Process safety has paramount importance in a chemical process. A well designed control
system is the first layer in a process system. The warning system works as the
upper protection layer above the control system. It alerts the operators when the
control system fails to prevent an undesired situation. A typical warning system issues
warnings when a monitored variable exceeds the threshold. Often these do not
allow operators sufficient lead-time to take corrective actions. With the motivation
of improving the operator’s working environment by providing lead-time, the current
research develops a predictive warning scheme using a moving horizon technique.
The main hypothesis proposed in this thesis is given the current state of process system,
the future states of the system can be predicted using a suitable model of process
system. If an external input disturbs the system state, the controller will try to bring
the system within the desired control/safety limits of the system. A warning is issued
if it is determined that the control system will not be able to keep the system withing
the safety limits. Based on the hypothesis, warning systems were developed for both
linear and nonlinear systems. For linear systems, using the gain of the models, a
linear constrained optimization problem was formulated. Linear programming (LP)
was used to determine if the system will remain within the safety limits or not. In
case the LP determines that there is no feasible solution within the constrained limits,
warnings are issued. The predictive warning scheme was also extended for nonlinear systems. A non-linear
receding horizon predictor was used to predict the future states of the nonlinear
system. However, for nonlinear system formulation leads to nonlinear constrained
optimization problem, where the constraints are the safety limits. Controller’s ability
to keep the predicted states inside the safety limit was checked using a feasibility
test algorithm. The algorithm uses a constraint separation method with weighting
functions to determine the existence of a feasible solution. The algorithm calculates
the global minimum of the objective function. If the global minimum of the objective
function is positive, it signifies no feasible solution within the input and output constraints
of the system and a warning is issued.
Prediction of the effect of the disturbances requires the knowledge of the disturbances.
In process industries, disturbances are often unmeasured. This thesis also investigates
the estimation of unknown disturbances. An iterative Expectation Minimization (EM)
algorithm was proposed for the estimation of the unknown states and disturbances of
nonlinear systems.
Efficacy of the proposed methods was shown through a number of case studies. The
warning system for the linear system was simulated on a virtual plant of a continuous
stirred tank heater (CSTH). The nonlinear warning system was implemented on a
continuous stirred tank reactor (CSTR). Both case studies showed that, the proposed
method was capable of providing a warning earlier than the traditional methods that
issues warning based on the measured signals