本文選題：生物氧化預(yù)處理 + 預(yù)測(cè)控制�。� 參考：《新疆大學(xué)》2017年碩士論文

【摘要】：在生物氧化預(yù)處理過程中進(jìn)氣量是影響細(xì)菌和礦漿氧化效率的重要因素,所以,保證氧化槽內(nèi)一定的進(jìn)氣量對(duì)整個(gè)生產(chǎn)工藝具有重要意義。受新疆高寒、高海拔地區(qū)影響,生物氧化預(yù)處理過程中系統(tǒng)呈現(xiàn)出非線性、滯后性等特點(diǎn),無法實(shí)現(xiàn)進(jìn)氣量的實(shí)時(shí)控制,各級(jí)氧化槽普遍采用“寧多勿少”的進(jìn)氣原則,導(dǎo)致氧氣利用率較低,造成很大的能源浪費(fèi)。針對(duì)上述問題,對(duì)生物氧化預(yù)處理過程中進(jìn)氣量研究分析勢(shì)在必行。本文主要研究工作如下:一、針對(duì)工業(yè)現(xiàn)場(chǎng)數(shù)據(jù)中包含隨機(jī)噪聲的回歸問題,提出了一種基于機(jī)會(huì)約束的魯棒回歸算法。通過概率不等式的轉(zhuǎn)化,將機(jī)會(huì)約束規(guī)劃轉(zhuǎn)化為二階錐規(guī)劃問題,利用成熟的凸優(yōu)化進(jìn)行求解。實(shí)驗(yàn)結(jié)果驗(yàn)證了該算法在處理數(shù)據(jù)不確定性問題上的優(yōu)勢(shì),同時(shí),為后續(xù)提高進(jìn)氣量預(yù)測(cè)模型的精度奠定基礎(chǔ)。二、為了提高氧氣利用率,實(shí)現(xiàn)氧化槽進(jìn)氣量的實(shí)時(shí)控制,建立了一個(gè)非線性預(yù)測(cè)控制模型。其中,選用在線支持向量回歸作為預(yù)測(cè)模型,并采用粒子群算法與最速下降原理相結(jié)合的算法對(duì)目標(biāo)函數(shù)的性能指標(biāo)進(jìn)行優(yōu)化,實(shí)現(xiàn)進(jìn)氣量的實(shí)時(shí)控制。仿真結(jié)果表明,所提出的控制模型能夠有效地對(duì)氧化槽內(nèi)進(jìn)氣量進(jìn)行預(yù)測(cè)和控制,為生物氧化預(yù)處理過程中進(jìn)氣量的研究提供新的方法。三、當(dāng)系統(tǒng)受到外界強(qiáng)烈干擾時(shí),容易造成模型的不確定性,為了保證進(jìn)氣量仍然能夠達(dá)到實(shí)時(shí)控制效果,提出了魯棒模型預(yù)測(cè)控制策略。通過引入兩個(gè)離散時(shí)間混沌系統(tǒng)的同步控制,確保了模型存在不確定性時(shí)兩個(gè)離散時(shí)間系統(tǒng)的同步,使供氧系統(tǒng)達(dá)到最優(yōu)。
[Abstract]:The air intake is an important factor affecting the oxidation efficiency of bacteria and slurry during the biological oxidation pretreatment. Therefore, it is of great significance to ensure a certain amount of air intake in the oxidation tank for the whole production process. Under the influence of high altitude and high altitude in Xinjiang, the biological oxidation pretreatment process presents the characteristics of nonlinearity, lag and so on. It is impossible to realize the real-time control of air intake. The air intake principle of "better than less" is generally adopted in oxidation tanks at all levels. Lead to low oxygen utilization rate, resulting in a great waste of energy. In view of the above problems, it is imperative to study and analyze the air intake in the biological oxidation pretreatment process. The main work of this paper is as follows: firstly, a robust regression algorithm based on opportunity constraints is proposed for the regression problem with random noise in industrial field data. Through the transformation of probabilistic inequalities, the opportunity-constrained programming is transformed into a second-order conical programming problem, which is solved by a mature convex optimization. The experimental results demonstrate the superiority of the algorithm in dealing with the uncertainty of the data and lay a foundation for improving the accuracy of the air intake prediction model in the future. Secondly, a nonlinear predictive control model is established to improve the oxygen utilization rate and realize the real-time control of the air intake of the oxidation tank. The on-line support vector regression is chosen as the prediction model, and the particle swarm optimization algorithm combined with the principle of the steepest descent is used to optimize the performance index of the objective function to realize the real-time control of the air intake. The simulation results show that the proposed control model can effectively predict and control the air intake in the oxidation tank and provide a new method for the study of the air intake in the biological oxidation pretreatment process. Thirdly, when the system is strongly disturbed by the outside world, it is easy to cause uncertainty of the model. In order to ensure that the air intake can still achieve real-time control effect, a robust model predictive control strategy is proposed. The synchronization control of two discrete-time chaotic systems is introduced to ensure the synchronization of the two discrete-time systems with uncertainty in the model, and the oxygen supply system is optimized.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TF831;TF18;TP273

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本文編號(hào)：1780074