2014 AIChE Annual Meeting

(126g) Dynamic Modeling and Optimization of an Industrial Super-Staged Argon Plant

Authors

Yanan Cao - Presenter, McMaster University
Christopher L. E. Swartz, McMaster University

Air separation units (ASUs) are power-intensive processes with major operation costs due to electricity consumption. Industrial gas producers in US consume over $ 1 billion dollars’ worth of electricity annually (Chen et al., 2010). Traditionally, ASUs had infrequent changes in production rate and operation conditions, due to a relatively steady electricity price and customer demand. However, the situation has changed after electricity price deregulation, and operation of ASUs is subject to time-of-day or real-time electricity price fluctuations and varying customer demand. Frequent changes in operating conditions and/or startup/shutdown would be required to gain economic benefits from utility price fluctuations (Zhu et al., 2001). In other words, the profitability of air separation plants is highly sensitive to operation policies that utilize smart energy management and plant designs that allow flexible operation (Mitra et al., 2012). Furthermore, the tight energy and material integration and strict operational constraints also pose operation/design challenges and research opportunities. These process characteristics of ASUs call for an optimization-based framework to systematically explore both operation and design to obtain improvements in process performance. 

Systematic approaches to the design of dynamically operable plants have been the subject of many research studies over the past three decades. Studies on operation and design of ASUs in the literature are relatively limited, with one or more of the following considerations: (1) dynamic behavior (i.e. through implementing rigorous dynamic models), (2) design modification, (3) uncertainty and disturbances, (4) multi-period operation policy and (5) control structure (e.g. Zhu et al., 2010; Mitra et al., 2012). In our study, we aim to address integrated design and operation problems under uncertainty for a super-staged argon system with emphasis on the cold-box through a rigorous dynamic model in an optimization-based framework.   

The proposed strategy relies on an accurate and yet robust model that can be solved efficiently in the optimization studies. Two modeling approaches were investigated. First principles models that capture key process phenomena were first developed with columns modeled in a stage-wise manner. The posed integrated model was then validated using available plant information through parameter estimation to ensure that the model accurately represents the plant. Desired prediction accuracy was achieved after the model validation with negligible discrepancies in key process variables. However, the stage-wise model results in a large scale differential-algebraic equation system, which poses challenges in dynamic optimization studies with either the simultaneous or sequential approach.  Hence, model reduction was conducted through an orthogonal collocation approach (Stewart et al., 1985; Swartz and Stewart, 1986). Significant model reduction can be achieved without losing desired prediction accuracy, and promising improvements in the solution time have been observed in simulation and optimization studies performed. The orthogonal collocation approach can also be applied to model the primary heat exchanger in the air separation plant. In our study, gPROMS is the main modeling platform. While solving the dynamic optimization problems, both the sequential approach (through gOPT) and simultaneous approach (through AMPL) are applied, with an in-house developed program MLDO used to convert the gPROMS model into AMPL code.

In this paper, we present both full and reduced order modeling approaches, discuss and compare their computational performance, and illustrate their capability through both dynamic simulation and dynamic optimization.

References

Chen, Z., Henson, M., Belanger, P., and Megan, L. (2010). Nonlinear model predictive control of high purity distillation columns for cryogenic air separation. Control Systems Technology, IEEE Transactions on, 18(4), 811 –821.

Mitra, S., Grossmann, I. E., Pinto, J. M., and Arora, N. (2012). Robust Scheduling under Time-sensitive Electricity Prices for Continous Power-intensive Processes. In FOCAPO 2012: Foundations of Computer-Aided Process Operations, Savannah, Georgia January 8 - 11.

Stewart, W., Levien, K., and Morari, M. (1985). Simulation of fractionation by orthogonal collocation. Chemical Engineering Science, 40, 409 – 421.

Swartz, C.L.E., and Stewart, W. (1986). A collocation approach to distillation column design. AIChE Journal, 32, 1832-1838.

Zhu, G., Henson, M. A., and Megan, L. (2001). Low-order dynamic modeling of cryogenic distillation columns based on nonlinear wave phenomenon. Separation and Purification Technology, 24, 467 – 487.

Zhu, Y., Legg, S., and Laird, C. D. (2010). Optimal design of cryogenic air separation columns under uncertainty. Computers & Chemical Engineering, 34(9), 1377 – 1384.