(86c) Dynamic and Manufacturing Scheduling Approaching the Maximum Profitability Applications of Air Separation Units

Song Wang, Honglin Qu, and Qiang Xu

Dan F. Smith Department of Chemical Engineering

Lamar University, Beaumont, TX 77710, USA

Abstract

Cryogenic air separation processes use different in boiling points of the components to separate air into the desired products oxygen, nitrogen and argon. Unlike other chemical processes, the cost of feedstock (air) of an ASU is trivial; meanwhile, the energy supply of the entire process is totally in the charge of electricity power. ASUs are also characterized by fluctuating operating conditions to respond to changing product demands [1]. Although it is not much necessary to improve current ASU designs since there are quite proven techniques of cryogenic air separation design existing in industries; however, there are still significant potentials for improving their profitability in terms of optimal scheduling and operation ASU manufacturing.

Currently, many air separation plants are operated in a dynamic economic environment [2]. The electricity price could frequently fluctuate according to real time marketing (RTM) demands in different regions. In this paper, a general methodology based on dynamic simulation and scheduling of an ASU has been developed, which can optimize the ASU operation with respect to the electricity cost to achieve the best profit as well as highly efficiency. It contains three merits: (i) considering the entire ASUs dynamic simulation case study with detailed control strategy; (ii) optimal scheduling for maximize process profitability under uncertainties; (iii) smart production decision based on marketing supply-demands relationship. First, the methodology develops reliable dynamic simulation models under various production modes (capacities) and studies the dynamic transition performance among those production modes. Second, the optimal scheduling and transition will be conducted to maximize the plant profitability under the electricity cost pattern. Finally, the optimized ASU schedule and operations will be fine turned and validated by rigorous dynamic simulations. The developed methodology can be further extended to help ASUs for the predictive and proactive smart manufacturing.

Keywords: Air Separation Unit, Production Scheduling, Maximum Profitability, Smart Manufacturing

References

[1] Rui, H; Victor M. Zavala; Lorenz T. Biegler *. Advanced Step Nonlinear Model Predictive Control for Air Separation Units. Journal of Process Control. www.elsevier.com/locate/jprocont.

[2] Li, T*; Thierry, R; and Marc, B. Production Scheduling of Air Separation Processes. Air Liquide, Newark, DE 19702; Air Liquide, Champigny-sur-Marne, France; Air Liquide, Madrid, Spain.