4 research outputs found
IMPROVING NONLINEAR PROCESS MODELING USING MULTIPLE NEURAL NETWORK COMBINATION THROUGH BAYESIAN MODEL AVERAGING (BMA)
Improving model generalization of aggregated multiple neural networks for nonlinear dynamic process modeling using Bayesian Model Averaging (BMA) is proposed in this paper. Using BMA method, the posterior probability of a particular network being the true model is used as the combination weight for aggregating the network despite of using fixed combination weight as the model. The posterior probabilities are calculated using the sum square error (SSE) from the training data on each of the sample time, and tested to the testing data. The selections for the final weight are based on the least SSE calculated when each of the posterior probability is applied to the testing data. The likelihood method is employed for calculating the network error for each input data. Then, it is used to calculate the combination weight for the networks. Two non-linear dynamic system-modeling case studies are selected for this proposed method, which are water tank level prediction and pH neutralization process. Application result demonstrates that the combination using BMA technique can significantly improve model generalization compared to other linear combination approaches
Data-driven modelling and monitoring of industrial processes with applications in nuclear waste vitrification
PhD ThesisProcess models are critical for process monitoring, control, and optimisation. With the
increasing amount of process data and advancements in computational hardware, data-driven
models are a good alternative to mechanistic models, which often have inaccuracies or are too
costly to develop. One problem with data-driven models is the difficulty in ensuring that the
models perform well on new data and produce accurate predictions in complex situations, which
are frequently encountered in the process industry.
Within this context, part of this thesis explores developing better data-driven models through
using a latent variable technique, known as slow feature analysis, as a pre-processing step to
regression. Slow feature analysis extracts slow varying features that contain underlying trends
in the data, which can improve model performance through providing more meaningful
information to regression, reducing noise, and reducing dimensionality. Firstly, the
effectiveness of combining linear slow feature analysis with a neural network is demonstrated
on two industrial case studies of soft sensor development and is compared with conventional
techniques, such as neural networks and integration of principal component analysis with a
neural network. It is shown that integration of slow feature analysis with neural networks can
significantly improve model performance. However, linear slow feature analysis can fail to
extract the driving forces behind data in nonlinear situations such as batch processes. Therefore,
using kernel slow feature analysis with a neural network is proposed to further enhance process
model performance. A numerical example was used to demonstrate the effective extraction of
driving forces in a nonlinear case where linear slow feature analysis cannot. Model
generalisation performance was improved using the proposed method on both this numerical
example, and an industrial penicillin process case study.
Dealing with radioactive nuclear waste is an important obstacle in nuclear energy. Sellafield
Ltd have a nuclear waste vitrification plant which converts high-level nuclear waste into a more
stable, lower volume glass form, which is more appropriate for long term storage in sealed
containers. This thesis presents three applications of data-driven modelling to this nuclear waste
vitrification process. A predictive model of the pour rate of processed nuclear waste into
containers, an early detection system for blockages in the dust scrubber, and a model of the
long-term chemical durability of the stored glass waste. These applications use the previously
developed slow feature analysis methods, as well as other data-driven techniques such as
extreme learning machine and bootstrap aggregation, for enhancing the model performance.Engineering and Physical Sciences Research Council (EPSRC) and
Sellafield Lt