Soot capture in an electrocatalytic reactor

A major problem with many soot emission control devices is the fact that they quickly become loaded with soot which must be removed by a regeneration process. A soot capture reactor using a large flow channel was studied in order to eliminate channel plugging and avoid regeneration. Electrostatic precipitation was used in order to enhance particle diffusion to the catalyst wall of the reactor tube. The system effectiveness for soot capture was measured with filter paper sampling of the incoming versus the outgoing flow through the reactor. Soot filter loadings were analyzed by laser optical transmission. From the soot filter paper samplings combined with a visual inspection of the catalyst material surface, the system effectiveness at low voltages was a combination of the electrostatic precipitation and the catalytic oxidation. Reactor outlet soot concentrations showed a significant decrease when high voltage was applied, showing a strong effect of the electrostatic precipitation. However, catalytic oxidation was not apparent at high voltages because a heavy coating of soot was found on the catalyst surface. Computer simulation models using the Chebyshev Polynomial Software Package were developed to approximate the amount of soot deposited in the reactor tube. The simulation predictions are compared to the experimentally observed soot capture results. The results from this simulation confirmed that the external electric field generated by the use of a central wire has a major effect on the soot capture in the reactor tube

Process control of a laboratory combustor using neural networks

Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields