基于神经网络的汉默斯坦模型识别实验室规模的批量反应堆
Murugan Balakrishnan1, Vinodha Rajendran1, Shettigar J Prajwal2
1Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalainagar 608 002, Tamil Nadu, India.
ACS omega
|January 15, 2024
概括
本研究介绍了两种神经网络方法,用于识别批量反应堆聚合中的汉默斯坦模型. 这些技术为改进控制器设计提供了高效的非线性系统建模.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 批量反应堆过程,如烯胺聚合,表现出复杂的非线性动态.
- 准确的系统识别对于有效的控制器设计至关重要.
- 传统的汉默斯坦模型识别可以是计算密集的.
研究的目的:
- 开发和比较两个基于神经网络的方法,用于哈默斯坦模型的识别.
- 为了简化控制器设计,有效地识别非线性系统.
- 探索工艺控制中的先进机器学习应用.
主要方法:
- 基于梯度的反向传播算法用于训练多层神经网络.
- 极端学习机器 (ELM) 用于训练代表非线性块的单一隐藏层前网络.
- 使用神经网络权重对哈默斯坦模型块进行直接参数化.
主要成果:
- 这两种神经网络方法都成功地确定了批量反应堆过程的汉默斯坦模型.
- 基于ELM的方法在没有梯度计算的情况下证明了高效的训练.
- 已识别的模型使线性和非线性控制器设计更容易.
结论:
- 基于神经网络的汉默斯坦模型识别为复杂的非线性系统提供了有效的方法.
- ELM方法为参数估计提供了一个计算效率高的替代方案.
- 未来的工作包括实施基于机器学习的非线性模型预测控制.
相关概念视频
Multimachine Stability
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
Bioreactor Design and Operational System
Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
Bioreactor Controls-I
Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly monitored using...
Bioreactor Controls-II
In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the fermentor via a sparger...


