Chemstations Inc.

Houston, TX, United States

Chemstations Inc.

Houston, TX, United States
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Hayashi H.,Tokyo Gas Co. | Hayase K.,Tokyo Gas Co. | Matsuoka K.,Yokogawa Electric Corporation | Hill D.,Chemstations Inc.
10AIChE - 2010 AIChE Spring Meeting and 6th Global Congress on Process Safety | Year: 2010

The Tokyo Gas Corp's Sodegaura Terminal is now one of the world's largest receiving terminals in the world. After the LNG is regasified, it is delivered to customers in the Tokyo cosmopolitan area via high pressure gas pipelines. From an economical and global warming point of view, the plant's operations and controls should have a minimal effect on the environment. This can be accomplished by limiting amounts of natural gas expelled into the atmosphere. The existing pressure control valve (CV) used for start-ups and emergency cases is being replaced. Fine-tuning of PID control parameters will be required. Proper tuning also minimized the amount of LNG vented during startup operations. The techniques used to examine controls scenarios in the Tokyo Gas Corp's Sodegaura Terminal project are presented. A "virtual plant" in a software model is developed. Both the validity of the plant engineering design, and the chance to obtain an optimum adjusted parameter, are increased due to the use of a virtual plant environment for parameter tuning. This is an abstract of a paper presented at the AIChE 2010 Spring National Meeting (San Antonio, TX 3/21-25/2010).

Hill D.,Chemstations Inc. | Matsuoka K.,INPEX Corporation
Chemical Engineering | Year: 2010

One of the best ways to realize value from your process data is to use those data - both real-time and historical - in a process simulator. The many applications for doing so are generally categorized into offline and online categories. A discussion covers process simulators; modern distributed control systems; reasons for integrating process simulators with control system data; example applications, which include a natural gas pipeline that had two looped branches and additional third loop to expand capacity, a chemical producer that integrated a steady-state simulator as a software sensor for its control system, and a company that wants to calculate fouling factors throughout a heat exchanger network; determination of capabilities; and implementation of a data connection.

Hill D.,Technical Support Inc. | Herrick A.,Chemstations Inc. | Matsuoka K.,INPEX Corporation
11AIChE - 2011 AIChE Spring Meeting and 7th Global Congress on Process Safety, Conference Proceedings | Year: 2011

Chemstations has advised on several modeling projects for LNG processes. Dynamic simulation has helped engineers gather insight into such processes. A discussion of scenarios was put forward, including what was achieved by the engineers modeling them. This is an abstract of a paper presented at the 2011 AIChE Spring Meeting & 7th Global Congress on Process Safety (Chicago, IL 3/13-17/2011).

Abrol S.,University of Texas at Austin | Lu M.,Chemstations Inc. | Hill D.,Chemstations Inc. | Herrick A.,Chemstations Inc. | Edgar T.F.,University of Texas at Austin
Industrial and Engineering Chemistry Research | Year: 2010

In situ adaptive tabulation (ISAT) is applied to dynamic process simulators for reducing computational run-time. Several enhancements of previous approaches are presented here, including a method for estimating the sensitivities using input-output data, along with different strategies for record distribution. A modified version of the original algorithm (mISAT) to improve performance of ISAT is also suggested. Case studies for first-principles and data-driven models using ISAT are performed to generate accurate trajectories, which are essentially the same as those obtained by direct integration. Computational speed-up using ISAT is also shown for these studies. © 2010 American Chemical Society.

Vahid A.,University of Akron | Gray N.H.,Chemstations Inc. | Elliott J.R.,University of Akron
Macromolecules | Year: 2014

Polymeric mixtures of hydrocarbons and alcohols have been simulated with discontinuous potential models to characterize the Helmholtz energy of the repulsive reference fluids. This quantity is equivalent to the athermal mixture entropy. The reference compressibility factor and Helmholtz free energy have been correlated for various molecular structures from single to infinite chain lengths. The mixtures included small n-alkanes, branched alkanes, aromatics, and alcohols, with polymeric molecules of: n-alkanes, ethyl-styrenes, ethyl-propylenes, and isoprenes. We find that the athermal entropy of mixing at constant packing fraction deviates significantly from ideality as the volume ratio increases, but the nonideality is fairly insensitive to structural details like branching and rings. Volume ratio alone does not provide a complete characterization, however. For example, a mixture of C40 and C80 would yield a small deviation whereas a mixture of C2 and C4 would provide a relatively large deviation. This observation leads to the introduction of a characteristic parameter in terms of entropy density, designated as an entropic solubility parameter. In both ideal and nonideal solutions, the trends still follow van der Waals (vdW) mixing. This leads to an accurate characterization of the entropic contribution to the χ parameter (χS) of Flory-Huggins theory for mixtures of all sizes, shapes, and compositions of molecular structures. A general rule is developed for predicting the athermal entropy of mixing based on knowledge of the volume ratios and entropic solubility parameter of the constituent molecules. The simulations are compared to Flory-Huggins (FH), group contribution lattice fluid theory (GCLF), statistical associating fluid theory (SAFT), Sanchez-Lacombe (SL), and Guggenheim-Staverman (GS) theories of polymer chains. © 2014 American Chemical Society.

Reed W.Y.,Chemstations Inc. | Schwab S.,Chemstations Inc.
18th Topical Conference on Refinery Processing 2015 - Topical Conference at the 2015 AIChE Spring Meeting and 11th Global Congress on Process Safety | Year: 2015

Advances in computing speed continue to enable dynamic modeling of ever increasing complexity. One example of this includes simulation of non-routine conditions such as startup of distillation columns. A refining column case study will be reviewed to point out guidelines for use when setting up these types of models.

Herrick A.,Chemstations Inc.
AIChE Annual Meeting, Conference Proceedings | Year: 2013

Design and scale-up of new batch processes presents a unique set of challenges versus design of continuous processes. One key difference is optimization of the time required for each process step. Rules of thumb are often used due to lack of data or tools. Improvements in dynamic simulation software have allowed for more rigorous modeling of increasingly complex systems and scenarios. Modeling of batch process steps in the design phase can highlight potential problem areas such as heat balance limitations, short residence times, reversal of flows, and pressure/flow dependencies. This allows for efficient optimization of step scheduling using the latest dynamic simulation techniques. Examples of batch designs are presented using CHEMCAD simulation software. A new method is included for simplification of handling any number of process steps and control elements.

Hill D.,Chemstations Inc. | Justice F.C.,Chemstations Inc.
Chemical Engineering Progress | Year: 2011

An overview of applied thermodynamics that can help researchers select the right model for their process simulation is presented. Process simulation software calculates heat and material balances. This requires determining the separation of a chemical mixture between the liquid and vapor phases. While many different thermodynamic models are available, using the wrong model, or estimating a physical property incorrectly, can lead to inaccurate results for the overall simulation. Some K models were derived from physical laws, some from thermodynamic constraints, and some simply fit to observed data. Understanding the behavior of molecules in the vapor and liquid phases can help one determine which K model category might be appropriate for one's mixture. K model falls into several general categories: equation of state, activity coefficient, combined and special-purpose, and aqueous electrolyte system models.

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