Charging operation is an essential regulatory measure at the top of blast furnaces. Accurately predicting the burden surface shape (BSS) and reasonably adjusting the charging matrix (CM) are vital for achieving precise charging operation. However, the existing methods neglect the influence of the burden motion state, which greatly limits prediction accuracy of BSS, and complex constraints of CM further cause the lack of effective CM optimization strategies. To address these challenges, a BSS prediction model and a CM optimization strategy are innovatively proposed in this article. First, the burden motion state under different accumulation behaviors is introduced into the BSS modeling, and the iterative solution of BSS is realized with the volume conservation constraint. Then, a hybrid constraint-handling mechanism consisting of the infeasible solution rejection method and the penalty function method is proposed, which is used to develop a CM optimization model. Finally, an improved algorithm, named dynamic heterogeneous multiswarm particle swarm optimization, is designed to solve the optimal CM (i.e., the best particle in the population). The prediction results of BSS model are highly similar to those of the model based on discrete element method, and the effectiveness of CM optimization strategy is validated in two simulation cases.

中文翻译：

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高炉装料过程的炉料表面形状建模和装料矩阵优化


加料操作是高炉炉顶一项重要的调控措施。准确预测装料面形状（BSS）和合理调整装料矩阵（CM）对于实现精确装料操作至关重要。然而现有方法忽略了负载运动状态的影响，极大地限制了BSS的预测精度，而CM的复杂约束进一步导致缺乏有效的CM优化策略。为了应对这些挑战，本文创新性地提出了BSS预测模型和CM优化策略。首先，将不同堆积行为下的载荷运动状态引入BSS建模中，并在体积守恒约束下实现BSS的迭代求解。然后，提出了一种由不可行解拒绝法和罚函数法组成的混合约束处理机制，用于开发CM优化模型。最后，设计了一种称为动态异构多群粒子群优化的改进算法来求解最优CM（即种群中的最佳粒子）。 BSS模型的预测结果与基于离散元法的模型高度相似，并通过两个仿真案例验证了CM优化策略的有效性。