基于改进免疫克隆多目标算法的轧制规程优化

 针对冷连轧轧制规程目标函数之间存在耦合、相互制约、难以选择目标侧重的问题,采用了一种先优化后选择的方式设定轧制规程。构造了等功率裕度、板形良好和防打滑目标负荷,建立了多目标轧制规程目标函数,采用改进的免疫克隆多目标算法对唐山某钢厂冷连轧机进行轧制规程优化计算。试验结果表明,改进的免疫克隆多目标算法能够很快地收敛到Pareto前沿,并且解集的分布性良好;优化后的不同偏好轧制规程组合可以满足不同的选择要求。与原规程相比,各轧机的利用更加合理,提高了轧机的利用效率,改善了带钢板形和表面质量,并且减少了划痕的产生概率。     

基于改进差分进化算法的带钢冷连轧负荷分配

 为了更加合理地制定冷连轧轧制规程,选取打滑因子、等功率裕度和最小能耗作为目标函数,采用改进的差分进化算法求解Pareto前沿,减少了冗余的寻优代数、缩短了优化时间。以求解出的Pareto前沿为选择池,基于不同的偏好因子选择出不同的负荷分配方法,满足不同工况下的要求。仿真结果表明,该算法可以快速均匀地覆盖Pareto前沿,产生偏重于不同目标的负荷分配规则。优化规程与原规程相比,降低了打滑现象出现的概率,减小了轧制功率。     

基于改进混洗蛙跳算法的冷连轧轧制规程优化

 轧制规程优化是使轧制过程达到最佳状态的重要保证。综合考虑影响冷连轧轧制过程的多种因素,以等功率裕度和克服划痕为目标函数,建立了轧制规程多目标优化模型,并采用自适应混沌变异蛙跳算法对轧制规程进行优化设计。该算法是在基本蛙跳算法中,加入自适应混沌变异操作,具有较好的寻优精度和收敛速度。对某钢厂1370mm冷连轧机轧制规程进行优化的结果表明,优化后的规程较好地实现了各机架等功率负荷分配,降低了划痕出现的概率和程度,大大地提高了产品质量,增加了经济效益。     

基于等相对负荷的5机架冷连轧轧制规程优化

针对某企业5机架冷连轧机组第2机架负荷过大而导致生产效率较低的实际情况,以电机功率的等相对负荷为目标函数,采用遗传算法对其前4机架的轧制规程进行了优化.优化后的轧制规程使各机架负荷分配趋于均衡,充分地利用了现有轧机的生产能力,提高了轧制效率.     

适应辊期变化的HC冷连轧机负荷分配智能优化

为提升国内某1 340 mm HC冷连轧机组的带钢板形质量和生产效率,建立了以带钢板形良好、电机功率平衡和预防打滑为优化目标的四机架HC冷连轧机负荷分配多目标优化模型,采用非支配排序遗传算法(NSGA-Ⅱ)获得Pareto最优解集。按轧制状态将工作辊辊期划分为4个阶段,基于各阶段工作辊的热凸度、粗糙度以及控轧需求,合理确定3个优化目标的权重系数,解决目标函数间的冲突关系,从而获得能适应辊期动态变化的轧制规程。仿真表明：优化后的轧制规程有助于改善板形质量、提高生产效率并减小打滑概率。     

基于粒子群算法的冷连轧轧制规程优化设计

选取等相对负荷为目标函数,令轧制力、轧制功率、轧制力矩、速度等参数满足一定约束条件,采用罚函数法,将有约束条件转为无约束条件,采用粒子群算法对目标函数进行优化,求得最优解,这可使各机架功率合理分配,设备能力充分发挥,生产效率提高。     

自适应遗传算法在冷连轧负荷优化中的应用

利用自适应遗传交叉变异策略,设计了一种冷连轧轧制负荷优化方法。以各机架相对压下率为自变量,以各机架轧制力相对均衡为优化目标,建立目标函数。通过与在某1370冷连轧机的负荷优化方法进行比较,仿真结果表明,自适应遗传算法具有搜索速度更快、收敛精度更高等优点,更适合在实际生产中应用。     

基于IAGA的冷连轧机预防打滑的规程优化设计

为解决冷连轧轧制过程中的打滑问题,在引入打滑因子的基础上,建立了以预防打滑为目标的规程优化模型.针对标准遗传算法存在的早熟收敛、振荡和随机性太大等缺点,利用改进的自适应遗传算法进行优化.该算法提出了一种基于排序的多轮轮盘赌选择算子,提高了算子的选优能力,也减少了随机性所产生的误差,同时依据个体适应度的值确定染色体的交叉概率和变异概率,使前期变异明显,后期趋于稳定,保证了种群开发和搜索的平衡及全局收敛性.现场试验及生产实践情况证明,该优化规程模型能够有效地降低打滑发生的概率,提高产品的质量,获得更好的经济效益.     

冷连轧升降速过程板形控制工艺润滑制度优化

 冷连轧机组在带钢升降速过程中,轧制速度会出现频繁的、较大程度的波动,轧制变形区的摩擦因数也会随之发生较大的波动,引起轧制压力来回波动,从而造成升降速阶段的板形相较平稳阶段的板形而言呈现出大幅度变差的问题。工艺制度优化对于摩擦因数引起的板形问题非常有效,因此,首先分析了不同乳化液浓度、初始温度和流量下的带钢在升降速过程中板形的变化过程。针对升降速阶段板形缺陷,采用分段离散法将带钢分别沿横向和纵向分成若干条元,提出升降速过程中板形横向目标函数和纵向目标函数,进而构造出升降速过程中板形动态变化目标函数,实现对轧制过程中板形波动在横向和纵向上的综合控制。由于乳化液浓度和初始温度在轧制过程中无法改变,所以结合板形目标函数,以带钢不发生打滑和热划伤、各机架轧制力不超过限定轧制力为约束条件,提出乳化液浓度和初始温度优化设定函数;乳化液流量优化针对频繁变化的局部浪形缺陷能够起到有效控制,因此乳化液流量一般随轧制速度呈非线性变化,以出口板形波动最小为控制函数,以不发生打滑和热划伤、各机架乳化液总量不超限为约束条件,提出乳化液流量跟随速度优化函数。最后将优化模型应用于国内某钢厂冷连轧机组,根据优化前后轧制力分布、带钢板形云图可知现场应用效果良好。     

基于案例推理的中厚板轧机轧制规程智能优化

为了提高中厚板轧制规程分配的合理性和计算效率,提出利用智能优化技术进行中厚板轧制规程智能优化.该智能技术为混合遗传算法(GA)的改进粒子群算法(PSO)与案例推理算法(CBR)结合运用的一种多目标智能优化技术.采用CBR检索最相似案例,如果案例可以重用,将其作为最终规程进行现场轧制;如果不能重用,将其作为智能优化初始值,通过GAPSO算法围绕目标函数进行全局优化.分析了CBRGAPSO算法的时效性和有效性,证明该算法计算时间更短且优化后的规程分配更合理,更适合中厚板轧制规程优化计算.     

基于多目标遗传算法的工作辊温度场计算与分析

等效换热系数是热连轧机工作辊温度场仿真模型的核心输入参数,多采用遗传算法优化得到,某1800 mm 热连轧机存在品种、规格交替轧制,等效换热系数的准确计算比较困难.选取多组典型工艺条件下的工作辊下机后表面温度作为优化目标,采用多目标遗传算法进行优化,并通过改变遗传算子有效避免了算法早熟及局部收敛等问题,获取了具有较强适应性的等效换热系数.仿真和实测数据的对比结果证明了优化模型的可靠性.利用仿真模型分析了主要工艺参数对工作辊热凸度的影响,并提出同宽交替时,工作辊热凸度随轧制进程呈指数变化,而在品种、规格交替编排轧制工艺下相邻带钢轧制时工作辊热凸度存在6-21.8μm 的波动,且随轧制进程趋于稳定.      

多目标遗传算法在八辊五机架全连续冷连轧机轧制策略优化中的应用

针对八辊五机架全连续冷连轧机在轧制过程中的特点，提出一种多目标优化问题的求解策略——多目标遗传算法，并对其进行改进，提出基于多目标遗传算法的多辊冷连轧机轧制策略的优化。该方法具有计算精度高、速度快等特点，适合在线实时计算的要求。该优化算法在河北中钢1250mm八辊五机架全连续冷连轧机中的应用，证明了该算法的适用性和灵活性，为多辊全连续冷连轧机在计算机过程控制中进行轧制策略优化设计提供了一条新的途径。     

Multi-Objective Optimization for Tandem Cold Rolling Schedule

 Based on rolling feature of continuous tandem cold rolling mill, such as multi-variable, strong coupling, non-linear, analyze and set the equal relative load, preventing slippage and profile well as the multi-objective optimization goal, BP neural network with self-learning function is adopted to replace traditional rolling force models and then Levenberg-Marquardt algorithm is adopted to predict the rolling force. Then use multi-objective fuzzy theory to solve the problem of multi-objective optimization of tandem cold rolling schedule. With the example of 1370mm tandem cold rolling, the rolling schedule of the common rolling, the single-object optimization design and the multi-objective fuzzy optimization design are compared with each other, optimization result shows the proposed optimization method decreases the value of three objective functions simultaneous. The performance of the optimal rolling schedule is satisfying and it is promising.     

Generalized Shape and Gauge Decoupling Load Distribution Optimization Based on IGA for Tandem Cold Mill

 Load distribution is the foundation of shape control and gauge control, in which it is necessary to take into account the shape control ability of TCM (tandem cold mill) for strip shape and gauge quality. First, the objective function of generalized shape and gauge decoupling load distribution optimization was established, which considered the rolling force characteristics of the first and last stands in TCM, the relative power, and the TCM shape control ability. Then, IGA (immune genetic algorithm) was used to accomplish this multi objective load distribution optimization for TCM. After simulation and comparison with the practical load distribution strategy in one tandem cold mill, generalized shape and gauge decoupling load distribution optimization on the basis of IGA approved good ability of optimizing shape control and gauge control simultaneously.     

Multi-Objective Load Distribution Optimization for Hot Strip Mills

Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of objectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better performance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.     

Robust multi-objective optimization of rolling schedule for tandem cold rolling based on evolutionary direction differential evolution algorithm

According to the actual requirements, profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear, roll di-ameter has some uncertainty during the rolling process, ignoring which will cause poor robustness of rolling schedule.In order to solve this problem, a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evo-lutionary direction was proposed.The algorithm calculated the horizontal angle of the vector, which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algo-rithm was verified by two benchmarks.Meanwhile, in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution, a modified Latin Hypercube Sampling process was proposed.Finally, the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the ac-tual rolling schedule.Meanwhile, robustness of solutions was ensured.     

连铸与热轧工序余材集成匹配模型与算法

针对钢铁企业连铸工序余材板坯及热轧工序余材钢卷对热轧生产合同的匹配问题,基于一体化的管理思想建立了余材板坯、钢卷与合同集成匹配的多目标0-1非线性整数规划模型,在模型中引入板坯材质与合同要求钢种特征差异值矩阵及钢卷钢种与合同要求钢种特征差异值矩阵.采用分段整数编码、基于启发式修复策略的改进遗传算法求解.最后,通过对实际生产数据的仿真实验验证了所提模型和算法的有效性,为科学合理地匹配铸轧工序余材板坯、钢卷提供了有效的解决方法.      

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.