Abstract The cooling water is sprayed into the desuperheater to regulate the temperature of the superheated steam. Computational fluid dynamics (CFD) was applied to investigate the spray evaporation process in the desuperheater. Discrete phase model (DPM) was applied to describe the gas-liquid two-phase flow characteristics based on the Eulerian-Lagrangian approach. The Rosin-Rammler distribution and TAB model were used for the description of the primary and secondary droplet breakup, respectively. The coupling effect of gas-liquid two-phase, influence of temperature on latent heat, the stochastic collision and coalescence of droplets were considered in the CFD model. The numerical results show good agreement with the plant data. The hydrodynamics and temperature distribution characteristics were analysed. The influence of water mass flow rate and orifice dimensions on temperature distribution and temperature non-uniformity was further investigated. The results indicate that increasing the water flow rate can improve the desuperheating ability, but it will make the temperature uniformity worse. Under the same operating conditions, smaller orifice diameters are beneficial for production of droplets with smaller size, leading to better desuperheating ability.
In steel-making process, when a furnace is charged, there are many optional steel grades for each slab. It is a difficult problem to select the appropriate steel grade for each slab. In this paper, based on the analysis of technics constraints in steel-making process, the steel grade intensivism problem is described, and the mathematical model is also established. To solve the above problem, a newly designed hierarchical genetic algorithm is proposed, where the hierarchical manner is used to decrease the solution space. The effectiveness of the approach is demonstrated by a simulation. The optimal solution can be obtained in reasonable time, which will be helpful to decrease the scraps between two steel grades while casting, to decrease the sum of surplus, and eventually to cut down the stock.
The combining tundish problem on continuous casting plan was described and the multiple traveling salesman problem(MTSP) model was constructed.A hybrid optimization algorithm composed of the heuristic method,the k-opt neighborhood search,and the estimation of distribution algorithms(EDA) was proposed to solve the model.First,a heuristic method was used to determine the counts of dummy furnace which were involved in chromosome code to fix on the code length.Each chromosome presented a scheme of combining tundish and then the probability matrix model of the EDA was designed to optimize chromosome globally.Moreover,the k-opt was used as local search strategy.Unlike genetic algorithm(GA),the EDA had no crossover operator,therefore the illegal code resulted from crossover operator was avoided.Simulation results on the real production data indicated that the proposed algorithm had fairly good performance and utility.
On the basis of research on charge design and cast scheduling methods and process rules of steelmaking-continuous casting production scheduling in melting factory, we establish a constrained 0-1 integer quadratic programming model for optimal charge design and cast scheduling, and then propose the structure of PSO algorithm and the solving method to this model. And the simulation computation with practical data shows that both the model and the solving method proposed are effective.
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