耦合有机朗肯循环的换热网络全局优化  

Global Optimization of Heat Exchanger Networks Integrated with Organic Rankine Cycle

作　　者：杨路 AKRAM Naeem 常承林 莫文龙[3,4] 申威峰 YANG Lu;AKRAM Naeem;CHANG Chenglin;MO Wenlong;SHEN Weifeng(School of Chemistry and Chemical Engineering,National-Municipal Joint Engineering Laboratory for Chemical Process Intensification and Reaction,Chongqing University,Chongqing 401331,China;School of Chemical Engineering,Minhaj University Lahore,Lahore 54000,Punjab,Pakistan;State Key Laboratory of Carbon-based Energy Resource Chemistry and Utilization,Xinjiang Key Laboratory of Clean Coal Transformation and Chemical Process,College of Chemical Engineering,Xinjiang University,Urumqi 830017,China;Xinjiang Yihua Chemical Company Limited,Changji 831100,Xinjiang,China)

机构地区：[1]重庆大学化学化工学院,化工过程强化与反应国家地方联合工程实验室,重庆401331 [2]明哈吉大学拉合尔分校化工学院,巴基斯坦旁遮普拉合尔54000 [3]新疆大学化工学院,省部共建碳基能源资源化学与利用国家重点实验室,煤炭清洁转化与化工过程新疆维吾尔自治区重点实验室,乌鲁木齐830017 [4]新疆宜化化工有限公司,新疆昌吉831100

出　　处：《华东理工大学学报（自然科学版）》2024年第5期668-677,共10页Journal of East China University of Science and Technology

基　　金：中央高校基本科研业务费(2024CDJXY010);新疆维吾尔自治区区域协同创新专项科技援疆计划(2024E02036);重庆市技术创新与应用发展重点专项项目(2024TIAD-KPX0168);重庆市出站留(来)渝博士后择优资助(Z20240373);国家自然科学基金优秀青年科学基金(22122802);国家自然科学基金(22278044);重庆市自然科学基金杰出青年科学基金(CSTB 2022NSCQ-JQX0021)。

摘　　要：针对换热网络与有机朗肯循环耦合集成优化问题,建立了严谨的混合整数非线性规划(MINLP)模型。同时,通过添加与物流匹配的关键约束,剔除掉重复性的网络结构,开发了一种新型的枚举算法,将复杂的MINLP模型分解为混合整数线性规划(MILP)和非线性规划(NLP)两个子模型。迭代运行MILP模型,枚举出所有可行的网络结构;再利用全局求解器BARON优化每一个网络结构所对应的NLP模型,求得固定结构的年度总费用;最后对比所有网络结构的年度总费用,筛选出全局最优的设计方案。案例分析结果表明,该算法仅需16 s就能收敛至全局最优解,与文献相比,年度总费用降低33.4%,且所提出的网络结构约束能使重复性的网络结构数量减少81.25%,从而提高算法的优化求解效率。A rigorous mixed integer nonlinear programming(MINLP)model is developed to solve the integrated optimization problem of a heat exchange network integrated with the organic Rankine cycle.At the same time,a new enumeration algorithm is developed to decompose the complex MINLP model into mixed integer linear programming(MILP)and nonlinear programming(NLP)sub-models by adding the key constraints of stream matching and eliminating the repetitive network structure.Within this algorithmic framework,the first step involves iteratively solving the MILP model to enumerate all feasible network structures.Subsequently,for each network structure,a global solver(BARON)is employed to optimize the NLP model and determine the total annual cost(TAC)of that specific structure.Finally,through a comparison of the TACs of all network structures,the globally optimal design solution is determined.The case study demonstrates that,the proposed algorithm can converge to the global optimal solution in only 16 s,and the proposed network structure constraints can reduce the number of repetitive network structures by 81.25%,thus improving the optimization efficiency of the algorithm.

关 键 词：换热网络 全局优化 能量回收 超结构 混合整数非线性规划 

分 类 号：TQ021.8[化学工程]