Parameter Identification of SSF Process with Non-Linear Adaptive PSO Algorithm

doi:10.7652/xjtuxb201307023

您当前的位置：

首页 >

文章列表页 >

Parameter Identification of SSF Process with Non-Linear Adaptive PSO Algorithm

更新时间：2025-07-09

- Parameter Identification of SSF Process with Non-Linear Adaptive PSO Algorithm
- Vol. 47, Issue 7, Pages: 124-128(2013)
- 作者机构：
  
  东南大学能源热转换及其过程测控教育部重点实验室,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201307023
  CLC： X705
- Online First：10 July 2013，
  
  Published：2013
- 稿件说明：
移动端阅览
Parameter Identification of SSF Process with Non-Linear Adaptive PSO Algorithm[J]. 2013, 47(7): 124-128.
DOI：

Parameter Identification of SSF Process with Non-Linear Adaptive PSO Algorithm[J]. 2013, 47(7): 124-128. DOI： 10.7652/xjtuxb201307023.

摘要

为进一步优化同步糖化发酵(SSF)工艺

在经典发酵动力学的基础上

总结出SSF工艺中的还原糖变化方程

并采用自适应粒子群优化(PSO)算法进行菌体生长、产物生成以及还原糖消耗的模型参数辨识。通过比较分析线性和非线性动态变化惯性权重的自适应PSO算法在动力学参数辨识过程中的优劣

确定了非线性方法的快速收敛特性。结果表明:模型的拟合值与实验数据比较接近

即利用这些模型来反映此SSF过程的机理具有一定的准确性和可靠性; 通过非线性动态变化惯性权重的自适应PSO算法进行参数辨识具有一定的可行性和推广性

也为模型参数辨识提供了一种新思路。

Abstract

To further optimize the simultaneous saccharification and fermentation(SSF)process

the adaptive particle swarm optimization(PSO)algorithm was applied to the parameter identification for the dynamic models of cell growth

product synthesis and sugar consumption. The model equation of reducing sugar consumption was presented based on the kinetics of fermentation. The adaptive PSO algorithm with non-linear changed inertia weight was found to be more effective in convergence compared with the linear method. The results show that the values simulated fit the experimental data well

indicating specific accuracy and feasibility of the employed models in reflecting the mechanisms of the SSF technology. It appears that the present adaptive PSO method with non-linear changed inertia weight may effectively be used in the model parameter estimation.

关键词

Keywords

references

邓雪. 玉米秸秆水解成糖发酵乙醇 [D]. 长春: 长春工业大学, 2010.

LIU L, SUN J, ZHANG D X, et al. Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantum-behaved particle swarm optimization algorithm [J]. Enzyme and Microbial Technology, 2009, 44(1): 24-32.

SKOLPAP W, NUCHPRAYOON S, SCHARER J M, et al. Fed-batch optimization of alpha-amylase and protease-producing bacillus subtilis using genetic algorithm and particle swarm optimization [J]. Chemical Engineering Science, 2008, 63(16): 4090-4099.

许久峰, 苗国立, 聂金荣, 等. 基于自适应权重PSO的模型参数估计算法 [J]. 河南工程学院学报: 自然科学版, 2010, 22(3): 68-72.

XU Jiufeng, MIAO Guoli, NIE Jinrong, et al. An algorithm of model parameter estimation based on adaptive weight PSO [J]. Journal of Henan Institute of Engineering: Natural Science Edition, 2010, 22(3): 68-72.

周驰. 粒子群优化算法应用研究 [D]. 武汉: 华中科技大学, 2007.

GARLAPATI V K, VUNDAVILLI P R, BANERJEE R. Evaluation of lipase production by genetic algorithm and particle swarm optimization and their comparative study [J]. Appl Biochem Biotechnol, 2010, 162(5): 1350-1361.

代志凯, 印遇龙, 阮征. 微生物发酵动力学模型及其参数拟合的软件实现 [J]. 计算机与应用化学, 2011, 28(4): 437-440.

DAI Zhikai, YIN Yulong, RUAN Zheng. Microbial fermentation kinetic model and its parameters estimation by software [J]. Computers and Applied Chemistry, 2011, 28(4): 437-440.

吕振肃, 侯志荣. 自适应变异的粒子群优化算法 [J]. 电子学报, 2004, 3(3): 416-420.

LÜ Zhensu, HOU Zhirong. Particle swarm optimization with adaptive mutation [J]. Acta Electronica Sinica, 2004, 3(3): 416-420.

赖智乐. 汽爆玉米秸秆同步糖化发酵产乙醇的工艺研究 [D]. 郑州: 郑州大学, 2010.

于雷, 裴晓林, 雷霆. L-乳酸生产菌分批发酵动力学模型 [J]. 食品工业科技, 2008, 29(5): 100-102.

YU Lei, PEI Xiaolin, LEI Ting. Dynamic models for L-lactic acid batch fermentation by Lactobacillus rhamnosus [J]. Science and Technology of Food Industry, 2008, 29(5): 100-102.

余洪波, 张晓昱, 柯静, 等. 生物-碱氧化预处理玉米秸秆酶解条件的优化 [J]. 农业工程学报, 2009, 25(4): 201-205.

YU Hongbo, ZHANG Xiaoyu, KE Jing, et al. Optimization of enzymatic hydrolysis of corn straw after biological-alkaline/oxidative pretreatment [J]. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(4): 201-205.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on the prediction method of winding temperature rise of oil-immersed distribution transformer based on the identification of thermal circuit model parameters

A System Identification Method Using Particle Swarm Optimization

On-Line Identification Algorithm for Transformer Leakage Inductance

Kinematic Model and Parameter Calibration for Flexible Articulated Coordinate Measuring Arm

Optimal Design of Dry-Type Air-Core Reactor Using Diversity-Guided Particle Swarm Optimization Algorithm

Related Author

JI Shengchang

HU Yujia

YU Congwei

ZHENG Zhiyao

CHEN Hongtao

ZHANG Hebin

ZHANG Fan

LI Jialing

Related Institution

State Key Laboratory of Electrical Insulation of Power Equipment, Xi'an Jiaotong University

Zhejiang Huadian Equipment Testing and Research Institute Co. Ltd.

Nanjing Institute of Technology

State Key Laboratory for Manufacturing System Engineering, Xi'an Jiao tong University

School of Sciences, Xi'an Jiaotong University

⁰