Jeonghwa Moon

Integrated design and control under uncertainty: Embedded control optimization for plantwide processes

Abstract High performance processes should operate close to design boundaries and specification limits, while still guaranteeing robust performance without design constraint violations. Since design chemical process is operating close to tighter boundaries safely; much attention has been devoted to integrating design and control, in which the design decisions, dynamics, and control performance are considered simultaneously in some optimal fashion. However, rigorous methods for solving design and control simultaneously lead to challenging mathematical formulations which easily become computationally intractable. In an earlier paper of our group, a new mathematical methodology to reduce the combinatorial complexity of integrating design and control was introduced ( Malcolm et al., 2007 ). We showed that substantial problem size reduction can be achieved by embedding control for specific process designs. In this paper, we extend the embedded control methodologies to plantwide flowsheet. The case study for the reactor-column flowsheet will demonstrate the current capabilities of the methodology for integrating design and control under uncertainty.

A hybrid sequential niche algorithm for optimal engineering design with solution multiplicity

Abstract This paper introduces a new hybrid algorithm for locating all solutions in multimodal optimization problems. This algorithm combines an adaptive sequential niche technique with deterministic local optimization to detect all extrema efficiently and reliably. A genetic element of the hybrid algorithm performs a global search while the deterministic local optimizer computes the precise coordinates of the extremum. Once an extremum is precisely located, a niche demarcating the area of attraction around the local minimum is recorded. The sequential process proceeds to search for additional extrema. Our novel method overcomes challenges to distinguish multiple extrema in problem-specific terrain by an automatic niche radius adjustment. Several comparative simulation experiments with previous niche algorithms demonstrate the novel algorithms performance and reliability. We also present a difficult case study for solution multiplicity in catalytic pellets. We determine multiple solutions for distributed inversion problems.

Design and optimization of energy efficient complex separation networks

Abstract Separation processes account for more than half of capital and operating costs in chemical manufacturing. Separations are the energy intensive operations in the chemical industry. Rising energy consumption combined with the environmental impact increases the need for energy saving separation processes. Fortunately complex column networks have the potential for major energy savings estimated to range between 30 and 70% over simple column configuration. In this paper, a computer-aided synthesis method is introduced to synthesize optimal complex column arrangements which encode the cost and states of global solutions with minimum user input. A robust feasibility criterion helps select design and operating conditions for the entire network that can be realized in practice. Our method builds on thermodynamic transformations entitled temperature collocation which provides crucial advantages to determine the operating conditions, structure , and size of the separation network for achieving the desired product cuts. The computational approach guarantees realizable column profiles, which can be validated with industrially accepted simulation software such as Aspen Hysys and Aspen Plus.

Synthesis of Energy Efficient Complex Separation Networks

Abstract Separation processes account for about fifty percent of capital and operating costs including the highest energy demand in the chemical industry. The rise in energy consumption, the high cost of the fuels, and combined with the environmental impact increases the demand for energy saving separation processes such as optimal complex column networks which are estimated to achieve energy savings of up to 70%. This paper introduces a novel algorithm to create optimal complex column arrangements which encode the cost and states of global solutions with minimum user input. In the complex column networks, several combinations and internal connections increase the difficulty to optimize these processes. The proposed configuration algorithm generates all possible network configurations expressed as a continuous sequence of column section profiles and discriminates suboptimal solutions taking the column network structure as input. Simultaneously, a robust feasibility test is applied to the generated systematic network based in thermodynamic transformations called temperature collocation where the operating conditions, structure, and length of the separation network for realizing the desired product purities are not predefined. This robust hybrid algorithm combines the advantages of deterministic and stochastic search techniques. This computational approach guarantees rigorous column profiles validated with industrially accepted simulation software such as ASPEN. The capabilities will be illustrated using multicomponent realistic case studies especially quaternary systems.