基于改进量子粒子群算法的换热网络同步综合及换热流股优化

发布时间：2018-05-15 23:41

本文选题：换热网络 + 量子粒子群算法　；参考：《华东理工大学》2015年硕士论文

【摘要】：自上世纪全球性的能源危机以来,世界各国都尤其重视能源的开发、利用以及对环境的影响。在能源的使用过程中,换热网络结构与工艺过程的匹配程度影响整个系统的能量综合利用程度以及投资操作费用,因而换热网络优化设计、改造与运行对实施工业过程节能降耗具有重要意义。本文基于改进量子粒子群算法对换热网络展开全局优化研究,并以常压塔换热网络为例,对网络中涉及的流股进行局部优化。首先,基于换热网络分级超结构模型,构建了有分流换热网络的数学模型与无分流换热网络的数学模型。基于狼群算法参数设置思想对量子粒子群算法参数设置进行改进研究。对有分流且非等温混合的分级超结构模型,以最小化年综合费用为目标函数,采用双层求解与改进量子粒子群优化算法相结合的策略,分别对外层网络结构参数、内层分流比和换热量等工艺变量进行全局搜索。与文献中典型算例进行比较验证,表明双层改进量子粒子群算法求解有分流换热网络模型问题具有可行性和有效性。进而,提出用单层改进量子粒子群算法综合无分流换热网络,即在生成无分流换热网络结构变量的同时,生成换热负荷变量。不涉及流股分流的同步综合模型比有分流的复杂度与求解时间显著降低。采用三个典型算例对该方法的有效性进行了验证。最后,对换热网络中的流股进行改造研究。采用基于代理模型的全局优化方法来优化常压塔的余热回收过程,在优化迭代过程中用kriging代理模型来代替耗时的精确模型评估,优化了常压蒸馏塔中段回流操作条件,提升高品质能的利用,而且实现了能量优化并且保证了侧线产品之间的分离精度。
[Abstract]:Since the global energy crisis in the last century, countries all over the world have attached great importance to the development, utilization and environmental impact of energy. In the process of energy use, the matching degree between the heat transfer network structure and the technological process affects the comprehensive utilization of energy and the operation cost of the whole system, so the heat transfer network is optimized. The transformation and operation are of great significance to the implementation of energy saving and consumption reduction in industrial processes. Based on the improved Quantum Particle Swarm Optimization (QPSO) algorithm, the global optimization of the heat exchanger network is carried out in this paper. Taking the atmospheric tower heat exchanger network as an example, the local optimization of the flow streams involved in the network is carried out. Firstly, based on the hierarchical superstructure model of heat transfer network, the mathematical model of the heat exchanger network with shunt and the mathematical model of the heat exchanger network without shunt are constructed. The parameter setting of quantum particle swarm optimization (QPSO) is studied based on the parameter setting idea of werewolf swarm optimization algorithm. For the hierarchical superstructure model with shunt and non-isothermal mixing, the objective function is to minimize the annual synthesis cost, and the strategy of two-layer solution combined with the improved quantum particle swarm optimization algorithm is adopted, and the parameters of the outer layer network structure are obtained, respectively. Internal flow ratio and heat transfer process variables such as global search. The comparison with typical examples in literature shows that the two-layer improved quantum particle swarm optimization (QPSO) algorithm is feasible and effective in solving the model of shunt heat transfer network. Furthermore, a single layer improved quantum particle swarm optimization algorithm is proposed to synthesize the shunt free heat transfer network, that is to say, the heat transfer load variable is generated at the same time as the network structure variable without shunt heat transfer. The complexity and solution time of the synchronous synthesis model without shunt is significantly lower than that of the model with shunt. Three typical examples are used to verify the effectiveness of the method. Finally, the modification of heat transfer network is studied. The global optimization method based on agent model is used to optimize the recovery process of waste heat in atmospheric distillation column. In the optimization iteration process, the kriging agent model is used to replace the accurate evaluation of time-consuming model, and the operation conditions of the middle section of atmospheric distillation column are optimized. Improve the use of high-quality energy, and achieve energy optimization and ensure the separation accuracy of side-line products.
【学位授予单位】：华东理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TQ021.8;TP18

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