Research On The Production Scheduling Optimization In Converter Steel Plants With The Synergy Of Material And Energy Flows

The steel industry has the typical characteristics of high material consumption,high energy consumption and high pollution.Green manufacturing and intelligent manufacturing are the inevitable development directions of China’s steel industry.Since the steel enterprises are faced with the urgent requirements of energy conservation and emission reduction,cost reduction and efficiency increase and other sustainable development goals,the operation optimization technology oriented to the synergy of material flow and energy flow in the steel manufacturing process will become an important means to improve the coordination efficiency of material flow and energy flow,realize the efficient production,energy saving and consumption reduction.For the steel enterprises with long process,the converter steel plant is the key to the formation of the variety,specification and quality of steel products.Ferriferous material flow moves from discrete to continuous,accompanied by energy demand constraints and temperature control requirements,etc.,the production scheduling considering the synergy of material flow and energy flow is the core technical means to ensure the operation optimization of steel manufacturing process.For this reason,the subject of "Research on the Production Scheduling Optimization in Converter Steel Plants with the Synergy of Material and Energy Flows " is put forward.With the improvement of the information construction in iron and steel enterprises and the development of optimization theories and technologies,the problem of material flow and energy flow operation optimization has been widely concerned by enterprises and scholars,and remarkable progress has been made in relevant modeling theories and solving technologies.However,due to the understanding of the complex coupling relationship between material flow and energy flow and the synergistic goals,the limitations of measures,as well as the limitations of dealing with the uncertain factors of material flow running time in the production process,there is still an obvious gap between the theoretical research results of steel production scheduling and the practical requirements.Therefore,this paper takes the production process of converter steel plants as the object,considers the control requirements of multi-procedure and multi-constraint operation optimization under the cooperation of production organization and energy saving and consumption reduction,and explores the optimization method of production scheduling for steel plants oriented to the synergy of matter flow and energy flow.Based on the analysis of the coupling relationship between material flow and energy flow,the optimization problem of production scheduling for material flow and its temperature drop control and the optimization problem of production scheduling for the coordination of material flow and oxygen consumption mode are studied.On this basis,the uncertain factors of the steel production and their potential effects are fully considered,and the steel production scheduling problem under the condition of time-related uncertainty is studied.Therefore,the operation optimization of material flow can be realized under the cooperative goal of material flow and energy flow in steel plants,which can provide guidance for the production organization and decision-making.The main research contents and conclusions are as follows:Aiming at the cooperative optimization of material flow operation and temperature drop control,the steel production scheduling problem considering the temperature drop of material flow was studied.Based on the analysis of the characteristics of high temperature operation in steel plants,the constraints of minimum casting superheat and the constraints of target tapping temperature were introduced on the basis of the classical scheduling model,and a multi-objective mathematical programming model for the problem was established.A multi-objective genetic evolutionary algorithm(MOHGALS)combined with enhanced evolutionary strategies was proposed based on the problem characteristics.The test results showed that MOHGALS method can get the optimal multi-objective Pareto solution set,and the production decision maker can dynamically select the corresponding optimal schedule according to their preference.In addition,the schedule selected by the principle of minimizing the temperature drop related penalty could further reduce the total temperature drop of material flow by 19.71%compared with the schedule selected by the principle of minimizing the starting time deviation penalty.The algorithm performance was tested and analyzed through different randomly generated cases.The results showed that the proposed enhanced evolution strategies can effectively improve the performance of MOHGALS.In addition,MOHGALS had better performance than NSGA-II and SPEA2 in multi-objective evaluation indexes.Aiming at the co-optimization of material flow operation and oxygen consumption mode,the steel production scheduling problem considering oxygen consumption optimization was studied.An optimal production scheduling model was built for minimizing the oxygen consumption fluctuation and the penalty of casts’ starting time deviation.To solve the model,a hybrid genetic algorithm combining variable neighborhood search(HGAVNS)was used.Results of computational experiments indicated that HGAVNS outperformed the genetic algorithm(GA)and the variable neighborhood search algorithm(VNS).The sensitivity analysis showed that the deviation penalty decreased with increasing model coefficient at the expense of increasing the oxygen consumption fluctuation.Results also indicated that the more frequent supply of hot metal and increasing percentage of dephosphorized charges would in general increase the oxygen consumption fluctuation.In addition,the analysis of two industrial cases showed that the oxygen demand fluctuation could be reduced by49.32% and 51.32% respectively at most,indicating that the proposed model could be conducive to ensuring the production stability and reducing the potential oxygen emissions,so as to realize cleaner and sustainable steel production.Considering the cooperation of material flow and energy flow,the proactive scheduling problem of steel production under the condition of time-related uncertainties was studied.A stochastic programming model for the problem was established to minimize the expected temperature drop of material flow,the expected oxygen consumption fluctuation in converter operation and the expected penalty of casting break time.A hybrid simulation optimization algorithm based on modified variable neighborhood search(h-Sim-MVNS)was proposed.The test results showed that the h-Sim-MVNS method could fully consider the potential impact of time-related uncertainties in advance,and obtain a predictive schedule with the expected optimal objectives through simulation evaluation and iterative optimization.In addition,compared with the benchmark algorithms,the experimental results showed that the PRI index obtained by h-Sim-MVNS is lower than the results of h-Sim-VNS and h-Sim-ILS,which verified that h-Sim-MVNS has better optimization performance in the test cases.Finally,the influence of different parameters such as the weight coefficient of the objective function and the intensity of hot metal supply on the synergistic effect of material and energy flow was obtained through sensitivity analysis experiments,which provided a decision basis for further application of the model.Finally,based on the above research contents,a simulation and optimization system of production scheduling for material flow and energy flow collaboration was developed.The deterministic scheduling function considering the temperature drop of material flow and considering the optimization of oxygen consumption could be completed respectively by the system.Optimized production schedules could be developed,and the influence of scheduling optimization on logistics temperature drop and oxygen consumption fluctuation in converter process could be visually presented through the display interfaces.The system also realized the simulation and optimization function of production scheduling under the condition of uncertain time.The simulation and optimization of schedules in stochastic environment could be realized based on the cellular automata model,and the system had many functions,such as process modeling,custom configuration of simulation parameters,simulation analysis,coordination effect evaluation of material flow and energy flow,etc.To sum up,this paper aimed at the steel production scheduling problem considering the synergy of material flow and energy flow.The production scheduling model considering the temperature drop of material flow,the production scheduling model considering oxygen consumption optimization,and the proactive scheduling model of steel production under the condition of time-related uncertainties established in this paper provided a new technical means for realizing the production control under the cooperative optimization objective of material flow and energy flow.The simulation and optimization system of production scheduling developed in this paper provided simulation experiments and auxiliary decision-making tools for the production regulation.