分时电价下炼钢连铸生产调度优化方法

发布时间：2018-01-09 14:07

  本文关键词：分时电价下炼钢连铸生产调度优化方法　出处：《山东大学》2017年博士论文　论文类型：学位论文

  更多相关文章： 炼钢连铸 车间调度 智能优化 分时电价 交叉熵算法

【摘要】：炼钢连铸生产过程是钢铁企业生产中的一个重要环节,具有多阶段、多机器、以及离散与连续生产相混合的特点。其生产调度优化可以使钢铁企业各工序间有效衔接,并加快生产节奏、提高生产效率、降低生产成本,历来是企业界和研究领域关注的热点问题。炼钢连铸生产过程消耗的能源介质种类多,其中电能消耗量大而且电力成本与分时电价密切相关。优化分时电价下的电能消耗和电力成本可以降低生产过程的总能耗及能源成本,提高企业经济效益,具有重要的理论研究意义和实际应用价值。本文针对分时电价下电能消耗及电力成本最小的炼钢连铸生产调度优化这一复杂问题,从工艺路径相同、复杂工艺路径、加工时间不确定、多目标等情况进行深入研究。针对工艺路径相同的炼钢连铸生产过程电能消耗及电力成本最小化问题,建立了分时电价下的调度优化模型。针对该模型引入分时电价后0-1变量急剧增加、目标计算复杂、求解速度慢等问题提出了一种基于局部搜索的混合启发式交叉熵算法。该算法采用矩阵编码策略和基于阶段顺序的倒推解码方法、基于FIFO启发式规则的混合样本生成和基于行列交换的局部搜索等策略,能在较短的时间内求得高质量的调度方案,具有很好的稳定性和收敛性。仿真结果表明,与优化炉次驻留时间以间接降低能耗相比,该优化模型对降低炼钢连铸生产过程的电能消耗效果更好,尤其是分时电价下的电力成本优化效果更明显。针对复杂工艺路径的炼钢连铸生产过程电能消耗及电力成本最小化问题,建立了分时电价下的调度优化模型。与工艺路径相同的情况比较,复杂路径约束导致交叉熵算法的编码与解码更困难;分时电价的引入使优化模型的决策变量规模扩大至少三倍,导致目标计算更复杂、模型更难以求解。因此提出了一种基于动态参数的混合自适应交叉熵算法。该算法采用基于操作顺序的倒推解码方法,以及基于全局选择和随机置换启发式规则的混合样本生成、基于矩阵分割与行列交换的局部搜索和参数动态调整等策略,求解质量高、求解速度快、自适应能力强。仿真结果表明,该优化模型能有效地描述更复杂的大规模炼钢连铸生产过程,在优化分时电价下电能消耗及电力成本方面比只考虑炉次驻留时间时的效果更好。针对炼钢连铸生产过程中炉次LF精炼时间和炉次基本加工时间等不确定的情况,建立了分时电价下电能消耗及电力成本最小化问题的调度优化模型。该模型整数变量及其约束条件增加,LF精炼时间需要调整,从而决策变量更多、规模更大、求解更困难。因此,提出了一种离散与连续交叉熵算法相混合的串级交叉熵算法。该算法将不确定加工时间的求解与炉次机器分配状态的求解分别进行,简化了问题的编码及解码过程,减少了不可行解的数量,避免了遗传算法染色体太长、交叉变异复杂、解码困难的问题,从而缩短了求解时间。提出了基于关键炉次的混合调整方法,对炉次LF精炼时间进行调整以补偿温度损失,降低了分时电价下增加的电能消耗及电力成本。仿真结果表明,与加工时间确定的随机实例和特殊实例的求解结果相比,该模型优化了不确定加工时间的组合,在减少电能消耗及电力成本方面合理且有效。最后,针对炼钢连铸生产过程考虑分时电价后电能消耗、电力成本以及完工时间等多个目标相互矛盾而难以抉择的问题,建立了多目标调度优化模型。针对该模型目标种类多、引入分时电价后计算更复杂、个体排序及评价困难、求解结果多样性差、分布较集中等问题,提出了一种基于Pareto最优的混合多目标交叉熵算法。该算法采用混合多样本生成、基于快速非支配排序的个体评价、基于拥挤距离和精英样本聚类的多样性保持等策略,达到了很好的求解效果。尤其是聚类算法的引入,有效地避免了非支配解向Pareto前沿中间部分聚集,提高了非支配解的多样性和分布广泛性。仿真结果表明,该模型的求解既能为调度决策者提供各目标相对均衡的折中方案,又能提供在基本不恶化其它目标的情况下偏向某个目标的调度方案。
[Abstract]:Steelmaking and continuous casting production process is an important link in the production of iron and steel enterprises, with multiple stages, multiple machines, and the characteristics of discrete and continuous production mix. The production scheduling optimization of iron and steel enterprises can make effective connection between each process, and accelerate the pace of production, improve production efficiency, reduce production costs, is always a hot topic business and research in the field of energy consumption. The medium type of steelmaking and continuous casting production process, the electricity consumption and electricity cost and TOU price are closely related. Optimization of TOU electricity under the total energy consumption and cost of energy consumption and electricity costs can be reduced in the production process, improve the economic efficiency of enterprises, has the important theoretical significance and practical value. Based on TOU power and power consumption under the minimum cost of steelmaking and continuous casting production scheduling optimization of this complex problem, from The same process path, complex process, uncertain processing time, in-depth research on multi objective and so on. In the same process of steelmaking and continuous casting production process of electricity consumption and electricity cost minimization problem, a scheduling optimization model. The price for 0-1 variables a sharp increase in the model introduced tou, target complex calculation, problem solving speed presents a hybrid heuristic cross entropy algorithm based on local search. The algorithm uses the matrix encoding and decoding method based on the strategy of pushing down the stage order, based on the mixed sample to generate FIFO heuristic rules and based on the ranks of exchange local search strategies such as scheduling scheme can be obtained in high quality a short period of time, has good stability and convergence. The simulation results show that compared with the optimal furnace dwell time to indirectly reduce energy consumption, the optimization The model can better reduce the consumption of electric steelmaking and continuous casting production process, especially the TOU power cost optimization effect is more obvious. In view of the complex process path of steelmaking and continuous casting production process of electricity consumption and electricity cost minimization problem, a scheduling optimization model for electricity price under time. Compared with the same process conditions that complex path constraints lead to encoding and decoding cross entropy algorithm is more difficult; tou was introduced to make decision variable scale optimization model is enlarged at least three times, the target calculation is more complex, more difficult to solve the model. This paper presents a method based on the dynamic parameters of the hybrid adaptive cross entropy algorithm. This algorithm is used to push down decoding method based on operation sequence, and based on the mixed sample generation global selection and random permutation heuristic rules, local search matrix segmentation and exchange based on ranks The cable and the parameters of dynamic adjustment strategy, high quality solution, solution speed, strong adaptive ability. The simulation results show that this model can effectively describe large-scale steelmaking and continuous casting production process more complicated, in optimizing the TOU price under the power consumption and the cost of electricity is better than only considering the effect of furnace residence time for steelmaking and continuous casting production process of furnace LF refining furnace time and basic processing time is uncertain, a scheduling optimization model of TOU power consumption and electricity cost minimization problem. The model increases the integer variables and constraints, the refining time of LF need to be adjusted, thus more decision variables, scale more and more difficult to solve. Therefore, put forward the cascade cross entropy algorithm combined a discrete and continuous cross entropy algorithm. This algorithm will not determine the processing time of the solution and furnace machine To solve the allocation state respectively, simplifying the encoding and decoding process, reduce the number of infeasible solutions, avoid the genetic algorithm chromosome crossover and mutation is too long, complicated, difficult decoding problems, shorten the solving time. Put forward the mixed adjustment method based on key furnace, furnace refining time of LF adjusted to compensate for temperature loss, reduce the TOU price increase power consumption and electricity cost. The simulation results show that, compared with the processing time of the solution to determine the random instances and special examples, the model optimizes the combination of processing time uncertainty in reducing power consumption and electricity cost is reasonable and effective finally, for steelmaking and continuous casting production process considering tou after power consumption, power cost and the completion time of a number of conflicting goals and difficult choices, establish a multi-objective Scheduling optimization model. According to the model of the target species, the introduction of TOU price calculation is more complex, individual ranking and evaluation results difficult, diversity, centralized distribution, proposes a cross entropy hybrid multi-objective algorithm based on Pareto optimal. The algorithm generated by mixed samples, non fast individual evaluation dominated sorting based on diversity crowding distance and clustering and elite strategy based on reaching a good solution. Especially the introduction of clustering algorithm, can effectively avoid the non dominated solution to the Pareto front in the part of aggregation, improve the wide diversity and distribution of non dominated solutions. The simulation results show that the model can provide decision makers for scheduling compromise each target is relatively balanced, but also provide a target bias scheduling scheme basically does not worsen the effects of other target case.

【学位授予单位】：山东大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TF758
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本文编号：1401681