Applied thermodynamics for process modeling

Abstract

IntroductionT he process industries spend an estimated $500 billion annually worldwide in conceptual design, process engineering, detailed engineering, construction, startup, plant operations, and maintenance for chemical, refining, polymer and power plants. In order for chemical engineers to successfully execute these process and product studies, they perform process modeling and capture knowledge of the thermodynamic properties and phase behavior of the chemical systems they work with.Process modeling is a key enabling technology for process development and design, equipment sizing and rating, and process debottlenecking and optimization. More recently, process modeling has enabled offline dynamic simulation for controllability studies, operator training simulators, online model-based process sensors, stateestimation, look-ahead predictors, and online process control and optimization. Success in process modeling is critically dependent upon accurate descriptions of the thermodynamic properties and phase behavior of the concerned chemical systems. A perspective is offered here on applied thermodynamics from an industrial viewpoint. Industry Uses Thermodynamic InnovationsIndustry uses a wide array of thermodynamic innovations: engineering correlations, reference quality models, estimation methods, databanks, and flash algorithms.• Chemical engineers benefit most from models and correlations that capture the dominant physical and chemical behavior of chemical systems. Engineers use these correlative models within a thermodynamic modeling framework to describe and validate available data and to extrapolate with reasonable confidence outside the range of available data.• For commonly encountered systems such as water and steam, air, ammonia, and light hydrocarbons, comprehensive experimental data are available and accurate description is essential and feasible. Highly parameterized models are accepted and useful if they represent available data within experimental accuracy. These reference quality models suffer the disadvantage, however, of not easily allowing incorporation of additional components into the system being modeled.• Engineers frequently lack either experimental data or expertise to develop and validate models. As a result, they often rely on estimation techniques such as group-contribution methods.• Engineers need databanks that are compilations of validated experimental data and model parameters for pure component and mixture properties. Databanks and correlations of known accuracy play key roles in engineering calculations.• The value of thermodynamic models is especially evident in "flash" calculations. Robust and computationally efficient flash algorithms for a variety of phase equilibrium and chemical equilibrium conditions are an integral part of the practice of applied thermodynamics. Practicing engineers prefer "simple and intuitive" thermodynamic models that can be applied easily. Models that are constantly being revised, sophisticated theories requiring expert users, models with excessive com...

Thermodynamics of CO2-MDEA using eNRTL with Differential Evolution Algorithm

“…The major problem concerning the VLE measurements of aqueous alkanolamine-acid gas systems, in general, is that lack of consistency and regularity in the numerous published values. Excess Gibbs energy-based activity coefficient models provide a practical and rigorous thermodynamic framework to model thermodynamic properties of aqueous electrolyte systems, including aqueous alkanolamine systems for CO 2 capture (Chen & Mathias, 2002, Chen, 2006. Austgen et al (1991) and Posey (1996) applied the electrolyte NRTL model (Song & Chen, 2009;Chen & Evans, 1986;Chen et al, 1982) to correlate CO 2 solubility in aqueous MDEA solution and other aqueous alkanolamines.…”

GE Models and Algorithms for Condensed Phase Equilibrium Data Regression in Ternary Systems: Limitations and Proposals

“…Simple thermodynamic models are preferred for industrial applications versus more sophisticated models with a high number of parameters, unless a clear advantage is evident [35]. For example, Chen and col. [39] correlated the LLE data of some quaternary systems using ternary and quaternary interaction parameters in an extension of the UNIQUAC model, which was not able to reproduce the quaternary systems only with binary data.…”

“…Advantages of global versus local correlations for separation equipment design calculations are evident, since parameters are valid for all the composition space. A model that provides an accurate description of the thermodynamic properties of a system, in the entire diagram, facilitates the plant design improvement strategies, the solution of problems and the process control [35].…”

Industrial perspective

“…Traditional methods for physical property modeling are well understood (Chen and Mathias, 2002) and will not be considered in this paper. Instead, we will briefly consider recent and current developments in six areas that we believe will have a significant impact on the practice of physical property modeling over the next several years.…”

“…It has impacted process modeling in a number of ways: 1) help elucidate fundamental physical and chemical interactions and support development of new theories and models, 2) complement experiments for data generation especially for systems that are not readily amenable to existing experimental procedures, and 3) provide an alternative approach to extend and improve on existing applied thermodynamic models (Chen and Mathias, 2002).…”