditions,only seven sets of equipment were needed to meet the energy demand after model optimization(with 1#35t boiler out of service).Furthermore,the thermal efficiency of the remaining steam boilers was improved compared to the results of single-objective optimization,resulting in a 5.20%decrease in total costs.To address production planning and scheduling optimization in iron and steel enterprises,the deep coupling relationship between energy consumption and the production process was considered.As a result,a hot rolling batch planning model and a steelmaking-continuous casting section production scheduling model were developed using a system optimization theoretical model.A hot rolling batch planning model was developed to minimize the total penalty value of adjacent slab width,thickness,hardness difference,and power consumption cost while taking energy cost into account.The model was tested with 240 slabs,and an improved genetic algorithm was used to solve it.Results showed that after considering energy cost,the power consumption cost of the hot rolling batch model was reduced by 8.9%and 1.9%compared to manual experience and the model without considering energy cost,respectively.This demonstrates the model’s ability to effectively reduce power consumption costs while maintaining product quality.A production scheduling model was developed for the steelmaking-continuous casting section that considered energy costs and aimed to minimize furnace waiting time and power consumption costs.The model was tested with 40 furnaces,and results showed a 6.25%reduction in waiting time between furnaces and an 8.24% reduction in power consumption costs compared to manually compiled results.These findings demonstrate the model’s effectiveness in reducing waiting time and electricity costs during the production process.Furthermore,a production scheduling model was developed for the continuous casting-hot rolling section heating furnace,taking into account the temperature drop of the slab,to reduce gas consumption during the hot rolling process and improve process cohesion.The model was tested with 240 slabs,and results showed a 5.57% reduction in slab temperature drop time,an increase of about 36 ℃ in the slab entering furnace temperature,and an 8.57% reduction in waiting time for slab rolling process compared to manually scheduled results.These findings demonstrate the model’s effectiveness in reducing energy consumption of the heating furnace while improving process cohesion.A simulation system was developed for production and energy scheduling in iron and steel enterprises,allowing for a comprehensive evaluation of the synergy between material flow and energy flow.The system was capable of constructing production processes,managing equipment,and regulating material and energy flow operations.A synergy evaluation model of material flow and energy flow was developed based on the theory of synergy.Using this simulation platform,the production of an Iron and Steel Company was simulated over six periods.Results showed that the degree of synergy played a crucial role in the evaluation of material flow and energy flow.The T1 period had the smallest synergy degree at 0.269,while the T4 period had the largest synergy degree at 0.732.Overall,This dissertation offers valuable insights into optimizing production planning and scheduling,energy consumption,and the synergy evaluation of material flow and energy flow in iron and steel enterprises.The results have the potential to improve the efficiency and sustainability of iron and steel production processes,while reducing energy consumption and costs.