Strong stability of optimal design to dynamic system for the fed-batch culture

Author:

Zhang Jinxing12,Yuan Jinlong123,Dong Zhenyu12,Feng Enmin1,Yin Hongchao2,Xiu Zhilong2

Affiliation:

1. School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, Liaoning, P. R. China

2. School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, Liaoning, P. R. China

3. School of Life Science and Biotechnology, Dalian University of Technology, Dalian 116024, Liaoning, P. R. China

Abstract

Most economic and industrial processes are governed by inherently nonlinear dynamic system in which mathematical analysis (with few exceptions) is unable to provide general solutions; even the conditions to the existence of equilibrium point for the nonlinear dynamic system are simply not established in some special cases. In this paper, based on numerical solution of a nonlinear multi-stage automatic control dynamic (NMACD) in fed-batch culture of glycerol bioconversion to 1,3-propanediol (1,3-PD) induced by Klebsiella pneumoniae (K. pneumoniae), we consider an optimal design of the NMACD system. For convenience, the NMACD system is reconstructed together with the existence, uniqueness and continuity of solutions are discussed. Our goal is to prove the strong stability with respect to the perturbation of initial state for the solution to the NMACD system. To this end, we construct corresponding linear variational system for the solution to the NMACD system, and also prove the boundedness of fundamental matrix solutions to the linear variational system. On this basis, we prove the strong stability appearing above through the application of this boundedness.

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Modelling and Simulation

Reference27 articles.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analytical modeling of laminated composite plates using Kirchhoff circuit and wave digital filters;Journal of Industrial & Management Optimization;2020

2. Iterative Learning Control of Multi-phase Batch Processes;Iterative Learning Stabilization and Fault-Tolerant Control for Batch Processes;2019-03-19

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