本文選題：層流冷卻　切入點(diǎn)：遺傳算法　出處：《北京科技大學(xué)》2016年博士論文

【摘要】：在現(xiàn)代鋼鐵工業(yè)中,層流冷卻工藝是通過(guò)軋后強(qiáng)制水冷來(lái)改善帶鋼的組織性能,提高帶鋼質(zhì)量和產(chǎn)量的過(guò)程。帶鋼在層流冷卻過(guò)程中發(fā)生復(fù)雜的水冷、空冷換熱及內(nèi)部的熱傳導(dǎo)過(guò)程,具有工況條件變化劇烈、強(qiáng)非線性、參數(shù)時(shí)變、數(shù)學(xué)模型難以精確描述的復(fù)雜工業(yè)特性,而且整個(gè)冷卻區(qū)的惡劣環(huán)境不能逐點(diǎn)安裝溫度檢測(cè)儀表,帶鋼溫度難以連續(xù)檢測(cè),現(xiàn)有的控制方法存在不能適應(yīng)變化頻繁的工況條件、過(guò)于依賴(lài)帶鋼溫度模型精度的問(wèn)題,導(dǎo)致卷取溫度控制精度不高、對(duì)給定冷卻速率跟蹤效果差。本文以某鋼鐵公司帶鋼熱連軋生產(chǎn)線的層流冷卻過(guò)程為研究對(duì)象,以提高帶鋼成品質(zhì)量為目標(biāo),從溫度預(yù)報(bào)模型優(yōu)化和多目標(biāo)優(yōu)化控制策略研究?jī)煞矫嫒胧?將先進(jìn)控制理論和改進(jìn)的優(yōu)化算法引入到生產(chǎn)實(shí)際中,提出了基于再進(jìn)化遺傳算法的相關(guān)性剪枝法(Re-evolutionary Genetic Algorithm-Correlation Pruning Algorithm,REGA-CPA)優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)卷取溫度預(yù)報(bào)模型和基于轉(zhuǎn)基因多目標(biāo)遺傳算法(Transgenic Multi Objective Genetic Algorithm, TMOGA)的層流冷卻優(yōu)化控制策略,并利用層流冷卻過(guò)程實(shí)際生產(chǎn)數(shù)據(jù)進(jìn)行了仿真實(shí)驗(yàn)研究,仿真結(jié)果驗(yàn)證了所提出溫度預(yù)報(bào)模型的高精度和多目標(biāo)優(yōu)化控制策略的有效性。本文研究工作具體表現(xiàn)在以下幾個(gè)方面：1)再進(jìn)化遺傳算法(REGA)現(xiàn)有諸多改進(jìn)遺傳算法(Genetic Algorithm,GA)終究只是在種群的正常進(jìn)化過(guò)程中所采取各種策略,在設(shè)計(jì)理念上明顯受到自然界生物自然進(jìn)化思想的束縛,對(duì)由于種群進(jìn)化過(guò)程中的盲目性、隨機(jī)性而引起的退化現(xiàn)象明顯應(yīng)對(duì)措施不足,對(duì)克服GA收斂速度慢和易陷于局部最優(yōu)等缺點(diǎn)的效果終究有限�；诖�,本文在進(jìn)化策略上另辟蹊徑,提出了一種基于重新進(jìn)化思想的REGA。其中,首次提出了重新進(jìn)化的思想,用“返祖”操作找回丟失的較優(yōu)模式并將其耦合至下一代種群中,極大的提高了算法的收斂速度；分析了“種群解的空間跨度”和“基因段距離”對(duì)種群多樣性的影響,用“優(yōu)生”操作來(lái)推動(dòng)算法從平面到多維空間的立體式搜索,以勘探和挖掘出更廣、更優(yōu)的尋優(yōu)區(qū)間,并在種群進(jìn)化后期,強(qiáng)力驅(qū)動(dòng)算法收斂于全局最優(yōu).2)基于REGA-CPA優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)卷取溫度預(yù)報(bào)模型本文提出了一種基于REGA-CPA優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)卷取溫度預(yù)報(bào)模型,“階段性跨度淘汰法”主要是從保持種群多樣性方面考慮,隨時(shí)考量整個(gè)種群在平面空間的分布均勻性,以拓展搜索空間,使算法能夠在更廣、更優(yōu)的區(qū)域?qū)?yōu)；“DNA鑒定法”從多維空間來(lái)考量種群在全局空間的尋優(yōu)遍歷性,為判斷任意兩個(gè)個(gè)體在多維空間的距離提供了直觀、高效的方法。仿真結(jié)果表明：該卷取溫度預(yù)報(bào)模型的收斂速度快、精度高,滿足實(shí)時(shí)在線的控制要求,預(yù)報(bào)精度在±10℃范圍之內(nèi),3)“隨機(jī)動(dòng)態(tài)輸入模式”卷取溫度預(yù)報(bào)模型的在線應(yīng)用在離線方式下訓(xùn)練好的基于REGA-CPA優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)卷取溫度預(yù)報(bào)模型為主模型,即可應(yīng)用于在線的卷取溫度預(yù)報(bào)。鑒于層流冷卻系統(tǒng)是一個(gè)強(qiáng)耦合、強(qiáng)非線性、大滯后且滯后的時(shí)間時(shí)變的系統(tǒng),因主模型權(quán)值、閾值、結(jié)構(gòu)已固定,在線預(yù)報(bào)卷取溫度時(shí),若干點(diǎn)的精度有時(shí)可能會(huì)低于離線時(shí)訓(xùn)練的精度。針對(duì)此問(wèn)題,提出了“隨機(jī)動(dòng)態(tài)輸入模式”卷取溫度預(yù)報(bào)模型,以最大限度的保證在線溫度預(yù)報(bào)模型的預(yù)報(bào)精度在±10℃范圍以?xún)?nèi),能為層流冷卻的預(yù)設(shè)定及前饋控制提供可靠的參考數(shù)據(jù),從而為進(jìn)一步提高卷取溫度的控制精度提供了新的途徑。4)轉(zhuǎn)基因多目標(biāo)遺傳算法(TMOGA)提出了TMOGA,利用歷代種群Pareto前沿面的交集來(lái)提取較優(yōu)模式并建立基因庫(kù),庫(kù)中的優(yōu)秀基因通過(guò)“轉(zhuǎn)基因”的方式移植到下一代種群,以保證種群進(jìn)化穩(wěn)步向Pareto最優(yōu)解集迫近；基于決策變量的擁擠距離策略和基因庫(kù)的競(jìng)爭(zhēng)機(jī)制,保持了種群的多樣性,使算法可以挖掘和勘探出更廣、更優(yōu)的搜索空間；隨機(jī)抽取基因的模式保證了歷代種群Pareto前沿面均具有良好的空間分布均勻性；基因庫(kù)的記憶、固化功能形成強(qiáng)力驅(qū)動(dòng)機(jī)制,使算法接近收斂時(shí)迅速跳出局部前沿,快速逼近真實(shí)的Pareto最優(yōu)解集。5)基于TMOGA的層流冷卻系統(tǒng)粗調(diào)區(qū)優(yōu)化控制策略針對(duì)如何提高帶鋼卷取溫度的控制精度和如何準(zhǔn)確跟蹤給定冷卻速率的問(wèn)題,提出了基于TMOGA的層流冷卻系統(tǒng)粗調(diào)區(qū)優(yōu)化控制策略,用于搜索粗調(diào)區(qū)集管的最佳開(kāi)閉模式集合(Pareto最優(yōu)解集)；仿真結(jié)果表明,該多目標(biāo)優(yōu)化控制策略可獲取全局Pareto最優(yōu)解集且在空間分布均勻,所提供的決策變量豐富、合理,因此控制系統(tǒng)的控制范圍廣、精度高,對(duì)多目標(biāo)的均衡能力強(qiáng),從而為新鋼種的開(kāi)發(fā)、冷卻工藝優(yōu)化提供了強(qiáng)有力的技術(shù)手段,同時(shí)為發(fā)展高端、高附加值的帶鋼產(chǎn)品打下了堅(jiān)實(shí)的基礎(chǔ)。
[Abstract]:In modern iron and steel industry, the laminar cooling process is through forced water cooling after rolling to improve the microstructure and properties of steel strip, improve the quality and yield of strip. The occurrence of complex water in the laminar cooling process, air heat transfer and internal heat conduction process, with dramatic changes in working conditions, strong nonlinear, parameter time varying, complex industrial characteristic mathematical model difficult to describe precisely, and the cooling zone of the harsh environment can not point installed temperature measuring instrument, the strip temperature control method to continuous detection, the existing can not adapt to the frequent changes in working conditions, the accuracy of the model is too dependent on the strip temperature problem, cause the volume is not high accuracy temperature control for a given cooling rate, tracking effect is poor. Based on the laminar cooling process of hot strip rolling production line of a Steel Corp as the research object, in order to improve the product quality for the purpose of a strip The subject, starting from the optimal temperature prediction model and multi-objective optimization control strategy based on two aspects, the optimization and improvement of the advanced control theory into the actual production, this paper presents a correlation pruning method evolved based on genetic algorithm (Re-evolutionary Genetic Algorithm-Correlation Pruning Algorithm, REGA-CPA) BP neural network optimization model and prediction of coiling temperature transgenic based on multi-objective genetic algorithm (Transgenic Multi Objective Genetic Algorithm, TMOGA) of the laminar cooling control strategy optimization, and using the laminar cooling process of actual production data are simulated. The simulation results verify that the proposed temperature prediction model with high precision and multi objective optimization of the effectiveness of the control strategy. The specific performance of this research work in the following aspects: 1) and genetic algorithm (REGA) to the much improved genetic algorithm (Ge Netic Algorithm, GA) are only in the normal population evolution process in the various strategies in the design concept was constrained by natural biological evolution thought, due to the blindness of the evolutionary process, the randomness caused by the degradation of inadequate measures, is limited to overcome the slow convergence speed of GA and easy to fall into local optimum effect. Based on this, this article on the evolution strategy and put forward a kind of the re evolution based on REGA. is proposed for the first time to the evolution of thought "is a good method to retrieve the lost progenitor return operation which is coupled to the next generation, great to improve the convergence speed of the algorithm; analyze the impact of" space "population solution and gene segment distance on population diversity, with" eugenics "operation to promote the algorithm from the plane to The three-dimensional space to search, exploration and mining more widely, better searching interval, and the population in the late stage of evolution, strong drive algorithm converges to the global optimal.2) BP optimization REGA-CPA neural network prediction of coiling temperature model this paper proposes a BP REGA-CPA neural network optimization based on coiling temperature based on the prediction model, the stage span elimination method "is mainly considered from the diversity of the population, the population distribution of any consideration in the plane space uniformity, to expand the search space, the algorithm can in a wider area, seeking better;" DNA identification method "from the multidimensional space to consider in population the global space searching ergodicity, for the judgment of any two individual distance in the multidimensional space provide an intuitive and efficient method. The simulation results show that the coiling temperature prediction model has fast convergence speed and high precision, real-time Online control requirements, forecast accuracy within the range of - 10 DEG C, 3) "random dynamic input mode volume online temperature prediction model for application in offline BP optimization REGA-CPA neural network prediction of coiling temperature model based on the model of training, can be applied to the online prediction of coiling temperature in laminar flow. The cooling system is a strong coupling, nonlinear, large delay systems and the lag time variable, because the main model weights, threshold, fixed structure, on-line prediction of coiling temperature, some accuracy may sometimes be below the line from the training precision. Aiming at this problem, put forward the" random dynamic the input mode of coiling temperature prediction model, in order to guarantee the maximum online prediction model for temperature prediction accuracy within the range of - 10 DEG C, can provide reliable reference data for pre setting and feedforward control in laminar cooling, In order to further improve provides a new way for.4 the control accuracy of the coiling temperature) transgenic multi-objective genetic algorithm (TMOGA) proposed by TMOGA, the population Pareto frontier intersection to extract the optimum model and the establishment of gene pool, good genes in the library through the "transgenic" way to transplant to the next generation the population, in order to ensure the evolution of population steadily approaching to the Pareto optimal solution set; decision variable crowding distance strategy and competition mechanism based on gene library, keep population diversity, the algorithm can mining and exploration of a broader, better search space; random gene model to ensure the population were Pareto frontier has a good spatial distribution uniformity; gene library memory, curing function to form a strong driving mechanism, the algorithm is close to convergence quickly jump out of the local frontier, fast approaching the true Pareto optimal solutions. Set.5 TMOGA) coarse area optimization control strategy on how to improve the accuracy of the coiling temperature and how to accurately track the given cooling rate of laminar cooling system based on the proposed coarse region optimization control strategy of laminar cooling system based on TMOGA, search for the coarse set tube opening and closing mode set (the best the Pareto optimal solution set); the simulation results show that the control strategy can obtain the global optimal solution set of Pareto and the spatial distribution of the uniform multi-objective optimization, the decision variables supplied by the rich, reasonable, so the control system wide control range, high precision, strong ability to balance multiple objectives, so as to develop new steel grades. It provides powerful techniques for optimization of cooling process at the same time, the development of high-end, high value-added steel products to lay a solid foundation.

【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TG334.9;TP18

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