Design and Operation at Unstable Steady States
Many processes have been overdesigned because engineers are reluctant to design near or within regimes of complex operations, where they are often economically optimal. This is prevalent in processes with exothermic and autocatalytic reactions and where phases appear and disappear, especially in the critical region. We are developing designs that operate more economically closer to these nonlinear regimes, which are characterized by multiple steady states, periodic and even chaotic operation, often exhibiting inverse response. Improved control strategies are being developed to permit reliable operation near these regimes.
Nonlinear Model-based Controller Design for Non-minimum-phase Processes
To achieve more effective process designs, nonlinear model-based controllers are being
developed for processes with a non-minimum-phase, delay-free part. The controllers are derived by exploiting the connections between model-predictive and input-output, linearization controllers, and are designed to satisfy input constraints. The performance of the control laws is illustrated using numerical simulation and real-time experiments. Special challenges arise when the process exhibits both non-minimum-phase and unstable steady states. In one approach, we seek to alter the design to modify the process dynamics. Using bifurcation analysis, optimization algorithms, and the addition of sensors and actuators, proposed design changes are explored more effectively.
Dynamic Risk Assessment of Inherently Safe Chemical Processes
To obtain inherently safer plant designs, we are experimenting with game theory to solve the multiobjective optimization problem that involves tradeoffs between profitability, controllability, safety and/or product quality, and flexibility. Then, given more optimal designs, that are inherently safer, we are developing methods for plant-specific, dynamic risk assessment using accident precursor data; that is, data recorded when abnormal events occur. Our models estimate the failure probabilities of various critical accident scenarios, associated with a process unit after the occurrence of an abnormal event, using Bayesian analysis and copulas. Currently, we are studying the dynamic risk analysis of steam-methane reformers operated by American Air Liquide.
Our emphasis is on improving process and human operator models to achieve more accurate risk analysis. To accompany sparse alarm and process data associated with plant trips and accidents, we have been constructing informed prior distributions to generate low-variance posterior distributions in Bayesian analysis. To quantify model quality, which impacts prior and posterior distributions, we have been experimenting with higher-frequency alarm and process data to select the most relevant constitutive equations and assumptions.
Bifurcation Control of High-Dimensional Nonlinear Chemical Processes
Our emphasis has been on the control of nitroxide-mediated, radical polymerization (NMRP) in a continuous-stirred-tank reactor (CSTR) to achieve reduced levels of poly-dispersity. We have been developing new optimization algorithms for washout-filter feedback control. Our algorithms adjust the eigenvalues of the model Jacobian matrix to relocate the Hopf bifurcation points, stabilizing solution branches in specified regions having high monomer conversion.
Semicontinuous Distillation with Reaction in a Middle Vessel (SDRMV)
We are currently laying the foundations for this novel concept. Semicontinuous Distillation with Reaction in a Middle Vessel (SDRMV) combines reaction and distillation in a manner that overcomes the difficulties that makes typical reactive distillation cost prohibitive. Based on the semicontinuous distillation method developed by our group, SDRMV combines ordinary distillation with tank reactors used as middle vessels. By operating in a complex sequence of changing operating modes, the SDRMV systems we are studying achieve reaction and separation goals by using process equipment for multiple purposes while keeping the scale of the system small enough to satisfy the demands of the fine chemicals industry. In future work, we seek to optimize this hybrid system using novel stochastic optimization methods that navigate through narrow flooding and weeping constraints.
Algae to Biofuels
We were members of the National Alliance for the Advancement of Biofuels and Bioproducts (NAABB) sustainability team, which concluded in April 2013. Our efforts focused primarily upon the simulation of algae-oil transesterification processes. We collaborated with Albemarle-Catilin and took kinetic measurements of their T-300, solid-base catalyst. Using the collected data, we were able to build and simulate a complete transesterification process (including a glycerolysis pre-treatment section) using ASPEN PLUS. We carried out sizing and costing of the process equipment, which was used to conduct profitability analyses and optimizations.
During our work with NAABB, the need for further development of the algae oil extraction and transesterification processes was evident. We have been focusing our efforts on the intensification of these processes using CO2, first using microbubbles to fracture the algae cell walls, followed by supercritical transesterification to biodiesel. We carry out kinetic modeling coupled with multiphase equilibria using the SAFT equation-of-state: PC-SAFT using RGIBBS in ASPEN PLUS and SAFT-γ Mie in gPROMS.