生物氧化预处理过程中进气量的预测控制研究

发布时间：2018-04-21 00:14

本文选题：生物氧化预处理 + 预测控制　；参考：《新疆大学》2017年硕士论文

【摘要】：在生物氧化预处理过程中进气量是影响细菌和矿浆氧化效率的重要因素,所以,保证氧化槽内一定的进气量对整个生产工艺具有重要意义。受新疆高寒、高海拔地区影响,生物氧化预处理过程中系统呈现出非线性、滞后性等特点,无法实现进气量的实时控制,各级氧化槽普遍采用“宁多勿少”的进气原则,导致氧气利用率较低,造成很大的能源浪费。针对上述问题,对生物氧化预处理过程中进气量研究分析势在必行。本文主要研究工作如下:一、针对工业现场数据中包含随机噪声的回归问题,提出了一种基于机会约束的鲁棒回归算法。通过概率不等式的转化,将机会约束规划转化为二阶锥规划问题,利用成熟的凸优化进行求解。实验结果验证了该算法在处理数据不确定性问题上的优势,同时,为后续提高进气量预测模型的精度奠定基础。二、为了提高氧气利用率,实现氧化槽进气量的实时控制,建立了一个非线性预测控制模型。其中,选用在线支持向量回归作为预测模型,并采用粒子群算法与最速下降原理相结合的算法对目标函数的性能指标进行优化,实现进气量的实时控制。仿真结果表明,所提出的控制模型能够有效地对氧化槽内进气量进行预测和控制,为生物氧化预处理过程中进气量的研究提供新的方法。三、当系统受到外界强烈干扰时,容易造成模型的不确定性,为了保证进气量仍然能够达到实时控制效果,提出了鲁棒模型预测控制策略。通过引入两个离散时间混沌系统的同步控制,确保了模型存在不确定性时两个离散时间系统的同步,使供氧系统达到最优。
[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.
【学位授予单位】：新疆大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TF831;TF18;TP273

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