An object-oriented framework for modular chemical process simulation with semiconductor processing applications

Chen, Jing; Adomaitis, Raymond A.

doi:10.1016/j.compchemeng.2006.03.002

Cited by 15 publications

(7 citation statements)

References 47 publications

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“…However, each component and element of the network is well annotated and described by appropriate constitutive equations. Each element of the network can be represented as an appropriate object in modular form (Chen, Adomaitis 2006), whereas the equation-oriented formalism enables the exact representation of each module by a set of detailed mathematical equations (Pattison, Baldea 2014). Assembling of individual models can easily be automated in a “drag-and-drop” manner and complex models can therefore be generated and analyzed in great detail.…”

Section: Qsp: the Frameworkmentioning

confidence: 99%

Quantitative Systems Pharmacology: A Framework for Context

Androulakis

2016

Curr Pharmacol Rep

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Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

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Section: Qsp: the Frameworkmentioning

confidence: 99%

Quantitative Systems Pharmacology: A Framework for Context

Androulakis

2016

Curr Pharmacol Rep

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“…Computing the AB limit‐cycle over the time period 0 ≤ t ≤ τ A + τ B requires one additional important criterion; that the state of the surface return to its initial condition at the end of the cycle t = τ A + τ B . The combination of initial conditions and the Equations 2 and 3 constitute a set of ordinary differential and linear algebraic equations; we reduce this set of equations to a larger set of (non)linear algebraic equations using a collocation procedure, solving the algebraic equations using the Newton‐Raphson method 35…”

Section: Limit‐cycle Solutionsmentioning

confidence: 99%

“…To compute gpc , we recall that the number of Al and O atoms deposited per unit area over one deposition cycle are N Al and N O atoms per nm 2 , respectively, and can compute these values by integrating the reaction rates over each half‐cycle using the quadrature weights of the collocation procedure,35 as in Equation 20. …”

Section: Limit‐cycle Solutionsmentioning

confidence: 99%

Modeling ALD Surface Reaction and Process Dynamics using Absolute Reaction Rate Theory

Travis

Adomaitis

2013

Chemical Vapor Deposition

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A physically based model of atomic layer deposition (ALD) surface reaction dynamics is developed and applied to alumina ALD with water and trimethylaluminum precursors. The time-dependent growth surface composition is modeled by computing the equilibrium precursor adduct surface concentrations during each half-reaction and the rate constants of the ligand exchange reactions using transition state theory. To describe the continuous cyclic operation of the deposition reaction system, a numerical procedure to discretize limit-cycle solutions is developed and used to distinguish saturating growth per cycle (GPC) from non-saturating (gpc) conditions. The transition between the two regimes is studied as a function of precursor partial pressure, exposure time, and temperature.

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“…MATLAB has been successfully applied to solve PSE problem, especially in the modelling and simulation parts, e.g. multivariate statistical process control for process fault detection and diagnosis,73–75 develop an object‐oriented framework for modular chemical process simulation with semiconductor processing applications 76…”

Section: Available Software Toolsmentioning

confidence: 99%

Software tools overview: process integration, modelling and optimisation for energy saving and pollution reduction

Lam

Klemeš

Kravanja

2011

Asia-Pacific J Chem Eng

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This paper provides an overview of software tools based on long experience and applications in the area of process integration, modelling and optimisation. The first part reviews the current design practice and the development of supporting software tools. Those are categorised as: (1) process integration and retrofit analysis tools, (2) general mathematical modelling suites with optimisation libraries, (3) flowsheeting simulation and (4) graph-based process optimisation tools. The second part covers an assessment of tools which enable the generation of new sustainable alternatives to adapt to the future needs. They deal with waste, environment, energy consumption, resources depletion and production cost constrains. The emphasis of the sustainable process design tools is largely on the evaluation of process viability under sustainable economic conditions, synthesis of sustainable process and supply chain process maintenance and life cycle analysis. Major software tools development and the potential of the research-based tools for sustainable process design task are overviewed in the concluding part. 

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An object-oriented framework for modular chemical process simulation with semiconductor processing applications

Cited by 15 publications

References 47 publications

Quantitative Systems Pharmacology: A Framework for Context

Quantitative Systems Pharmacology: A Framework for Context

Modeling ALD Surface Reaction and Process Dynamics using Absolute Reaction Rate Theory

Software tools overview: process integration, modelling and optimisation for energy saving and pollution reduction

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