Experiments are carried out in a paired packed-bed electrode reactor with the cathodic reduction of potassium ferricyanide coupled with the anodic oxidation of potassium ferrocyanide as the working system. Eight probes are used to determine the potential distribution. A two-dimensional model is developed and solved by the finite element method. Good agreement is observed between the experimental data and the model predictions for reactors of different sizes and operating conditions. The effect of the operating variables (cell voltage, electrolyte velocity, reactant inlet concentration and electrolyte conductivity) and the sizes of the electrodes on the potential and concentration distributions are investigated by simulation.

A model for trioxane synthesis in a coupled reactor/column system is developed. The performance of the coupled system, in terms of conversion, time/space yield and energy utilizing efficiency is investigated by simulation. Results show that if the heat input is given, there exists an optimal reflux ratio at which the time/space yield and the energy utilizing efficiency reach their maxima. As for design case, when the heat input and the reflux ratio are fixed, there exists an optimal number of trays: more trays give no improvement on the time/space yield and the energy efficiency, while less trays decrease the time/space yield and the energy efficiency significantly. Experiments are carried out to show the validity of the simplified model.

A novel on-line optimizing control strategy for wall-cooled fixed-bed reactors is presented. It is essentially a nonlinear model predictive algorithm in which the profit is uniquely expressed as the objective function, instead of a square sum of the deviations between model predicted outputs and a desired output variable trajectory. Excessive bed temperature is avoided by setting a constraint on the maximum reactor temperature. Excellent performance including fast tracking to the optimum operating condition and small overshooting of the reactor temperature has been demonstrated by taking advantage of a dynamic KL-NN reactor model.

A control vector parameterization approach using the Karhunen-Loeve (K-L) expansion for optimal control is presented. Optimal control profiles at different initial conditions are represented bya truncated K-L expansion and the optimal control is thus transformed into a parametric optimization with the K-L coefficient(s) as the optimization variable(s). Closed-loop optimal control therefore becomes feasible because the number of optimization variables is minimized. The presented approach is demonstrated on a batch electrochemical reactor in which reduction of oxalic acid glyoxalic acid is carried out under conditions with known or unknown disturbance, or electrode deactivation. The closed-loop control with the K-L expansion outperforms the open-loop optimal control with verylittle computational efiort.

Catalyst sintering is a slow complex process which may last for years but is contributed by physical processes of atomic femtosecond scale. In this article, sintering caused silver particle growth is considered to be the result of random migration and coalescence, on the basis of which a random walk model is proposed. Simulation results show that the particle size evolution can be well fitted by generalized power law expression, indicating that the random walk model is valid for sintering processes with di8erent sintering rates and time scales. With the assumption of identical grain sizes, changes in the specific surface intergrain boundary length during particle sintering are predicted by the random walk model and the epoxidation rate is related to the specific surface intergrain boundary length in terms of a selectivity factor. The results elucidate quite well the phenomenon of nonmonotonic change of the selectivity during silver catalyst sintering.