91成人短视频

Session Chair:

• John Bomberger, DuPont

Session Description:

Models are a vital part of process development, and are often employed in a number of important process development steps, including scale up, cost estimating, process optimization and energy integration, and control strategy development and testing. Models may be implicit in the computations used in process development, or can be explicitly constructed for purpose. They may also take a number of forms, like kinetic expressions, steady-state process simulations, dynamic models of unit operations or processes, or CFD models for mixing and heat transfer. This session will focus on practical applications of modeling and computation used in process development.

Schedule:

Abstracts:

Virtual Design and Commissioning: Solving Tomorrow鈥檚 Problems Today

Tracy Clarke-Pringle* and Nick Hernjak, DuPont

DuPont has a long history of using dynamic, plant-wide models to understand process operability, improve existing control strategies and train operators. However, the earlier a dynamic model is developed and used, the more value one can extract from it. In DuPont, a growing area for the application of these 鈥淰irtual Plants鈥� is in Process Development 鈥� understanding how the proposed process is going to run, BEFORE it is built. By exploring the future process in a Virtual Plant today, problems can be identified and remediated quickly, and at a much lower expense. Valuable insight gained can be used to design a more operable and robust process.

DuPont (and former DuPont entities) deliver the value of dynamic models by leveraging our in-house simulation software, DuPont鈩� TMODS. This talk will discuss recent case studies in which Virtual Plants have been used to support process development activities.

Optimizing the Conceptual Process Design of an Entire Plant Using Multi-Scale Models

Pieter Schmal, PSE

Many of the important decisions during the design of a new plant are made in the early stages. Yet, models used during FEED are typically standard flowsheet models. Furthermore, optimization of the design is often done by manual trial and error or, if a formal optimization is performed, only a limited number of design decisions and constraints is typically considered. Although in many cases the models are adequate for the purpose at hand, even properly optimized designs using simplified/standard models may lead to poor results. In a similar fashion setting up a simplified optimization problem typically results in a suboptimal solution. In this presentation we will demonstrate the importance of using detailed, multi-scale models in an optimization with 49 decision variables on an industrial case study. We will compare the results with using simplified models and trial-and-error optimization. We will demonstrate how uncertainty in some of the parameters can have a significant effect on ROI/IRR and how this uncertainty can be assessed. Finally, some new developments for simultaneous product and process design as well as structured product process design are discussed.

Bioprocesses Development Using Batch Processes Simulation - Application Examples with BatchReactor庐

O. Baudouin¹, S. D茅chelotte¹, P. Guittard¹, R. Sardeing¹, and B. Wincure*²

¹ProSim SA, Immeuble Strat猫ge A, 51 rue Amp猫re, F-31670 Lab猫ge, France

² ProSim, Inc., 325 Chestnut street, suite 800, Philadelphia, PA 19106, USA

Process simulation is widely used by process engineers in the design of new units as well as in operations of existing plants for process optimization, units troubleshooting or debottlenecking, plants revamping, performing front-end engineering analysis鈥� It is used in nearly all process industries: chemicals, pharmaceuticals, petrochemicals, oil and gas, refining, specialty chemicals鈥� However, bio-industries still do not use intensively process simulation. In these industries, microorganisms (fermentation), enzymatic reactions or reactive transformation of the plant material are involved. They are characterized by a great complexity of the raw material and the phenomena which are taking place in reactive unit operations. The number of components (often non-exhaustive for a plant material) and the number of reactions (several thousand for a given microorganism) limit the number of robust reactional models available in literature. As they most often operate in batch mode, bio-reactions are rarely present and used in process simulation software.

BatchReactor庐 is a software dedicated to the simulation of batch chemical reactors [1], which provides the user with a detailed modeling of the reactor (heating/cooling system, condenser, mixing device鈥�) and a reliable description of the production recipe. BatchReactor庐 allows modeling of complex systems by taking advantage of the power of Simulis庐 Thermodynamics (thermodynamic properties server) which offers an extensive set of thermodynamic models with a pure components database of more than 2,000 pure components (AIChE鈥檚 DIPPR庐 database [2]). Additional new components can be easily added in the database as, for example, microorganisms represented with a CHONPS formula. Moreover, thanks to the user 鈥渋nterpreted鈥� kinetic rate model available through Simulis庐 Reaction (chemical reaction server), any kind of specific kinetic models can be used in BatchReactor庐. This is essential to represent kinetic bio-reactions with often non-classical laws [3]. So, in BatchReactor庐, a stoichio-kinetic model can be coupled with thermodynamic and heat transfer models already available in the software to simulate the bio-reactor.

Several applications from various industries have been successfully simulated: food industry (tomato juice oxidation, brewing of beer, alcohol fermentation), pharmaceutical (production of thiolutin) and white biotechnology (production of polyhydroxybutyrate). A stoichio-kinetic model was necessary for all these applications but, once it was known, the strengths of a BatchReactor simulation appeared: accurate thermodynamic and transfer phenomena representation, recipe description, user-friendly interface.

References

[1]

[2] R.L. Rowley, W.V. Wilding, J.L. Oscarson, N.F. Giles, DIPPR庐 Data Compilation of Pure 91成人短视频 Properties, Design Institute for Physical Properties, AIChE, New York, NY (2011)

[3] Ramon Portugal F., Fillon M., Meyer X.M., Pingaud H., Strehiano P., I.S.B.N. 2-87805-022-3, 159- 170 (1997)