Neural Network-Based Noise Suppressor and Predictor for Quantifying Valve Stiction in Oscillatory Control Loops

Valve stiction-induced oscillations in chemical processing systems adversely affects control loop performance and can degrade the quality of products. Estimating the degree of stiction in a valve is a crucial step in compensating for the effect. This work proposes a neural network approach to quantify the degree of stiction in a valve once the phenomenon has been detected. Several degrees of stiction are simulated in a closed loop control system by specifying the magnitude of static (fs) and dynamic (fd) friction in a physical valve model. Each simulation generates controller output OP(t) and process variable PV(t) time series data. A feed-forward neural network (the predictor) is trained to model the relationship between a given OP and PV pattern, and the stiction parameters. To test the models predictive capability, a separate set of stiction patterns are generated with and without added process noise. An inverse neural network-based nonlinear principal component analysis (INLPCA) noise-suppressor effectively extracts the underlying stiction behaviour from the noise-corrupted OP and PV stiction patterns. In the noiseless test patterns, the predictor is shown to estimate fs and fd with a 0.65% average error. In the case of the noisy test patterns, the average error achieved was 1.85%. Since the predictor is developed offline, the use of computationally intensive real-time search/optimization routines to quantify stiction is avoided. The neural networks proved to be easily implementable, highly flexible models for extracting stiction behavior from control loops and accurately quantifying stiction, as long as an adequate first-principles description of the process dynamics can be developed

Design of Input Sequence to Capture Adequate Non Linearity for Empirical Modeling Purposes

The objective of this report is to discuss the preliminary research done and basic understanding of the chosen topic, which is Design of Input Sequence to Capture Adequate Non Linearity. The ultimate aim of the project is to find the best input sequence that can capture adequate non linearity and give the best predictive empirical model of Continuous Stir Tank Reactor. The challenge in this project is to find the available input sequence, which has been available in other people research project, applied them in MATLAB Simulink and further tested in various types of Neural Network. Simulation model will be design to test for the best input sequence that will give the best result for prediction of output. Once the result from the simulation has been get, the best input sequence will be test on the real system to prove that the result obtain in the real cases is similar with simulation that had been carried out

Modeling of precision motion control systems: a relay feedback approach

Ph.DDOCTOR OF PHILOSOPH

Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

This paper studies reduced-order-models for the fluid flow problem of a digital valve, and whether it may efficiently be formulated by a deep Artificial Neural Network (ANN) to model e.g. the valve flow, flow-induced force, stiction phenomena and steep local pressure gradients that arise before plunger impact, which may otherwise require CFD to be accurately modeled. Several methodologies are investigated to evaluate both the required computation time and the accuracy. The accuracy is benchmarked against CFD solutions of flows and forces. As basis for comparison an analytical model is proposed where some fitting parameters are allowed, and the equation is tested outside its fitting range. A similar model is built as a deep ANN which is trained with data from the analytical model to investigate the amount of data required for an ANN and its fitting capabilities. The results show that in higher dimensions the required training data can be maintained low if data is structured by a Latin Hypercube, otherwise the amount becomes infeasible. This makes an ANN surrogate feasible when compared to a look-up table, and may be expanded to higher dimension where dynamical effects are included. However, the required data and computational cost for this is too extensive for the valve design considered as basis for the analysis. Instead, for this specific problem, the derived analytical model is sufficient to describe the valve dynamics and reduces the computation time significantly