Impact of Advances in Computing and Communications Technologies on Chemical Science and Technology Report of a Workshop (1999) / Chapter Skim
Currently Skimming:
6 Vision 2020: Computational Needs of the Chemical Industry
6 Vision 2020: Computational Needs of the Chemical Industry
Pages 74-90
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 74... ...
chemical industry as it moves into the 21st century, including shareholder return, globalization, efficient use of capital, faster product development, minimizing environmental impact, improved return on investment, improved and more efficient use of research, and efficient use of people. As the chemical industry tries to achieve these goals, it is investigating the expanded use and application of new computational technologies employed in areas such as modeling, computational chemistry, design, control, instrumentation, and operations.
|
From page 75... ...
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.
|
From page 76... ...
Electronic structure calculations can also provide quantitative insights into bonding, orbital energies, and form, facilitating the design of new molecules with the appropriate reactivity. COMPUTATIONAL CHEMISTRY AND MOLECULAR MODELING The computational chemistry subgroup of Vision 2020 under the sponsorship of the CCR has outlined a set of computational "grand challenges" or "technology bundles" that will have a dramatic impact on the practice of chemistry throughout the chemical enterprise, especially the chemical industry.
|
From page 77... ...
For example, chemical reactions that occur on a surface may be influenced not only by the local site but also by distant sites that affect the local electronic structure or the surrounding medium. Grand challenges C and D in Box 6.1 are tightly coupled but are separated here because different computational aspects may be needed to address these areas.
|
From page 78... ...
Examples of complex phenomena that are just now being considered include the effects of turbulence and chaotic dynamics on the reactor system. A key role of computational chemistry is to provide input parameters of increasing accuracy and reliability to the process simulations.
|
From page 80... ...
Methodologies for implementing and modifying data analysis "on-the-fly" must be developed and refined. The question of reaching macroscopic time scales from molecular dynamics simulations cannot be solved solely by increases in hardware capacity, since there are fundamental limitations on how many time steps can be executed per second on a computer, whether parallel or serial.
|
From page 81... ...
It is the issue of operating systems, especially for large-scale batch computing, that is likely to hold up the ability to broadly address the computational grand challenge issues raised above. In summary, rapid advances on many fronts suggest that we will be able to address the complex computational grand challenges outlined above.
|
From page 82... ...
A new generation of model-based control theory has emerged during the past decade that is tailored to the successful operation of modern plants, addressing the "difficult" process characteristics encountered in chemical plants shown in Box 6.4. These advanced algorithms include model predictive control (MPC)
|
From page 83... ...
VISION 2020: COMPUTATIONAL NEEDS OF THE CHEMICAL INDUSTRY 83 Unmodeled/unmeasured disturbances Control objectives State variables Modeled, unmeasured disturbances MODEL-BASED CONTROLLER STATE AND DISTURBANCE ESTIMATOR Manipulated variables PROCESS A _ · Modeled/measured disturbances FIGURE 6.2 Generalized block diagram for model predictive control.
|
From page 84... ...
To determine highly promising new directions for methodological developments and application areas. The workshop emphasized future development and application in eight areas:3 · Molecular Characterization and Separations, · Nonlinear Model Predictive Control, · Information and Data Handling, · Controller Performance Monitoring, 2Material from the workshop will appear in Vol.
|
From page 85... ...
The improved modeling paradigms should address model reduction techniques, low-order physical modeling approaches, maintenance of complex models, and how common model attributes contribute pathological features to the corresponding optimization problem. Hybrid modeling, which combines fundamental and empirical models, and methodologies for development of nonlinear models (e.g., input sequence design, model structure selection, parameter adaptation)
|
From page 86... ...
Clearly the supply chain is a highly complex dynamic system. Nonetheless, the vision proposed for the operational domain is that in 2020 the success of a chemical enterprise will depend upon how effectively it generates value by dynamically optimizing the deployment of its supply chain resources.
|
From page 87... ...
To allow the vision of the dynamically optimized supply chain to be realized under each of these factors, innovations extending beyond developments in information and computing technology alone are required. However, it is clear that the infrastructure for storing and sharing information and technical computing tools that exploit such information constitute the key enabling technology.
|
From page 88... ...
The decision support tools for the process include streamlined modeling methodology, multi-view systems for abnormal situation management, nonlinear and adaptive model predictive control, and process optimization using dynamic, and especially hybrid, models. Model building is generally perceived to be a key stumbling block because of the level of expertise required both to formulate process _ .
|
From page 89... ...
The role of abnormal situation management systems is to identify plant trends, to diagnose likely causes and consequences, and to provide intelligent advice to plant personnel. While components that address portions of this entire process have been under investigation for the past decade, full integration of the various qualitative and quantitative support tools remains to be realized.
|
From page 90... ...
The objective is to expand the envelope beyond the process itself and to encompass the business processes that are essential to driving manufacturing and the entire supply chain. The tools include improved sales and market forecasting methodologies, supply and logistics planning techniques, methodologies for quantitative risk assessment, optimization-based plant scheduling methods, business modeling frameworks, and approaches to dynamic supply chain optimization.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.