Modelling and Control of Reactive Polymer Composite Moulding Using Bootstrap Aggregated Neural Network Models
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Jie Zhang
This paper presents using bootstrap aggregated neural networks for the modelling and optimisation control of reactive polymer composite moulding processes. Neural network models for the degree of cure are developed from process operational data. In order to improve model generalization capability, multiple neural networks are developed from bootstrap re-samples of the original data and are combined. Reliable optimal heating profiles are obtained by solving an optimisation problem using the bootstrap aggregated neural network model and incorporating model prediction confidence intervals in the optimisation objective function. The proposed method is applied to both simulated data and real industrial data.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
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- Optimisation of Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors
- Modelling and Optimization of Crude Oil Hydrotreating Process in Trickle Bed Reactor: Energy Consumption and Recovery Issues
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- Numerical Simulation of Fluid Flow and Heat Transfer in a Counter-Current Reactor System for Nanomaterial Production
- Knowledge Based System Implementation for Lean Process in Low Volume Automotive Manufacturing (LVAM) with Reference to Process Manufacturing
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Artikel in diesem Heft
- Article
- Editorial: Special Issue of CAPE FORUM 2011
- Optimisation of Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors
- Modelling and Optimization of Crude Oil Hydrotreating Process in Trickle Bed Reactor: Energy Consumption and Recovery Issues
- An Optimization-Based Framework for Process Planning under Uncertainty with Risk Management
- Modelling and Control of Reactive Polymer Composite Moulding Using Bootstrap Aggregated Neural Network Models
- Numerical Simulation of Fluid Flow and Heat Transfer in a Counter-Current Reactor System for Nanomaterial Production
- Knowledge Based System Implementation for Lean Process in Low Volume Automotive Manufacturing (LVAM) with Reference to Process Manufacturing
- Novel Heuristic for Low-Batch Manufacturing Process Scheduling Optimisation with Reference to Process Engineering
- CFD Modelling of Reverse Osmosis Channels with Potential Applications to the Desalination Industry
