Constrained Hybrid Neural Modelling of Biotechnological Processes

International audienceWe propose a general methodology to develop a hybrid neural model for a wide range of biotechnological processes. The hybrid neural modelling approach combines the flexibility of a neural network representation of unknown process kinetics with a global mass-balance based process description. The hybrid model is built in such a way that its trajectories keep their physical and biological meaning (mass balance, positivity of the concentrations, boundness, saturation or inhibition of kinetics) even far from the identification data conditions. We examine the constraints (a priori knowledge) that must be satisfied by the model and that provide additional conditions to be imposed on the neural network. We illustrate our approach with various biotechnological processes showing how to select the appropriate neural network architecture. The method is detailed for modelling an anaerobic wastewater treatment bioreactor using experimental data

Observer-Based Robust H_infty Control for Uncertain Discrete-Time T-S Fuzzy Systems

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Hybrid neural modelling of an anaerobic digester with respect to biological constraints

A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor

Hybrid neural modelling of anaerobic wastewater treatment processes

This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feed-forward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data

Tailoring iron complexes for ethylene oligomerization and/or polymerization

Recent progress in the use of iron-based complex pre-catalysts for ethylene reactivity is reviewed, illustrating the current state-of-the-art and the potential usefulness of such systems for delivering solely ethylene oligomerization or polymerization products. The problems associated with the industrial use of late transition metal complex pre-catalysts are generally regarded as catalyst deactivation and the formation of more products of lower molecular weight at elevated temperature. These problems have been addressed for iron-based complex pre-catalysts via the fine tuning of substituents of existing ligands and/or the design of new ligand sets. Results revealed that modified bis(imino)pyridyliron dichlorides were capable of operating at elevated temperatures, and were capable of delivering highly linear polyethylene. Other new models of iron complexes have achieved high activity for ethylene oligomerization and/or polymerization. Particularly successful has been the use of the 2-iminophenanthrolyliron pre-catalyst, which have now been utilized in a 500 tonne pilot plant. © 2013 The Royal Society of Chemistry