Professor Jutanís areas of interest include Process Control, Process Optimization and Engineering Statistics.

In the process control area Model Predictive Control(MPC) of nonlinear multivariable processes has been studied. This methodology has been applied to fluid catalytic cracking reactors. The application of MPC under constrained conditions has been investigated and applied to situations where process constraints are present and affect the nature of the control. Adaptive control and nonlinear control methods have been investigated and been applied to an experimental pressure tank with input constraints. Control of nonlinear processes using greybox modelling techniques has been implemented using neural networks. A new neural adaptive controller has been developed an applied to extremely nonlinear processes such as pH control. Control of distributed parameter systems, such as plug flow, reactors has been studied.

In the area of optimization, we have developed new continuous optimization methods for chemical processes which are operated under continuously changing conditions and thus the optimal operating conditions change with time. On-line optimization techniques of various types which are used to track these kinds of moving optimum, have sparked significant interest in recent years. However, most of these strategies deal only with static optimum or optimum that move so slowly that they can be considered static. We have extended the traditional Nelder-Mead simplex method to allow tracking of moving optimum, which results in a so-called Dynamic Simplex Algorithm. This has been applied t tracking the moving optimal conditions in chemical processes. Optimal control of batch reactor systems has also been studied and new online methods developed.

In the area of Statistics , Response Surface Methods (RSM) and experimental design methods have been applied to the tuning and robustness analysis of control loops. RSM has been applied to the tuning of MPC controllers. RSM has been successfully applied to optimization and control of nonlinear dynamic processes using a newly developed variant called Dynamic RSM. Statistical methodology has been applied to adaptive online process identification of processes for the purpose of continuous adaptive process optimization. Identification of processes under closed loop control has been studied

Key words: Nonlinear Control, Adaptive Control, Response Surface Methods, Real Time Optimization

Arthur Jutan
PROFESSOR EMERITUS