本文選題：超聲波提取 + 建模��； 參考：《江南大學》2017年博士論文

【摘要】：超聲波對天然物(本研究主要指中藥材和食材等天然植物)的有效成分提取是一種效率高、殘留少、無污染的綠色提取技術。近年來,隨著超聲波天然物提取工業(yè)規(guī)模的不斷擴大,迫切需要對提取過程進行先進的優(yōu)化和控制,超聲波天然物提取過程的模型化是過程優(yōu)化及控制的基礎和前提。目前國內外學者對超聲波天然物提取過程的動力學建模做了較深入的研究,然而利用機器學習理論對提取過程進行建模的研究還不夠成熟。另外在天然物提取效率方面,在連續(xù)寬頻帶范圍內針對超聲波天然物提取過程的頻率優(yōu)化問題,尚未有文獻報道。因此,根據超聲波天然物提取過程的實際情況,建立實用的提取過程模型,同時對天然物提取過程的超聲波頻率進行優(yōu)化都具有重要的科學意義和實用價值。鑒于此,論文對超聲波天然物提取過程的動力學和軟測量建模以及超聲波頻率優(yōu)化及其應用等問題進行了較深入地研究,主要工作如下。(1)針對現有的動力學模型未考慮超聲波頻率對模型的影響,以傳質動力學模型和超聲波強化機理為基礎,通過引入超聲波頻率,提出一種改進的超聲波天然物提取過程的動力學模型。進一步,為驗證模型的有效性,通過超聲波甘草酸提取實驗,在獲得最佳提取變量的基礎上,分別建立了關于甘草酸濃度與超聲功率、超聲波頻率和提取溫度的動力學模型,實驗和仿真結果表明,該模型是可行且有效的。(2)針對動力學模型存在的通用性差和可移植性不強問題,以支持向量回歸(SVR)理論為基礎,建立了基于支持向量回歸的超聲波甘草酸提取過程的預測模型,預測結果驗證了其有效性。進一步,針對支持向量回歸模型的訓練時間過長等問題,在分析最小二乘支持向量機(LSSVM)理論的基礎上,構建了最小二乘支持向量機的超聲波甘草酸提取過程的預測模型,并與支持向量回歸模型的預測結果進行了對比分析。(3)針對最小二乘支持向量機建模中參數優(yōu)化問題,通過引入動態(tài)步長調節(jié)因子和混沌優(yōu)化算法,提出了一種混沌動態(tài)步長果蠅優(yōu)化算法(CDSFOA),利用馬爾科夫收斂性分析理論,證明了該算法收斂于全局最優(yōu)解,并通過實例仿真驗證了算法的有效性;基此,利用該算法對模型參數進行優(yōu)化,建立了基于CDSFOA-LSSVM的超聲波甘草酸提取過程的預測模型,通過與支持向量回歸和最小二乘支持向量機模型的預測結果進行對比分析,得到該模型具有更快的訓練速度和更高的預測精度。(4)針對上述模型不能在線預測的問題,以無偏置LSSVM和在線LSSVM為理論,提出了在線無偏置LSSVM算法。進一步,針對多目標輸出問題,在結合在線無偏置LSSVM算法和多輸入多輸出LSSVM算法基礎上,提出了一種多輸入多輸出在線學習無偏置LSSVM算法。為進一步提高該算法的運算速度和精度,通過引入加權因子和利用在線遞推學習方法,對算法的遞推公式進行了改進�；�,為同時預測天然物矛衛(wèi)中的蘆丁和槲皮素兩種有效成分的濃度,建立了超聲波多目標預測模型并得到驗證。(5)針對目前超聲波提取過程存在頻率單一和無法連續(xù)選擇的問題,提出一種超聲波頻率優(yōu)化方法,即先在寬頻帶內對最優(yōu)頻帶進行大范圍的粗搜索,而后在獲得的最優(yōu)窄頻帶內對最優(yōu)頻率進行小范圍的細搜索,通過連續(xù)自動地搜索最優(yōu)超聲波頻率來實現提高超聲波天然物提取效率的目的�；谠擃l率優(yōu)化思想開發(fā)的超聲波提取系統(tǒng),分別對兩種天然物,即食用番茄和中藥材槐米的有效成分進行了提取實驗和頻率優(yōu)化研究,得到了各自的最優(yōu)超聲波頻率并取得了較好的提取效率。
[Abstract]:Ultrasonic extraction of natural materials (natural plants, such as natural plants and natural plants, such as Chinese medicinal materials and materials, etc.) is a green extraction technology with high efficiency, less residue and no pollution. In recent years, with the continuous expansion of the industrial scale of ultrasonic natural extraction, it is urgent to optimize and control the extraction process, and the ultrasonic natural products are urgently needed. The modeling of extraction process is the basis and premise of process optimization and control. At present, scholars at home and abroad have done a lot of research on dynamic modeling of ultrasonic natural extraction process. However, the research on the extraction process by machine learning theory is not mature enough. In addition, in the aspect of natural extraction efficiency, continuous broadband frequency is used. The frequency optimization problem in the range of ultrasonic natural extraction process has not been reported. Therefore, based on the actual conditions of the ultrasonic natural extraction process, a practical extraction process model is set up. At the same time, it has important scientific significance and practical value to optimize the ultrasonic frequency of natural extraction process. This paper studies the dynamics and soft measurement modeling of ultrasonic natural extraction process, the ultrasonic frequency optimization and its application. The main work is as follows. (1) the current dynamic model does not consider the influence of the ultrasonic frequency on the model, which is based on the mass transfer dynamics model and the ultrasonic strengthening mechanism. On the basis of the introduction of ultrasonic frequency, a dynamic model of improved ultrasonic natural extraction process is proposed. Further, in order to verify the validity of the model, the optimum extraction variables are obtained by ultrasonic glycyrrhizic acid extraction experiment, and on the basis of the optimum extraction variables, the content of glycyrrhizic acid and ultrasonic power, ultrasonic frequency and extraction temperature are established respectively. The dynamic model, experimental and simulation results show that the model is feasible and effective. (2) based on the support vector regression (SVR) theory, the prediction model of ultrasonic Glycyrrhiza extraction process based on support vector regression is established, which is based on the theory of support vector regression (SVR). The prediction results verify the model. Furthermore, on the basis of the analysis of least squares support vector machine (LSSVM) theory, the prediction model of ultrasonic Glycyrrhiza extraction process of least squares support vector machine (LS SVM) is constructed on the basis of the long training time of the support vector regression model. The model is compared with the prediction results of the support vector regression model (3). In order to optimize the parameter optimization problem in the least squares support vector machine modeling, a chaotic dynamic step size fruit fly optimization algorithm (CDSFOA) is proposed by introducing the dynamic step length regulation factor and chaos optimization algorithm. The algorithm converges to the global optimal solution by using the Markoff convergence analysis theory, and the algorithm is verified by example simulation. Based on this algorithm, the model parameters are optimized and the prediction model of ultrasonic Glycyrrhiza extraction process based on CDSFOA-LSSVM is established. The model is compared with the prediction results of support vector regression and least square support vector machine model, and the model has faster training speed and higher prediction precision. (4) (4) in view of the problem that the above model can not be predicted online, the online unbiased LSSVM algorithm is proposed with the unbiased LSSVM and online LSSVM as the theory. Based on the online unbiased LSSVM algorithm and the multi input and multi output LSSVM algorithm, a multi input and multi output online learning unbiasing is proposed. In order to further improve the computing speed and accuracy of the algorithm, the recursive formula of the algorithm is improved by introducing the weighting factor and using the online recursive learning method. Based on this, the ultrasonic multi target prediction model is established to predict the concentration of two effective components of rutin and quercetin in natural spear and guard. (5) in view of the problem that the current ultrasonic extraction process has a single frequency and can not be selected continuously, an ultrasonic frequency optimization method is proposed, that is to search the optimal frequency band in a wide range first in the broadband band, and then to search the optimal frequency in a small range in the optimal narrow band, and search continuously and automatically. The optimal ultrasonic frequency is used to improve the efficiency of ultrasonic natural extraction. Based on the ultrasonic extraction system developed by the frequency optimization idea, the effective components of two natural objects, edible tomato and Chinese Sophora japonica, were extracted and the frequency optimization was studied. The optimal ultrasonic frequencies were obtained. Better extraction efficiency was obtained.

【學位授予單位】：江南大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：Q946;TB559

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