【摘要】：化工生產過程,已從過去的單純考慮經(jīng)濟利益最大化單一目標,轉向兼顧生態(tài)環(huán)境、能量消耗等多個目標的評價上,多目標優(yōu)化問題在化工中越來越受到重視。流程模擬軟件的出現(xiàn)為人們模擬實際工業(yè)過程提供了方便,在模擬的基礎上將其與進化算法結合來解決化工過程多目標優(yōu)化問題,成為近些年來各方學者研究的一個熱門課題。但是,因流程模擬軟件存在模型復雜性高、收斂性弱等問題,導致了應用該方法對化工多目標問題進行優(yōu)化求解的時間消耗大且優(yōu)化效率不高。而工程與科學研究領域中常用到的并行算法將一個復雜問題交由多個處理器同時處理,可以在有效提高收斂性的同時,降低運算時間提高效率。并行算法的這些特點為人們解決化工多目標優(yōu)化問題提供了一種高效可行的解決方法。基于對上述問題的考慮,本文對并行算法和多目標優(yōu)化方法進行系統(tǒng)的研究。通過比較各種進化算法的優(yōu)劣,選取NSGA-II作為本文的多目標優(yōu)化研究方法,對其進行并行化改進,提出了一種基于種群的分布式并行遺傳算法。該方法與流程模擬軟件結合優(yōu)化能有效解決化工多目標優(yōu)化中計算速度慢和優(yōu)化效果不理想的問題。在應用經(jīng)典測試函數(shù)驗證該并行算法可靠性的基礎上,將該并行優(yōu)化方法應用到干氣脫硫和富液集中回收系統(tǒng)的多目標優(yōu)化中,在合理的優(yōu)化時間內取得了降低系統(tǒng)能耗、減少排放的優(yōu)化目標。本文的研究主要包括以下內容:(1)學習和總結近幾十年來國內外的優(yōu)秀算法,選取帶精英策略的非支配排序遺傳算法作為研究對象,對其可并行的部分進行研究。(2)通過選取主從式模型,考慮對NSGA-II的適應度評價部分進行并行計算,提出種群分布式并行遺傳算法(PDPGA),并與流程模擬軟件結合對脫硫溶劑再生塔這一簡單實例進行優(yōu)化求解,結果表明該方法能有效提高優(yōu)化效率降低時間消耗。(3)選取適宜的吸收劑對煉廠氣體脫硫及溶劑再生的工業(yè)過程進行研究,并對溶劑集中再生的改進方案進行全流程模擬,通過調節(jié)溶劑MDEA濃度、再生塔回流比和塔頂采出率等操作變量,使用PDPGA與Aspen Plus結合的優(yōu)化方法對上述過程進行了全流程優(yōu)化。(4)對本文的工作進行系統(tǒng)總結,并對下一階段的工作進行了展望。
[Abstract]:In the process of chemical production, the problem of multi-objective optimization has been paid more and more attention in chemical industry, from considering the single objective of maximizing economic benefits to the evaluation of ecological environment, energy consumption and so on. The emergence of process simulation software provides convenience for people to simulate the actual industrial process. Combining it with evolutionary algorithm to solve the multi-objective optimization problem of chemical process has become a hot topic for scholars in recent years. However, due to the high complexity of the model and the weak convergence of the process simulation software, the application of this method to solve the multi-objective chemical problems is time-consuming and inefficient. The parallel algorithm, which is often used in the field of engineering and scientific research, can deal with a complex problem by multiple processors at the same time, which can not only improve convergence but also reduce computational time and improve efficiency. These characteristics of parallel algorithms provide an efficient and feasible method for solving multi-objective optimization problems in chemical engineering. Based on the above problems, parallel algorithms and multi-objective optimization methods are systematically studied in this paper. By comparing the advantages and disadvantages of various evolutionary algorithms, NSGA-II is chosen as the multi-objective optimization research method in this paper, and the parallel genetic algorithm based on population is proposed. The method combined with process simulation software can effectively solve the problems of slow calculation speed and unsatisfactory optimization effect in multi-objective optimization of chemical industry. On the basis of applying the classical test function to verify the reliability of the parallel algorithm, the parallel optimization method is applied to the multi-objective optimization of dry gas desulfurization and concentrated recovery system, and the energy consumption of the system is reduced in a reasonable optimization time. Optimized emission reduction targets. The main contents of this paper are as follows: (1) studying and summarizing the excellent algorithms at home and abroad in recent decades, selecting the non-dominated sorting genetic algorithm with elitist strategy as the research object. The parallelism part is studied. (2) by selecting the master-slave model and considering the parallel computation of the fitness evaluation part of NSGA-II, a population distributed parallel genetic algorithm (PDPGA),) is proposed. A simple example of desulfurization solvent regeneration tower is optimized by combining with process simulation software. The results show that this method can effectively improve the optimization efficiency and reduce the time consumption. (3) the industrial process of gas desulfurization and solvent regeneration in refinery is studied by selecting suitable absorbent, and the whole process simulation of the improved scheme of concentrated solvent regeneration is carried out. By adjusting the operating variables such as concentration of solvent MDEA, reflux ratio of regenerator and recovery rate of tower top, the whole process is optimized by combining PDPGA with Aspen Plus. (4) the work of this paper is systematically summarized. The work of the next stage is prospected.
【學位授予單位】：武漢理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：X742

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