chemistry. Other Vision 2020 workshops have been held on subjects such as separations, catalysis, polymers, green chemistry and engineering, and computational fluid dynamics.1

This paper reviews the computational needs of the chemical industry as articulated in various Vision 2020 workshops. Subsequent sections of this paper deal with process engineering paradigm in 2020, computational chemistry and molecular modeling, process control and instrumentation, and process operations.

Process Engineering in 2020

Increased computational speeds have spurred advances in a wide range of areas of transport phenomena, thermodynamics, reaction kinetics, and materials properties and behavior. Fundamental mathematical models are becoming available due to an improved understanding of microscopic and molecular behavior, which could ultimately lead to ab initio process design. This will enable design of a process to yield a product (e.g., a polymer) with a given set of target properties, predictable environmental impact, and minimum costs. Ideally one would want to be able to start with a set of material properties and then reverse-engineer the process chemistry and process design that gives those properties.

Historically the chemical industry has used the following sequential steps to achieve commercialization:

    1.  

    Research and development,

    2.  

    Scale-up,

    3.  

    Design, and

    4.  

    Optimization.

Note that steps (1) and (2) generally involve several types of experimentation, such as laboratory discovery, followed by bench-scale experiments (often of a batch nature), and then operation of a continuous flow or batch pilot plant. It is at this level that models can be postulated and unknown parameters can be estimated in order to validate the models. A plant can be designed and then optimized using these models. If the uncertainty in process design is high, pilot-scale testing may involve several generations (sizes) of equipment. With the advent of molecular-scale models for predicting component behavior, some laboratory testing can be obviated in lieu of simulation. This expands upon the traditional relationship of scientific theory and experiment to form a new development/design paradigm of process engineering (see Figure 6.1).

The development of mathematical models that afford a seamless transition from microscopic to macroscopic levels (e.g., a commercial process) is a worthy goal, and much progress in this direction has occurred in the past 10 years in areas such as computational fluid dynamics. However, due to computational limitations and to some extent academic specializations, process engineering research has devolved into four more or less distinct areas:

    1.  

    Process design,

    2.  

    Structure property relationships,

    3.  

    Process control,

    4.  

    Process operations.

1  

See <http://www.chem.purdue.edu/v2020/>, the Vision 2020 Web site for workshop reports.



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