Multi-Objective Optimal Control Study of Fed-Batch Bio-Reactor**IIT Gandhinagar, Ahmedabad, Gujarat, India

Patel, Narendra; Padhiyar, Nitin

doi:10.1016/j.ifacol.2016.07.223

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Xiong and Zhang (2004) propose a new neural network modeling method to maximize the product yield. Patel and Padhiyar (2016) develop a multi-objective optimization method to find the minimum fed-batch time along with maximizing productivity-yield.…”

Section: Introductionmentioning

confidence: 99%

A forgetting-factor based data-driven optimal terminal iterative learning control with applications to product concentration control of ethanol fermentation processes

Lin

Liang

et al. 2019

Transactions of the Institute of Measurement and Control

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Ethanol fermentation process (EFP) is characterized as a repetitive batch process with strong nonlinear behavior, changing operational conditions and exogenous disturbances which causes huge cost and hard difficulties in modeling an EFP. In this work, a forgetting-factor based data-driven optimal terminal iterative learning control (FF-DDOTILC) is proposed for the product concentration control of an EFP, which is regarded as an unknown nonlinear and nonaffine discrete-time system in general. An iterative dynamic linearization method is introduced to transfer the nonlinear system equivalently into a linear parametric incremental input-output form. The learning control law is derived by iteratively optimizing the proposed new objective function with a forgetting-factor. Meanwhile, a project parameter updating law is designed to estimate the unknown parameters in the linear input-output data model. By introducing a forgetting-factor, the proposed method becomes more flexible and efficient with a better control performance. The proposed FF-DDOTILC only depends on the I/O data for the design and analysis where the convergence of tracking error is guaranteed mathematically. The proposed method is applicable and effective in the product concentration control of the ethanol fermentation process verified through detail simulations.

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Section: Introductionmentioning

confidence: 99%

A forgetting-factor based data-driven optimal terminal iterative learning control with applications to product concentration control of ethanol fermentation processes

Lin

Liang

et al. 2019

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…dynamic optimization or optimal control problems. Normally such problems are solved using (i) direct optimal control approaches using gradient based methods such as the sequential approach/single shooting and simultaneous approach/multiple shooting (Abo-Ghander et al, 2010;Logist et al, 2012) and (ii) stochastic approaches (Bhaskar et al, 2000;Mitra et al, 2004;Patel and Padhiyar, 2016).…”

Section: Introductionmentioning

confidence: 99%

Bi-objective optimization of dynamic systems by continuation methods

Keßler

Logist

Mangold

2017

Computers & Chemical Engineering

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As a proof of concept the properties of path-following methods are studied for multi-objective optimization problems involving dynamic systems (also called multi-objective dynamic optimization or multi-objective optimal control problems), which have never been presented before. Two case studies with two objectives are considered to cover convex, as well as non-convex trade-off curves or Pareto sets. In order for the method to be applicable, the infinite dimensional dynamic problems have to be discretized and scalarization parameters have to be introduced, which leads to large-scale parametric nonlinear optimization problems. For both the chemical tubular reactor and the fed-batch bioreactor case study it is found that a path-following continuation approach is able to compute the Pareto fronts accurately and efficiently. A branch switching technique is required whenever a constraint switches from active to inactive or vice versa. When dealing with non-convex problems, a technique for detecting inflection points is required. Simple switching techniques are suggested and have been tested successfully.

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Axial dispersion modeling of industrial hydrocracking unit and its multiobjective optimization

Harode

Ramteke

2017

Chemical Engineering Research and Design

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Multi-Objective Optimal Control Study of Fed-Batch Bio-Reactor**IIT Gandhinagar, Ahmedabad, Gujarat, India

Cited by 4 publications

References 10 publications

A forgetting-factor based data-driven optimal terminal iterative learning control with applications to product concentration control of ethanol fermentation processes

A forgetting-factor based data-driven optimal terminal iterative learning control with applications to product concentration control of ethanol fermentation processes

Bi-objective optimization of dynamic systems by continuation methods

Axial dispersion modeling of industrial hydrocracking unit and its multiobjective optimization

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