A simultaneous strategy for solving the integrated planning, scheduling and control problem considering short-term periods is proposed in this paper. The problem is formulated as a mixed integer dynamic optimization problem. The main practical motivation to use a simultaneous, rather than a sequential solution strategy is to seek improved optimal solutions by considering the interactions among planning, scheduling and control. Integration of the three levels of the problem represents a challenge given the very different horizon times involved in the operations, resulting in growth of the problem in terms of the number of equations to be solved, and hence on computational requirements to carry it out. A full space approach was used rather than trying a decomposition strategy. Moreover, a nonlinear model predictive control strategy was also used to account for on-line product transition trajectories. The integrated planning, scheduling and control is formulated as a mixed integer dynamic optimization model, which is tested using the dynamic models of three continuous stirred tank reactors featuring different nonlinear behavior.

No abstract

A new multiobjective optimization formulation dealing with simultaneous scheduling and control issues in single line processing systems is proposed. Objective functions featuring economic profit and dynamic performance are considered because normally they are in conflict. Because integer, continuous variables and process dynamic behavior are involved, the bicriterion optimization problem is cast in terms of a mixed-integer dynamic optimization (MIDO) problem. The Pareto front of each problem is computed using the ε-constraint method for handling multiobjective problems. The results indicate that improved optimal solutions can be obtained by using multiobjective optimization techniques instead of just simple merging of all the target objective functions into a single objective. The proposed multiobjective approach for handling scheduling and control problems is illustrated using three CSTR examples of varying nonlinear behavior.

Traditionally the optimization of processing systems has relied on the availability of an explicit model together with the corresponding gradient information. However, there are some practical scenarios such as (a) nondifferentiable systems, (b) physical experimental systems, (c) simulation environments, and (d) reduced order systems where such a model and its gradient are not available. Under these scenarios the deployment of derivative-free optimization strategies provides an alternative manner to cope with the optimization of such systems. In particular, in this work we deploy a derivative-free optimization trust region approach to deal with the product dynamic optimization problem of processing systems. To this aim, we use a closed-loop model predictive control strategy where the system to be optimized is embedded in a black-box dynamic simulation environment. The results demonstrate that black-box dynamic models can be dynamically optimized assuming that the number of decision variables is not large. The first-principles dynamic model of a binary distillation column embedded in the ASPEN dynamic simulation environment was deployed as our black-box dynamic model, to demonstrate the advantages of solving product dynamic transition problems when an explicit model of the dynamic model and/or its gradient information are not available.

No abstract