3 research outputs found
A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes
Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and the associated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data
Monitoring of Distillation Column Based on Indiscernibility Dynamic Kernel PCA
Aiming at complicated faults detection of distillation column industrial process, it has faced a grave challenge. In this paper, a new indiscernibility dynamic kernel principal component analysis (I-DKPCA) method is presented and applied to distillation column. Compared with traditional statistical techniques, I-DKPCA not only can capture nonlinear property and dynamic characteristic of processes but also can extract relevant variables from all the variables. Applying this new method to distillation column process (a hardware-in-the-loop simulation system), the results prove the proposed method has great advantages, that is, lower missing rate and higher detection rate for the faults, compared with KPCA and DPCA
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