Hybrid simulation-equation based synthesis of chemical processes

A challenging problem in the synthesis and design of chemical processes consists of dealing with hybrid models involving process simulators and explicit constraints. Some unit operations in modular process simulators are slightly noisy or require large CPU times to converge. In this work, this problem is addressed by combining process simulators and surrogate models. We have replaced some unit operations, which cannot be used directly with a gradient-based optimization, by surrogate models based on Kriging interpolation. To increase the robustness of the resulting optimization model, we perform a degree of freedom analysis and aggregate (or disaggregate) parts of the model to reduce the number of independent variables of the Kriging surrogate models (KSMs). Thus, the final model is composed of KSMs, unit operations (maintained in the process simulator) and also explicit equations. The optimization of the well-known vinyl chloride monomer (VCM) production process is performed to test the proposed approach. The effect of the heat integration is also studied. In addition, the economic feasibility of the optimized process is calculated assuming uncertainty in raw material and product prices.The authors gratefully acknowledge the financial support by the Ministry of Economy and Competitiveness from Spain, under the project CTQ2016-77968-C3-02-P (AEI/FEDER, UE), and Call 2013 National Sub-Program for Training, Grants for pre-doctoral contracts for doctoral training (BES-2013-064791)

Profitability, reliability and condition based monitoring of LNG floating platforms: a review

The efficiency and profitability of Floating, Production, Storage and Offloading platform (FPSO) terminals depends on various factors such as LNG liquefaction process type, system reliability and maintenance approach. This review is organized along the following research questions: (i) what are the economic benefit of FPSO and how does the liquefaction process type affect its profitability profile?, (ii) how to improve the reliability of the liquefaction system as key section? and finally (iii) what are the major CBM techniques applied on FPSO. The paper concluded the literature and identified the research shortcomings in order to improve profitability, efficiency and availability of FPSOs

Heat Transfer Mechanism In Particle-Laden Turbulent Shearless Flows

Particle-laden turbulent flows are one of the complex flow regimes involved in a wide range of environmental, industrial, biomedical and aeronautical applications. Recently the interest has included also the interaction between scalars and particles, and the complex scenario which arises from the interaction of particle finite inertia, temperature transport, and momentum and heat feedback of particles on the flow leads to a multi-scale and multi-physics phenomenon which is not yet fully understood. The present work aims to investigate the fluid-particle thermal interaction in turbulent mixing under one-way and two-way coupling regimes. A recent novel numerical framework has been used to investigate the impact of suspended sub-Kolmogorov inertial particles on heat transfer within the mixing layer which develops at the interface of two regions with different temperature in an isotropic turbulent flow. Temperature has been considered a passive scalar, advected by the solenoidal velocity field, and subject to the particle thermal feedback in the two-way regime. A self-similar stage always develops where all single-point statistics of the carrier fluid and the suspended particles collapse when properly re-scaled. We quantify the effect of particle inertial, parametrized through the Stokes and thermal Stokes numbers, on the heat transfer through the Nusselt number, defined as the ratio of the heat transfer to the thermal diffusion. A scale analysis will be presented. We show how the modulation of fluid temperature gradients due to the statistical alignments of the particle velocity and the local carrier flow temperature gradient field, impacts the overall heat transfer in the two-way coupling regime

Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model

Modeling viral dynamics in HIV/AIDS studies has resulted in a deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral dynamics models based on nonlinear differential equations have been proposed and well developed over the past few decades. However, it is quite challenging to use experimental or clinical data to estimate the unknown parameters (both constant and time-varying parameters) in complex nonlinear differential equation models. Therefore, investigators usually fix some parameter values, from the literature or by experience, to obtain only parameter estimates of interest from clinical or experimental data. However, when such prior information is not available, it is desirable to determine all the parameter estimates from data. In this paper we intend to combine the newly developed approaches, a multi-stage smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear differential equation model. In particular, to the best of our knowledge, this is the first attempt to propose a comparatively thorough procedure, accounting for both efficiency and accuracy, to rigorously estimate all key kinetic parameters in a nonlinear differential equation model of HIV dynamics from clinical data. These parameters include the proliferation rate and death rate of uninfected HIV-targeted cells, the average number of virions produced by an infected cell, and the infection rate which is related to the antiviral treatment effect and is time-varying. To validate the estimation methods, we verified the identifiability of the HIV viral dynamic model and performed simulation studies.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS290 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org