基于分级超结构的换热网络同步综合与改造方法研究
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摘要
能源供应日趋紧张是当今全球面临的巨大挑战,提高能源利用效率、发展低碳经济已经在世界范围内达成共识。在此背景下,以提高能量系统集成水平和能量回收最大化为目标的换热网络综合与改造问题已成为过程系统能量集成领域研究的热点。其中换热网络综合方法的研究起步较早,众多学者和工程技术人员投入了大量的研究精力,在学术上和工程应用方面均取得了重大进展。然而换热网络综合本质上是涉及庞大优化变量和约束条件的数学优化问题,现有换热网络综合方法在求解中等、大型换热网络综合问题时的能力仍有不足,相关研究还有待完善。另一方面,过程工业中常常需要对已经存在的换热网络系统进行改造以实现节能降耗,但是针对换热网络优化改造方法的研究相对较少,近年来这方面的研究工作正逐渐引起人们的重视。针对这两个研究方向存在的问题,本文进行了以下的研究工作：
1.本文基于无分流分级超结构、有分流分级超结构和混合分级超结构分别建立了以年度总综合费用最小为目标函数的换热网络同步综合数学模型。其中,混合分级超结构将超结构的所有级划分为“有分流”级和“无分流级”,从而在数学建模之前可以预先在分级超结构的完整性和数学模型的求解难度之间进行权衡。建立在此基础上的数学模型改进了等温混合的不合理假设,使得换热网络同步综合数学模型更加严格,更加符合工程实际,但同时在模型中增加了非线性等式约束条件,该模型的全局最优化求解将更有挑战性。
2.换热网络同步综合数学模型是一个复杂的混合整数非线性规划问题(MINLP)该问题具有不连续、非线性、非凸和多维等特点,属于最难于求解的NP-hard问题。求解此类优化问题的全局优化算法主要分为确定性算法和随机算法两大类,其中随机算法由于不需要利用优化函数的解析性质,在处理规模庞大的MINLP时相比确定算法显示出了较好的性能,在全局最优搜索过程中不易陷入局部极值陷阱。因此,随机性算法在换热网络同步综合领域具有非常高的研究价值和应用前景。本文针对非线性规划问题(NLP),建立了一种辅以遗传算法(GA)的改进粒子群算法(PSO),并应用于多个经典算例以验证混合优化算法的有效性。
3.基于本文提出的PSO/GA混合优化算法,建立双层优化策略用于换热网络同步综合数学模型的求解。双层优化策略能够有效处理MINLP (?)司题中离散与连续优化变量之间的层次性和关联性。上层优化是考虑换热匹配选择的离散组合优化问题,采用遗传算法能够非常有效的处理离散优化问题,寻找到较优的网络结构,从而避免计算大量下层优化问题；下层优化是在换热网络结构给定情况下,以年度最小总综合费用为目标函数确定最佳操作参数的NLP优化问题,本文将初始可行解产生策略、不可行解修复方法和PSO/GA混合优化算法成功地相结合应用于下层优化问题求解。5个不同规模的换热网络综合问题求解证明了本文提出的双层优化策略非常有效。
4.建立了基于混合分级超结构的换热网络同步优化改造数学模型。该模型不依赖于夹点约束,不需要预先给定最小传热温差,能够有效权衡改造投资费用与运行费用之间的关系。改造投资费用中考虑了现有换热器的重新配置费用、现有换热器新增换热面积费用和新增换热器费用,符合工程实际要求。换热网络同步优化改造数学模型也是一个复杂的MINLP模型,相比同步综合数学模型,优化变量和约束条件均有所增加。本文提出的双层优化策略同样适用于求解改造问题数学模型。4个不同规模的换热网络改造问题用于验证本文提出方法的有效性。
The energy supply situation is growing increasingly intense, which has been the great challenge to countries all over the world. Throughout the world, people have indentified the importance of higher energy efficiency and the effect of low carbon development. Under this background, the synthesis and retrofit of heat exchanger networks (HENs) have attracted significant research in the field of process systems engineering study. Numerous investigations on theory and application have been carried out to improve the performance of heat exchanger network synthesis (HENS). However, in most cases the industrial HENS problems are large scale optimization problems, the computation complexity of which promotes further perfection to the current optimization methods. In recent years the retrofit of existing HENs has become important due to the large savings of the utility consumption. But only a few methods for HENs retrofit design have been proposed. This paper has completed the following research work to solve the problems of the synthesis and retrofit method.
1. Based on the concept of hybrid stage-wise superstructure, a modified simultaneous synthesis model of heat exchanger network is proposed. In the proposed superstructure, the candidate networks featuring splits and series structures are considered simultaneously, which can control the solution performance and search space efficiently. The novel model removes the assumption of isothermal mixing in literature and is more suitable for engineering practice. However, the model is more complicated to solve due to additional optimization variables and nonlinear constraints.
2. The simultaneous synthesis of HEN model is usually formulated as a complex mixed integer nonlinear programming (MINLP) problem with a great number of optimization variables and constraints, which belongs to a particular difficult class of NP-hard optimization problems. Compared with deterministic methods, stochastic algorithms do not depend on gradients and work on function evaluation alone, and is therefore more suitable for dealing with complex MINLP model. In this work, a novel hybrid algorithm (PSO/GA) is developed to sovle nonlinear programming problems. Some benchmarks are presented to show the efficiency of the proposed algorithm.
3. In order to avoid trapping into local optimum prematurely, a two-level method is proposed for optimizing HENS problems. The proposed two-level method is supposed to consider levels and associations between discrete and continuous variables, and can evolve adequately in continuous search space since discrete variables evolve in the same pace. In the upper level of the presented method, GA is used for structural optimization, since it is more suitable for the non-continuous optimization problem than other stochastic algorithms. The information of structures is sent to the lower level, in which, the process parameters such as stream-split flows and heat loads of heat exchangers are optimized by the proposed hybrid algorithm (PSO/GA). This algorithm is demonstrated to be a powerful tool for solving continuous and unconstrained optimization problems. Five case studies are presented to show the applicability of the proposed methodology.
4. The hybrid stage-wise model for new designs is extended for Simultaneous retrofits of HEN. The presented model does not rely on pinch analysis, and does not require predetermining minimum heat transfer temperature difference. Through solving this model, investment costs in retrofit and operating costs can be optimized simultaneously. The investment costs in retrofit include reassignment cost of existing exchangers, cost of area for existing exchangers and cost of area for new exchangers. This model is a MINLP problem with more optimization variables and constraints, which can be solved efficiently through the proposed two-level method. Four case studies are presented to show the applicability of the proposed methodology.
1.本文基于无分流分级超结构、有分流分级超结构和混合分级超结构分别建立了以年度总综合费用最小为目标函数的换热网络同步综合数学模型。其中,混合分级超结构将超结构的所有级划分为“有分流”级和“无分流级”,从而在数学建模之前可以预先在分级超结构的完整性和数学模型的求解难度之间进行权衡。建立在此基础上的数学模型改进了等温混合的不合理假设,使得换热网络同步综合数学模型更加严格,更加符合工程实际,但同时在模型中增加了非线性等式约束条件,该模型的全局最优化求解将更有挑战性。
2.换热网络同步综合数学模型是一个复杂的混合整数非线性规划问题(MINLP)该问题具有不连续、非线性、非凸和多维等特点,属于最难于求解的NP-hard问题。求解此类优化问题的全局优化算法主要分为确定性算法和随机算法两大类,其中随机算法由于不需要利用优化函数的解析性质,在处理规模庞大的MINLP时相比确定算法显示出了较好的性能,在全局最优搜索过程中不易陷入局部极值陷阱。因此,随机性算法在换热网络同步综合领域具有非常高的研究价值和应用前景。本文针对非线性规划问题(NLP),建立了一种辅以遗传算法(GA)的改进粒子群算法(PSO),并应用于多个经典算例以验证混合优化算法的有效性。
3.基于本文提出的PSO/GA混合优化算法,建立双层优化策略用于换热网络同步综合数学模型的求解。双层优化策略能够有效处理MINLP (?)司题中离散与连续优化变量之间的层次性和关联性。上层优化是考虑换热匹配选择的离散组合优化问题,采用遗传算法能够非常有效的处理离散优化问题,寻找到较优的网络结构,从而避免计算大量下层优化问题；下层优化是在换热网络结构给定情况下,以年度最小总综合费用为目标函数确定最佳操作参数的NLP优化问题,本文将初始可行解产生策略、不可行解修复方法和PSO/GA混合优化算法成功地相结合应用于下层优化问题求解。5个不同规模的换热网络综合问题求解证明了本文提出的双层优化策略非常有效。
4.建立了基于混合分级超结构的换热网络同步优化改造数学模型。该模型不依赖于夹点约束,不需要预先给定最小传热温差,能够有效权衡改造投资费用与运行费用之间的关系。改造投资费用中考虑了现有换热器的重新配置费用、现有换热器新增换热面积费用和新增换热器费用,符合工程实际要求。换热网络同步优化改造数学模型也是一个复杂的MINLP模型,相比同步综合数学模型,优化变量和约束条件均有所增加。本文提出的双层优化策略同样适用于求解改造问题数学模型。4个不同规模的换热网络改造问题用于验证本文提出方法的有效性。
The energy supply situation is growing increasingly intense, which has been the great challenge to countries all over the world. Throughout the world, people have indentified the importance of higher energy efficiency and the effect of low carbon development. Under this background, the synthesis and retrofit of heat exchanger networks (HENs) have attracted significant research in the field of process systems engineering study. Numerous investigations on theory and application have been carried out to improve the performance of heat exchanger network synthesis (HENS). However, in most cases the industrial HENS problems are large scale optimization problems, the computation complexity of which promotes further perfection to the current optimization methods. In recent years the retrofit of existing HENs has become important due to the large savings of the utility consumption. But only a few methods for HENs retrofit design have been proposed. This paper has completed the following research work to solve the problems of the synthesis and retrofit method.
1. Based on the concept of hybrid stage-wise superstructure, a modified simultaneous synthesis model of heat exchanger network is proposed. In the proposed superstructure, the candidate networks featuring splits and series structures are considered simultaneously, which can control the solution performance and search space efficiently. The novel model removes the assumption of isothermal mixing in literature and is more suitable for engineering practice. However, the model is more complicated to solve due to additional optimization variables and nonlinear constraints.
2. The simultaneous synthesis of HEN model is usually formulated as a complex mixed integer nonlinear programming (MINLP) problem with a great number of optimization variables and constraints, which belongs to a particular difficult class of NP-hard optimization problems. Compared with deterministic methods, stochastic algorithms do not depend on gradients and work on function evaluation alone, and is therefore more suitable for dealing with complex MINLP model. In this work, a novel hybrid algorithm (PSO/GA) is developed to sovle nonlinear programming problems. Some benchmarks are presented to show the efficiency of the proposed algorithm.
3. In order to avoid trapping into local optimum prematurely, a two-level method is proposed for optimizing HENS problems. The proposed two-level method is supposed to consider levels and associations between discrete and continuous variables, and can evolve adequately in continuous search space since discrete variables evolve in the same pace. In the upper level of the presented method, GA is used for structural optimization, since it is more suitable for the non-continuous optimization problem than other stochastic algorithms. The information of structures is sent to the lower level, in which, the process parameters such as stream-split flows and heat loads of heat exchangers are optimized by the proposed hybrid algorithm (PSO/GA). This algorithm is demonstrated to be a powerful tool for solving continuous and unconstrained optimization problems. Five case studies are presented to show the applicability of the proposed methodology.
4. The hybrid stage-wise model for new designs is extended for Simultaneous retrofits of HEN. The presented model does not rely on pinch analysis, and does not require predetermining minimum heat transfer temperature difference. Through solving this model, investment costs in retrofit and operating costs can be optimized simultaneously. The investment costs in retrofit include reassignment cost of existing exchangers, cost of area for existing exchangers and cost of area for new exchangers. This model is a MINLP problem with more optimization variables and constraints, which can be solved efficiently through the proposed two-level method. Four case studies are presented to show the applicability of the proposed methodology.
引文
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