• Process and Product Design. The traditional process design will be expanded to include product design as an integral part of this area. Within the commodity chemicals industry major challenges that will be addressed include process intensification for novel unit operations, and design of environmentally benign processes. Areas that are likely to receive increased attention due to the growth in new industries include molecular design, synthesis of microchips, smart materials, bioprocess systems, bioinformatics, and design and analysis of metabolic networks.

• Process Control. The traditional process control will be expanded toward new applications such as nonlinear process control of biosystems. However, in the commodity chemicals industry there will be increased need for synthesizing plantwide control systems, as well as integrating dynamics, discrete events, and safety functions, which will be achieved through new mathematical and computer science developments in hybrid systems.

• R&D and Process Operations. The traditional area of process operations will expand upstream and downstream in terms of integrating R&D as well as logistics and distribution functions. Areas that are likely to receive increased attention include logistics for new product development, planning and supply chain management, real-time scheduling, and synthesis of operating procedures (safety).

• Integration. As is also described below, the integration of several parts of the chemical supply chain will give rise to a number of challenges, such as multiscale modeling for molecular dynamics, integration of planning, scheduling and control (including Internet based), and integration of measurements, control, and information systems.

Progress in these areas will require a number of new supporting tools that can effectively handle and solve a variety of mathematical models involving thousands and millions of variables. These supporting tools in turn will require that chemical engineers become acquainted with new advances in numerical analysis, mathematical programming, and local search techniques.

• There is a need for large-scale differential-algebraic methods for simulating systems at multiple scales (e.g., fluid mechanics and molecular dynamics), a capability that is still at a very early stage.

• There is a need for methods for simulating and optimizing models whose parameters are described by probability distribution functions, a capability that is in its infancy.

• There is a need for advanced discrete-continuous optimization tools that can handle mixed-integer, discrete-logic, and quantitative-qualitative equations to model synthesis and planning and scheduling problems.

• There is a need for methods that can determine global optima for arbitrary nonlinear functions, and that can handle extremely large nonlinear models for real-time optimization (on the order of millions of variables).