發(fā)布時(shí)間：2018-05-11 02:04

  本文選題：微波加熱 + 溫度場(chǎng)均勻性　； 參考：《重慶大學(xué)》2016年博士論文

【摘要】：面對(duì)日益增加的能量消耗以及嚴(yán)重的環(huán)境污染,節(jié)能減排已成為我國的基本國策,改變現(xiàn)有的以煤、石油為主的化石燃料加熱方式,使用清潔能源刻不容緩。微波能是一種清潔能源,可以通過使用電能的方式產(chǎn)生,可用于眾多工業(yè)熱處理領(lǐng)域。相較于傳統(tǒng)加熱方式,微波能在眾多工業(yè)領(lǐng)域顯露出卓越的節(jié)能省時(shí)特性,受到越來越多研究人員與公司的重視。但微波能應(yīng)用需要解決兩大問題:熱失控與熱不均,熱失控會(huì)導(dǎo)致加熱媒質(zhì)損壞,更嚴(yán)重的情況下會(huì)導(dǎo)致加熱腔體爆炸,而熱不均會(huì)影響最終加熱效果,導(dǎo)致媒質(zhì)不同位置溫度差異很大。本文的研究工作主要針對(duì)工業(yè)微波加熱特點(diǎn),基于微波加熱過程中微波功率分布和媒質(zhì)介電特性等先驗(yàn)性知識(shí),分析微波加熱過程中溫度場(chǎng)非均勻性、媒質(zhì)溫度辨識(shí)、熱點(diǎn)溫度控制以及多目標(biāo)優(yōu)化問題,改善微波加熱媒質(zhì)溫度場(chǎng)非均勻性,實(shí)現(xiàn)熱點(diǎn)溫度控制。增加微波輸入饋口,可以改善加熱媒質(zhì)溫度場(chǎng)均勻性,但現(xiàn)有的研究多關(guān)注于微波源在反應(yīng)腔體外壁饋口位置優(yōu)化選擇,對(duì)加熱過程中微波源輸入功率及相位的主動(dòng)控制實(shí)現(xiàn)溫度場(chǎng)均勻性較少研究。加熱媒質(zhì)在兩個(gè)輸入源下溫升過程是多輸入源的一種典型情況,本文對(duì)兩輸入微波源作用下的加熱媒質(zhì)溫度場(chǎng)均勻性實(shí)現(xiàn)進(jìn)行了分析。通過改變微波源入出功率和相位,可以在媒質(zhì)的任意位置得到希望的功率分布,以此可以得到在時(shí)間維度上均勻的微波功率分布,基于此設(shè)計(jì)了布谷鳥搜索結(jié)合滑模神經(jīng)網(wǎng)絡(luò)的控制算法,對(duì)兩個(gè)微波輸入源的功率和相位進(jìn)行實(shí)時(shí)控制,實(shí)現(xiàn)均勻的溫升過程。同時(shí),考慮實(shí)際情況下,微波源實(shí)際輸入功率和相位與控制算法計(jì)算值存在誤差,對(duì)微波源輸入功率在計(jì)算值100±40%范圍內(nèi)隨機(jī)變化、相位差在計(jì)算值100±20%范圍內(nèi)隨機(jī)變化以及溫度傳感器存在-0.3-0.3 K范圍內(nèi)隨機(jī)變化誤差的情況進(jìn)行了仿真計(jì)算,仿真結(jié)果表明:布谷鳥搜索結(jié)合滑模神經(jīng)網(wǎng)絡(luò)算法可以得到媒質(zhì)溫度場(chǎng)均勻的溫升過程。通過與遺傳算法比較分析可知,布谷鳥搜索算法可以在更短時(shí)間內(nèi)得到更優(yōu)的輸入功率值。在微波加熱過程系統(tǒng)辨識(shí)研究中,一般采用多層前向靜態(tài)神經(jīng)網(wǎng)絡(luò)。但由于微波加熱是時(shí)變系統(tǒng),且一種訓(xùn)練好的模型在媒質(zhì)加熱環(huán)境發(fā)生變化的情況下難以應(yīng)用,因此需要實(shí)時(shí)采樣過程數(shù)據(jù),導(dǎo)致靜態(tài)模型難以準(zhǔn)確描述微波加熱過程。本文提出了一種遞歸自進(jìn)化模糊量子神經(jīng)網(wǎng)絡(luò)模型,用以對(duì)微波常規(guī)加熱與干燥過程進(jìn)行系統(tǒng)辨識(shí),該模型通過實(shí)時(shí)采樣微波加熱過程數(shù)據(jù),實(shí)現(xiàn)神經(jīng)網(wǎng)絡(luò)參數(shù)及結(jié)構(gòu)更新,以得到最佳的辨識(shí)結(jié)果。遞歸自進(jìn)化模糊量子神經(jīng)網(wǎng)絡(luò)將微波輸入功率及先前狀態(tài)信息作為輸入層用以預(yù)測(cè)下一時(shí)刻的狀態(tài)數(shù)據(jù),在溫度辨識(shí)中,辨識(shí)誤差可以控制在1K以內(nèi)。將遞歸自進(jìn)化模糊量子神經(jīng)網(wǎng)絡(luò)應(yīng)用于在動(dòng)態(tài)系統(tǒng)辨識(shí)與混沌模型預(yù)測(cè),通過與現(xiàn)有的耦合局部反饋遞歸自進(jìn)化模糊神經(jīng)網(wǎng)絡(luò)與泛函連接交互式遞歸自進(jìn)化模糊神經(jīng)網(wǎng)絡(luò)比較分析,可以得出該模型在相同的訓(xùn)練周期下,具有更優(yōu)的辨識(shí)能力。在微波加熱過程中,在先驗(yàn)知識(shí)可用與不可用情況下,本文設(shè)計(jì)了兩種不同的控制算法。在先驗(yàn)知識(shí)可用的情況下,提出Lambert定律結(jié)合實(shí)時(shí)溫度信息算法計(jì)算微波功率分布,仿真結(jié)果表明該算法可以得到比Lambert定律準(zhǔn)確性更高的計(jì)算結(jié)果�；诖怂惴�,在過程參數(shù)近似已知情況下,進(jìn)一步提出了模型預(yù)測(cè)控制算法,實(shí)現(xiàn)媒質(zhì)溫度準(zhǔn)確跟蹤預(yù)期軌跡。但更普遍的情況是微波加熱過程中無可用先驗(yàn)知識(shí),在反應(yīng)腔體內(nèi)部,微波功率通常是非均勻性分布,并且該時(shí)變系統(tǒng)過程參數(shù)基本上是未知的�，F(xiàn)有的控制方法有比例積分微分(PID)控制、線性化跟蹤控制、經(jīng)驗(yàn)公式、自適應(yīng)神經(jīng)網(wǎng)絡(luò)模糊控制器等,但這些算法具有如:誤差大、需要系統(tǒng)參數(shù)、泛化能力差、需要大量訓(xùn)練等缺點(diǎn),因此需要研究一種具有更廣應(yīng)用范圍、參數(shù)容易確定、控制精度較高的控制算法。本文提出滑模徑向基神經(jīng)網(wǎng)絡(luò)控制算法對(duì)單微波輸入和微波結(jié)合空氣熱對(duì)流輸入情況下的控制輸入設(shè)計(jì)問題進(jìn)行了分析。針對(duì)加熱過程在相同實(shí)驗(yàn)條件下,可以多次重復(fù)與難以重復(fù)的情況,提出了相應(yīng)的固定學(xué)習(xí)速率控制算法及自適應(yīng)學(xué)習(xí)速率控制算法。在單微波輸入中,該算法在仿真與實(shí)際實(shí)驗(yàn)中,均獲得良好的控制效果,在實(shí)際應(yīng)用中,通過神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)過程,溫度跟蹤誤差可以逐漸收斂到1K以內(nèi)。在微波結(jié)合空氣熱對(duì)流多變量仿真實(shí)驗(yàn)中,該算法可以計(jì)算得出合適的微波功率與熱對(duì)流控制輸入值,保證媒質(zhì)溫度準(zhǔn)確跟蹤預(yù)設(shè)軌跡。在微波加熱過程中,針對(duì)控制目標(biāo),如:溫度、能量利用率、含水率等過程變量,為實(shí)現(xiàn)多目標(biāo)優(yōu)化控制,確定最優(yōu)輸入功率,本文研究了一種針對(duì)微波干燥過程的多目標(biāo)預(yù)測(cè)優(yōu)化算法。根據(jù)微波干燥過程的時(shí)變特性,提出了基于遞歸自進(jìn)化模糊神經(jīng)網(wǎng)絡(luò)的多目標(biāo)預(yù)測(cè)優(yōu)化控制算法。在紅衫木干燥仿真實(shí)驗(yàn)中,選取溫度和含水率作為控制對(duì)象,通過應(yīng)用遞歸自進(jìn)化模糊神經(jīng)網(wǎng)絡(luò)多目標(biāo)預(yù)測(cè)優(yōu)化控制算法,可以實(shí)現(xiàn)對(duì)溫度與含水率的優(yōu)化控制。在實(shí)際實(shí)驗(yàn)中,將褐煤作為干燥媒質(zhì),選取溫度作為被控對(duì)象,該優(yōu)化算法可以將褐煤干燥過程溫度誤差控制在2K以內(nèi)。
[Abstract]:In the face of increasing energy consumption and serious environmental pollution, energy saving and emission reduction has become the basic national policy of our country. It is very urgent to change the existing heating mode of fossil fuel based on coal and oil and use clean energy. Microwave energy is a kind of clean energy. It can be produced by means of electric energy and can be used in many industrial heat treatment. Compared to the traditional heating mode, microwave can show remarkable energy saving and time-saving characteristics in many industrial fields. More and more researchers and companies pay more attention to it. But the application of microwave energy needs to solve two major problems: heat out of control and thermal inhomogeneous, thermal runaway can cause heating medium to damage, more serious cases will cause heating chamber explosion. The research work of this paper mainly focuses on the characteristics of the industrial microwave heating. Based on the prior knowledge of the microwave power distribution and medium dielectric properties, this paper analyzes the non uniformity of the temperature field and the medium temperature identification in the process of microwave heating. Hot temperature control and multi-objective optimization problem can improve the non-uniformity of the temperature field of microwave heating medium and realize the hot temperature control. Increasing the microwave input feed inlet can improve the temperature field uniformity of the heated medium. However, the existing research pays much attention to the optimum selection of the feed position of the microwave source in the outer wall of the reaction cavity and the microwave source in the heating process. The active control of the input power and phase is less homogeneous in the temperature field. The temperature rise process of the heated medium under two input sources is a typical case of the multi input source. In this paper, the uniformity of the temperature field of the heated medium under the action of the two input microwave source is analyzed. The power and phase of the microwave source can be changed by changing the power and phase of the microwave source. In any position of the medium, the desired power distribution is obtained to get the uniform microwave power distribution on the time dimension. Based on this, the control algorithm of the cuckoo search and the sliding mode neural network is designed. The power and phase of the two microwave input sources are controlled in real time to realize the uniform temperature rise process. At the same time, the actual situation is considered. The actual input power and phase of the microwave source have error with the calculated value of the control algorithm. The input power of the microwave source is randomly changed in the range of 100 + 40%, the phase difference is randomly changed within the range of 100 + 20% and the temperature sensor has random variation in the range of -0.3-0.3 K. Simulation and simulation are carried out. The results show that the cuckoo search combined with the sliding mode neural network algorithm can get the uniform temperature rise process of the medium temperature field. By comparing with the genetic algorithm, it can be seen that the cuckoo search algorithm can get better input power value in a shorter time. In the study of system identification of microwave heating process, the multi-layer forward static God is generally adopted. But because the microwave heating is a time-varying system, and a good training model is difficult to be applied to the medium heating environment, so it is necessary to sample the process data in real time, which causes the static model to not accurately describe the microwave heating process. In this paper, a recursive self evolution fuzzy neural network model is proposed. The conventional microwave heating and drying process is systematically identified. By sampling the data of the microwave heating process in real time, the model realizes the neural network parameters and the updating of the structure to obtain the best identification results. The recursive self Evolving Fuzzy quantum neural network uses the microwave input power and the previous state information as the input layer to predict the next time. In the temperature identification, the identification error can be controlled within 1K. The recursive self evolving fuzzy neural network is applied to the dynamic system identification and the chaotic model prediction, and is compared with the existing coupled local feedback recursive self evolving fuzzy neural network and functional connection interactive recursive fuzzy neural network. It can be concluded that the model has better identification ability under the same training period. In the microwave heating process, two different control algorithms are designed in the case of prior knowledge availability and unavailability. In the case of prior knowledge, the Lambert law and the real-time temperature information algorithm are proposed to calculate the microwave power points. The simulation results show that the algorithm can obtain more accurate results than the Lambert law. Based on this algorithm, the model predictive control algorithm is further proposed to achieve accurate tracking of the expected trajectory under the approximate known process parameters, but the more common situation is that no prior knowledge is available in the microwave heating process. In the reaction chamber, the microwave power is usually nonuniform, and the parameters of the time-varying system are basically unknown. The existing control methods are proportional integral differential (PID) control, linearized tracking control, empirical formula, adaptive neural network fuzzy controller and so on, but these algorithms have such advantages as large error and need system parameters. This paper proposes a sliding mode radial basis neural network control algorithm for the control input design problem of single microwave input and microwave combined with air heat convection input. The corresponding fixed learning rate control algorithm and adaptive learning rate control algorithm are proposed for the heating process, which can be repeated and difficult to repeat under the same experimental conditions. In the single microwave input, the algorithm has good control effect in both simulation and practical experiments. In practical application, the neural network is used in the actual application. In the learning process of the collaterals, the temperature tracking error can be gradually converged to less than 1K. In the multi variable simulation experiment of microwave combined with air heat convection, the algorithm can calculate the appropriate input values of microwave power and heat convection control, and ensure that the temperature of the medium is accurately tracked by the preset trajectory. In the process of micro wave heating, the control target, such as temperature, can be used in the process of micro wave heating. In order to achieve multi-objective optimization control and determine the optimal input power, a multi-objective optimization algorithm for microwave drying process is studied in this paper. Based on the time-varying characteristics of the microwave drying process, a multi target predictive optimal control algorithm based on the recursive self evolution model paste neural network is proposed. In the drying simulation experiment of red shirt and wood, the temperature and water content are selected as the control object. The optimization control of temperature and water content can be realized by using the recursive self evolving fuzzy neural network multi target predictive optimization control algorithm. In the actual experiment, the lignite is used as the drying medium and the temperature is selected as the controlled object, and the optimization algorithm can be obtained. The temperature error of lignite drying process is controlled within 2K.

【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN015;TP183

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