本文選題：生化系統(tǒng) + 穩(wěn)態(tài)優(yōu)化��； 參考：《渤海大學(xué)》2017年碩士論文

【摘要】：生物技術(shù)正處于迅速發(fā)展的時(shí)期,生化系統(tǒng)工程的地位也越來(lái)越重要了。但是由于生化過(guò)程具有非線性、時(shí)變性和不確定性等特點(diǎn),所以學(xué)者們很難對(duì)它進(jìn)行優(yōu)化和控制。此外,在現(xiàn)實(shí)生活生產(chǎn)中,需要考慮的目標(biāo)往往不僅僅是一個(gè)而是兩個(gè)甚至更多。并且各目標(biāo)之間往往是相互沖突、相互制約的。因此,怎樣提高原材料的轉(zhuǎn)化率和目的產(chǎn)物的產(chǎn)率進(jìn)而提高整個(gè)生化行業(yè)的生產(chǎn)水平,是研究人員對(duì)生化系統(tǒng)研究的重中之重。本文研究了生化系統(tǒng)的穩(wěn)態(tài)優(yōu)化問(wèn)題。論文研究的主要內(nèi)容和取得的結(jié)果如下:1.針對(duì)生化系統(tǒng)的多目標(biāo)穩(wěn)態(tài)優(yōu)化問(wèn)題,在S-系統(tǒng)框架下提出了一種可求其最優(yōu)解的新方法。首先把生化系統(tǒng)的常微分方程模型改寫成S-系統(tǒng)形式,從而得到生化系統(tǒng)多目標(biāo)穩(wěn)態(tài)優(yōu)化問(wèn)題的S-系統(tǒng)形式;然后在對(duì)數(shù)空間下將得到的多目標(biāo)非線性優(yōu)化問(wèn)題轉(zhuǎn)化為多目標(biāo)線性規(guī)劃問(wèn)題;再基于NBI(Normal Boundary Intersection)方法把多目標(biāo)線性規(guī)劃問(wèn)題轉(zhuǎn)化為一系列單目標(biāo)線性優(yōu)化問(wèn)題來(lái)求解。與加權(quán)和及遺傳算法相比,本文方法計(jì)算結(jié)果可以獲得分布更加均勻的Pareto最優(yōu)解。2.針對(duì)一類生化系統(tǒng)的穩(wěn)態(tài)優(yōu)化問(wèn)題,本文在GMA(Generalized Mass Action)系統(tǒng)框架下,提出了一種雙層規(guī)劃優(yōu)化模型,其外層優(yōu)化問(wèn)題的目標(biāo)函數(shù)為通量的最大化;其內(nèi)層優(yōu)化問(wèn)題的目標(biāo)函數(shù)為生化系統(tǒng)代謝成本的最小化。為了有效求解所提出的雙層規(guī)劃模型,首先將外層和內(nèi)層目標(biāo)函數(shù)表示為冪函數(shù)形式,然后應(yīng)用對(duì)數(shù)變換將雙層規(guī)劃問(wèn)題轉(zhuǎn)化為相對(duì)簡(jiǎn)單的新問(wèn)題,最后應(yīng)用線性規(guī)劃的對(duì)偶理論將得到的新雙層問(wèn)題轉(zhuǎn)化為單層非線性優(yōu)化問(wèn)題,并對(duì)其進(jìn)行求解。計(jì)算研究表明,本文方法得到的計(jì)算結(jié)果比已有方法更具實(shí)際意義。
[Abstract]:Biotechnology is in a period of rapid development, and the status of biochemical systems engineering is becoming more and more important. However, because biochemical processes are nonlinear, time-varying and uncertain, it is difficult for scholars to optimize and control them. Moreover, in real life production, the goal to consider is often not just one but two or more. And the goals often conflict and restrict each other. Therefore, how to improve the conversion rate of raw materials and the yield of target products, and then improve the production level of the whole biochemical industry, is the most important research on biochemical system. In this paper, the steady-state optimization of biochemical systems is studied. The main contents and results of this paper are as follows: 1. A new method for solving the multiobjective steady-state optimization problem of biochemical systems is proposed under the framework of S- system. Firstly, the ordinary differential equation model of biochemical system is rewritten into the form of S- system, and the S- system form of multiobjective steady-state optimization problem of biochemical system is obtained. Then the multi-objective nonlinear optimization problem is transformed into a multi-objective linear programming problem in logarithmic space, and then the multi-objective linear programming problem is transformed into a series of single-objective linear optimization problems based on NBI(Normal Boundary intersection. Compared with weighted sum and genetic algorithm, the proposed method can obtain a more uniform Pareto optimal solution. 2. For a class of steady state optimization problems of biochemical systems, a bilevel programming optimization model is proposed under the framework of GMA(Generalized Mass action system. The objective function of the outer optimization problem is flux maximization. The objective function of the inner optimization problem is to minimize the metabolic cost of biochemical systems. In order to solve the bilevel programming model effectively, the outer and inner objective functions are first expressed as power functions, and then the bilevel programming problem is transformed into a relatively simple new problem by logarithmic transformation. Finally, the dual theory of linear programming is applied to transform the new bilevel problem into a single-layer nonlinear optimization problem and solve it. The computational results show that the results obtained by this method are more practical than those obtained by the existing methods.
【學(xué)位授予單位】：渤海大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O221

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