Chemical engineers who develop fundamental models often have difficulties estimating all model parameters due to problems with parameter identifiability and estimability. These two concepts are reviewed, as are techniques for assessing identifiability and estimability. When some parameters are not estimable from the data, modellers must decide whether to conduct new experiments, change the model structure, or to estimate only a subset of the parameters and leave the others at fixed values. Estimating a reduced number of parameters can lead to better model predictions with lower mean squared error (MSE). MSE-based techniques for parameter subset selection are discussed and compared. © 2011 Canadian Society for Chemical Engineering

Mathematical modelling of chemical processes—obtaining the best model predictions and parameter estimates using identifiability and estimability procedures

Reinforcement learning (RL) appeals to many researchers in recent years because of its generality. It is an approach to machine intelligence that learns to achieve the given goal by trial-and-error iterations with its environment. This paper proposes a case-based reinforcement learning algorithm (CRL) for dynamic inventory control in a multi-agent supply-chain system. Traditional time-triggered and event-triggered ordering policies remain popular because they are easy to implement. But in the dynamic environment, the results of them may become inaccurate causing excessive inventory (cost) or shortage. Under the condition of nonstationary customer demand, the S value of (T, S) and (Q, S) inventory review method is learnt using the proposed algorithm for satisfying target service level, respectively. Multi-agent simulation of a simplified two-echelon supply chain, where proposed algorithm is implemented, is run for a few times. The results show the effectiveness of CRL in both review methods. We also consider a framework for general learning method based on proposed one, which may be helpful in all aspects of supply-chain management (SCM). Hence, it is suggested that well-designed ''connections'' are necessary to be built between CRL, multi-agent system (MAS) and SCM.

Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system

Thermodynamics and chemical systems stability: The CSTR case study revisited

On the use of extensive variables in process dynamics and control

This paper is a survey on methods which have been developed and applied in the field of dynamical modelling, analysis, monitoring and control design of bioprocesses over the past fifteen years. A key feature of the paper is to show how to incorporate the well-known knowledge about the dynamics of biochemical processes (basically, the reaction network and the material balances) in monitoring and control algorithms. These are moreover capable of dealing with the process uncertainty (in particular on the reaction kinetics) by introducing, for instance in the control algorithms, an adaptation scheme.

Dynamical modelling, analysis, monitoring and control design for nonlinear bioprocesses

http://lup.lub.lu.se/search/ws/files/22426301/fjeld_74.pdf

Reaction invariants and their importance in the analysis of eigenvectors, state observability and controllability of the continuous stirred tank reactor

Validation of a compartmental population balance model of an industrial leaching process: The Silgrain® process

https://isiarticles.com/bundles/Article/pre/pdf/5370.pdf

Magne Fjeld

Papers

Reaction invariants and their importance in the analysis of eigenvectors, state observability and controllability of the continuous stirred tank reactor

Validation of a compartmental population balance model of an industrial leaching process: The Silgrain® process

Inventory control of particulate processes

Relaxed controls in asynchronous quenching and dynamical optimization

On a pitfall in stability analysis of chemical reactions