Abstract:

Daily process operations are impacted by several short and long-term uncertainties like daily fluctuations and seasonal variations in production levels, such as feed compositions or change in services quality. In order for the process to handle deviations from nominal condition the effect of uncertainties should be adequately incorporated in the conceptual design phase. In addition to parametric uncertainties, uncertainty associated with physical properties and process models add a difficulty to the design problem. Therefore, classic deterministic design based on information at nominal conditions alone may not lead to best process performance in the actual industrial plant. In industrial practice overdesign based in engineering judgment aims at increasing the robustness of a design. Recent progresses in both theoretical approaches as well as computer power have renewed the interest of many researchers in academia and industry to explore process design under uncertainty with more mathematical rigor. A problem that has not yet been well studied is the distinction between process design versus a controlled process design. The usual practice is design for the worst-case scenario and in a later stage design a control system to handle the uncertainty. This practice usually leads to a robust design but the trade-off between design and control is not exploited leading to non-optimal results. Clearly, this viewpoint brings together a very important aspect, namely the integration of design and control. Unfortunately little systematic control and design of processes under uncertainty is available. Grossmann and co-workers [e.g. Halemane and Grossmann, 1983; Pistikopoulos and Grossmann, 1988] introduced the concept of integrating design and control to obtain best trade-offs between cost and process flexibility considering a steady state assumption. Pistikopoulos and coworkers [e.g. Pistikopoulos and Dimitriadis, 1995 and Bansal, Perkins and Pistikopoulos, 2002] have shown that considering a steady-state point of view renders an unrealistic control scheme and a dynamic analysis is needed. In this presentation, we will propose a novel methodology that aim at obtaining best trade-offs between design and control decisions in a dynamic view of process control and design. Our methodology will include the concept of flexible design of controlled systems under uncertainty. We will also demonstrate, with the help of this dynamic approach, that integration of design and control at conceptual level yield better cost performance and higher flexibility as compared to designs, which consider process control separately. In particular we would like to study the impact of periodical uncertainty and the influence of their frequency of occurrence. We will compare the advantages and limitations of our methodology to different deterministic and probabilistic uncertain design approaches using static-control and illustrate our methodology with the help of benchmark case studies.

References:

Halemane, K. P; Grossmann, I. E.: Optimal Process Design under Uncertainty, AIChE J. 1983, 29, 425.

Pistikopoulos, E. N.; Grossmann, I. E.: Optimal Retrofit Design for Improving Process Flexibility in Linear System, Comp. Chem. Eng. 1988, 12, 719. Pistikopoulos, E. N.; Dimitriadis, V. D.: Flexibility Analysis of Dynamic Systems, Ind. Eng. Chem. Res. 1995,34, 4451-4462. Bansal, V.; Perkins, J.D.; Pistikopoulos, E. N.: A Case Study in Simultaneous Design and Control Using Rigorous, Mixed-Integer Dynamic Optimization Models, Ind. Eng. Chem. Res. 2002, 41, 760-778.