(177d) Novel Optimization-Based Approach for Process Design and Intensification of High-Dimensional Modular Systems | AIChE

(177d) Novel Optimization-Based Approach for Process Design and Intensification of High-Dimensional Modular Systems


Carrasco, J. C. - Presenter, West Virginia University
Lima, F. V., West Virginia University
In the recent years, due to the advent of the shale gas revolution, the US has incremented the annual production of processed natural gas by approximately 21% from 16.3 to 19.8 trillion cubic feet1. To accelerate the potential benefits brought by the abundant amounts of shale gas in the country, approaches are needed for the optimization and techno-economic viability analysis of novel natural gas utilization processes that produce heat, power, fuels and chemicals. However, the design of such processes is a challenging task as these systems are typically operated in a highly constrained and integrated environment that is represented by complex large-scale and nonlinear models. In this presentation, a novel optimization-based operability approach for process design and intensification of high-dimensional and modular energy systems is proposed. These modular units if enabled have the potential to transform the US economy by utilizing local stranded gas without the need of building expensive pipelines.

Process operability was originally developed as an approach for the design and control of complex chemical processes2. This approach has been applied to processes that are described by high-dimensional linear models3,4. For nonlinear systems, an operability-based approach for design and intensification of low-dimensional membrane reactor systems has also been proposed5,6. However, operability methods for nonlinear systems are currently limited by the problem size that they can address. The proposed approach bridges this gap in the literature by addressing the challenges of process nonlinearity and model size. The proposed method also broadens the scope of the traditional path of the operability approaches for design and control, mainly oriented to obtain the achievable output set (AOS) from the available input set (AIS), and compare the computed AOS to a desired output set (DOS). In particular, an optimization algorithm based on nonlinear programming tools is formulated for the high-dimensional calculations of the desired input set (DIS) that is feasible considering process constraints, performance levels and intensification targets.

To illustrate the effectiveness of the developed method, two modular systems are addressed: i) a membrane reactor (MR) for the direct methane aromatization (DMA) conversion to benzene and hydrogen; and ii) a natural gas combined cycle (NGCC) system for heat and power generation. Results on the application of this novel method as a tool for process intensification show reduction of the DMA-MR footprint (â??77% reactor volume and 80% membrane area reduction) for an equivalent level of performance, when compared to the base case. These results indicate that the novel approach can be a powerful tool for enabling the realization of the concept of the modular economy.


  1. EIA (2016). U.S. Energy Information Administration. Natural Gas Report. Available at: http://www.eia.gov/dnav/ng/ng_sum_lsum_dcu_nus_m.htm
  2. Vinson D. R., Georgakis C. A new measure of process output controllability. J. Proc. Cont. 2000, 10(2-3), 185-194.
  3. Lima F. V., Georgakis C. Design of output constraints for model-based non-square controllers using interval operability. J. Proc. Cont. 2008, 18(6):610-620.
  4. Lima F. V., Georgakis C. Input-output operability of control systems: the steady-state case. J. Proc. Cont. 2010, 20(6):769-776.
  5. Carrasco J. C. and Lima F. V. Nonlinear operability of a membrane reactor for direct methane aromatization. In Proceedings of the 2015 IFAC ADCHEM Symposium, Whistler, Canada, June 2015.
  6. Carrasco J. C. and Lima F. V. Novel operability-based approach for process design and intensification: application to a membrane reactor for DMA. Submitted for publication, 2016.