Performance comparison of controllers acting on a batch pulp digester using Monte Carlo modelling

Finding a suitable control structure for any process usually involves comparing the performance of different possible control structures and choosing one which best satisfies chosen criteria. It is desirable to do this performance comparison off-line, as installation of a sub-optimal controller will cost both time and money.Monte Carlo modelling provides a well documented method of evaluating the statistical properties of stochastic systems. Applied to control system design, Monte Carlo modelling can incorporate detailed process models and accurate estimates of input distributions to give an accurate estimate of the effect of different control strategies on the system.In this study, Monte Carlo modelling was used to compare three candidate controllers in order to determine the best controller in terms of two criteria, namely variance reduction and setpoint tracking. The modelling technique yielded results that could be interpreted without difficulty, showing one controller to be clearly superior to the others according to these criteria. These results can be used to implement the best controller without expensive trial and error procedures.In situ experiments on an operational digester correlated well with the simulation results, showing the best controller to reduce variance by 43% and reduce the mean error by 90% when compared to the controller currently in use.It is shown that Monte Carlo modelling is a viable technique for controller performance analysis on highly nonlinear processes, due to the increasing availability of powerful computing systems.