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Parallel and Vector Computation for Chemical Process Design

Computer aided design in chemical engineering has emphasized numerical solution of large sets of algebraic and differential equations that describe the process. Solution techniques for such large sets of equations usually concentrate on decomposing the problem for iterative solution. A sequential-modular decomposition for iterative solution, however, also partitions the large set of equations in way suitable for parallel computation. In addition, the equations describing a typical module often take a vector form allowing further optimization vector computers.

Initial research on parallel and vector computation would take place in two areas. The first area would concentrate on modular process design systems. Problem decomposition for parallel processing would be based on the process unit. In addition, process units would provide a basis for computational objects. Object oriented programming techniques would also contribute to the parallel computational decomposition. The second area would focus on solution of transport continuum equations. Finite difference and finite element solution of PDE's both generate large sets of algebraic equations suited to vector and parallel computation.

Visualization for Chemical Process Analysis

Rapid developments of computer hardware will cause a significant qualitative change in the chemical process design cycle. Computers play a strong role in solving the analytical equations describing the chemical process. These results are analyzed as individual case studies in an iterative design cycle. The analysis cycle in chemical engineering design is just beginning to make significant use of the rapidly developing capabilities of CAD systems. The ability to provide rapid graphical displays will greatly aid in the intuitive understanding of the process under analysis. Developing this understanding would be especially important in the educational setting. The displays would illustrate how material properties influence the transport processes that control chemical processes. For example, a proper graphic presentation can show the distinction between homogeneous and non-homogeneous materials. In addition, the display can show differences in isotropic and anisotropic transport processes. Research in this area would closely follow the above research on parallel processing for continuum equations. The methods developed would serve an educational role in teaching transport phenomena.

Multi-phase Processes in Chemical Processing

Many processes in chemical technology involve multi-phase systems in which heat and mass transfer occurs between phases. These operations depend on the particle size area resulting from the individual particle interactions. Typical operations with multi-phase systems include coal and hydrocarbon spray combustion, emulsification and polymerization, crystallization, liquid atomization, and fluidization. Fundamental understanding of particle interactions would be useful for understanding such processes. This research area would include both theoretical and experimental studies.

The particle size distribution of a multi-phase process depends on the individual particle dynamics averaged over the entire particle population. The population balance equation (PBE) models the effects of particle processes such as breakage, agglomeration, entrance, and exit from a control volume. In its most general form, the PBE completely describes the particle population in non-homogeneous systems. Solution of the PBE, however, must rely on numerical methods for most systems. The theoretical aspect of this work would concentrate on variational methods for solution of the integro-differential PBE. In addition, Monte Carlo methods would also be considered for modeling the PBE.

Experimental studies would require development of experimental equipment to study drop breakup by high speed photography of atomizing sprays. In addition, a fluidized bed would be developed for analysis of systems with minimal particle breakage and agglomeration. On-line instrumentation would measure and control the desired fluidization conditions. The on-line measurements would include fluidization velocity, bed depth, bed pressure drop, and material loss rates. Additional off-line analysis of the materials fed to and collected from the fluidized bed would be performed to determine material properties such as particle size, density, and morphology.

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Gregory W. Smith (WD9GAY)
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