Currently, chemical process design and control are separate disciplines assisting process development at different stages. In classical process synthesis, Design and control decisions are made independently despite the common aim of ensuring robust plant operations. Design decisions are made first, then control conditions are decided to protect the process from disturbance. Therefore, process is over-designed and controller tuning and optimization is limited by process dynamics so this may not lead to best process performance in the actual industrial plant.
This objective cannot be accomplished by the current practice of intuitive “over-design”, which is susceptible to: (i) unknown safety levels usually determined by experience, or previous over-designs; (ii) impaired dynamic performance like increased process resilience or lag time; (iii) possibility of failure under dynamic operation such as overshoots, point constraint violations. So an integrated design and control decisions bring maximizing system performance and profits under model uncertainty. However considering design spec and control conditions simultaneously is so complicated that it is impossible to solve. This is of particular significance in high-performance processes, whose dynamic operation in the vicinity to constraints requires precise quantification and optimal choice of robustness levels. This presentation aims at addressing critical challenges pertaining to the lack of integration between design and control objectives at the conceptual level.
Methodolgy
In this presentation, we suggest embedded control optimization approach which is two-stage problem decomposition. This decomposition results in substantial reduce of problem complexity. The first stage will optimize the reduced space design variables to satisfy the objective function such as minimum cost or maximum profit. In the second stage, the embedded control composed of system identification, state prediction and optimal control moves will automatically guarantee the stability and controllability. For system identification subspace identification method is used. A novel stochastic design optimization with embedded control will be demonstrated as a significant advancement towards overcoming the combinatorial complexity of integrated design and control.
Significance
This presentation advocates the integration of design and control for the consistent attainment of stringent product quality demands. A concise decision-making hierarchy allows designers to arrive at key structural decisions for the process flowsheet and control layout. Rigorous mathematical programming approaches are proposed for optimizing parametric design variables as well as structural alternatives. The case study will show that the proposed methodology is applicable to distillation column. As a result an optimal design was obtained. This new design can satisfactorily operate under the most adverse input condition.
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