An integrated qualitative and quantitative modeling framework for computer‐assisted HAZOP studies

The article proposes a novel practical framework for computer‐assisted hazard and operability (HAZOP) that integrates qualitative reasoning about system function with quantitative dynamic simulation in order to facilitate detailed specific HAZOP analysis. The practical framework is demonstrated and validated on a case study concerning a three‐phase separation process. The multilevel flow modeling (MFM) methodology is used to represent the plant goals and functions. First, means‐end analysis is used to identify and formulate the intention of the process design in terms of components, functions, objectives, and goals on different abstraction levels. Based on this abstraction, qualitative functional models are constructed for the process. Next MFM‐specified causal rules are extended with systems specific features to enable proper reasoning. Finally, systematic HAZOP analysis is performed to identify safety critical operations, its causes and consequences. The outcome is a qualitative hazard analysis of selected process deviations from normal operations and their consequences as input to a traditional HAZOP table. The list of unacceptable high risk deviations identified by the qualitative HAZOP analysis is used as input for rigorous analysis and evaluation by the quantitative analysis part of the framework. To this end, dynamic first‐principles modeling is used to simulate the system behavior and thereby complement the results of the qualitative analysis part. The practical framework for computer‐assisted HAZOP studies introduced in this article allows the HAZOP team to devote more attention to high consequence hazards. © 2014 American Institute of Chemical Engineers AIChE J 60: 4150–4173, 2014     

Syngas chemical looping process: Dynamic modeling of a moving‐bed reducer

The syngas chemical looping process coproduces hydrogen and electricity with iron oxide based oxygen carriers in a circulating moving bed system. In this article, a one‐dimensional (1‐D) dynamic model is developed to simulate the countercurrent gas–solid reactive flow in the moving‐bed reducer. This model is validated by TGA and bench‐scale experiments. Both the steady state and dynamic composition profiles are obtained to help understand the reaction and reactor behaviors. Numerical simulation on the effects of reactor length is conducted to optimize the moving‐bed reducer design. It is also found that minor variations in the feed rate ratio near a critical point that is represented by the reaction equilibrium could yield a significant difference in the time required for the reactions to reach a steady‐state operation. Such a difference has an important practical implication in that the moving‐bed reducer should be designed and operated to circumvent the critical point. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3432–3443, 2013     

Determination of anisidine value in thermally oxidized palm olein by fourier transform infrared spectroscopy

Fourier transform infrared (FTIR) spectroscopy with transmission cell is described to predict anisidine value of palm olein. The calibration set was prepared by mixing the thermally oxidized palm olein and the unoxidized palm olein with certain ratios (w/w) covering a wide range of anisidine values. A partial least square (PLS) regression technique was employed to construct a calibration model. This model was further accomplished by a validation step. The standard error of prediction found was 0.51. The precision of this method was shown to be comparable to the accuracy of the American Oil Chemists’ Society method used for measurement of anisidine value, with coefficient of determination (R ²) of 0.99. The study showed that mid-band FTIR spectroscopy combined with a PLS calibration technique is a versatile, efficient, and accurate technique for the estimation of anisidine value of palm olein within about 2 min with less than 2 mL of sample.     

Nonparametric Autoregression with Multiplicative Volatility and Additive mean

For over a decade, nonparametric modelling has been successfully applied to studying nonlinear structures in financial time series. It is well known that the usual nonparametric models often have less than satisfactory performance when dealing with more than one lag. When the mean has an additive structure, however, better estimation methods are available which fully exploit such a structure. Although in the past such nonparametric applications had been focused more on the estimation of the conditional mean, it is equally if not more important to measure the future risk of the series along with the mean. For the volatility function, i.e. the conditional variance given the past, a multiplicative structure is more appropriate than an additive structure, as the volatility is a positive scale function and a multiplicative model provides a better interpretation of each lagged value's influence on such a function. In this paper we consider the joint estimation of both the additive mean and the multiplicative volatility. The technique used is marginally integrated local polynomial estimation. The procedure is applied to the deutschmark/US dollar daily exchange returns.     

Combined experimental and computer simulation study of the kinetics of solute release from a relaxing swellable polymer matrix. I. Characterization of non‐Fickian solvent uptake

This article is concerned with a combined experimental and computer modeling study aimed at the characterization of the non‐Fickian absorption of liquid water by cellulose acetate (CA) matrices (in the form of thin films) on the basis of a strictly limited number of parameters. The observed kinetics exhibited two distinct non‐Fickian features, resulting from short‐term and long‐term viscous structural relaxations of the swelling glassy polymeric matrix. These relaxation processes were modeled by the extension of a previous model of micromolecular diffusion in a polymer subject to a single relaxation process. The successful simulation of the experimental kinetic curves then enabled the determination and cross‐checking of the relevant model parameter values. This work had the dual purpose of providing (1) input related to solvent absorption for the quantitative computer simulation (in part II of this series) of a model monolithic, solvent‐activated, controlled‐release device, consisting of a swellable glassy polymeric matrix (CA) loaded with an osmotically active solute (NaCl), and (2) an example of a general strategy of extracting meaningful values of micromolecular diffusivity in glassy polymers from non‐Fickian absorption kinetic data. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 2458–2467, 2004     

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research,a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation.The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs.In this procedure,the Artificial Neural Network was trained,tested and validated using assay values obtained from exploratory drillholes.Next,the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively.Finally,the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model.The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades.The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement.It could also be an effective mineral reserve evaluation method that could produce optimum block model for mine design.     

Mathematical modeling of reverse osmosis systems

The present investigation pertains to modeling of seawater desalination system. A simulation model was developed and verified for a small-scale reverse osmosis system. The proposed model combines material balances on the feed tank, membrane module andproduct tank with membrane mass transfer models. Finally a comprehensive simulation model has been developed incorporating the effect of mass transfer inhibition The model is non-linear differential equation representing the feed concentration as a function of operating time and space. The solution of the simultaneous differential equations was obtained using the fourth order Runge-Kutta method, due to self starting and stability. The model was verified using the experimental data from the literature [17,24]. Parameter sensitivity was carried out to select the proper step size. The simulation was run for over 1000 11 enabling a prediction of operational performance at high overall system recoveries.     

Towards optimization of the osmotic drying process of alumina-gelatin objects: Regression analysis and verification

In this study, further analysis of the osmotic drying process was conducted to identify the optimum combination of parameters for drying rectangular alumina-gelatin beams. This study was designed to determine the effect of three variables related to the osmotic drying process (osmotic pressure, molecular weight, and immersion time) on the interaction between the liquid desiccant and the submerged alumina-gelatin samples. The water loss from the alumina-gelatin samples was positively correlated with the molecular weight, osmotic pressure, and immersion time. Up to 40% by weight of the initial water content was removed during the osmotic drying process. The samples also experienced solids gain due to the counterflow of solute from the liquid desiccant. The least amount of solids gain resulted from drying for the shortest immersion time at low osmotic pressure and high molecular weight. Evidence of possible interactions between variables was noted for the sintered density metric. Statistical methods were used to form regression equations for the measured responses (water loss, solids gain, bulk density). A verification experiment was conducted to compare the experimental outcomes to the predicted outcomes. The responses were simultaneously optimized to identify the combination of variable settings required to meet specified goals. In order to maximize water loss, minimize solids gain, and maximize bulk density, the ceramic-gelatin object should be immersed for approximately 60?min in an aqueous solution of 100,000?g/mol poly(ethylene oxide) at an osmotic pressure of 2.50?MPa. These values are valid for the range of parameter settings tested and the sample fabrication and drying methods used.     

Removal of Cu(II) from aqueous solutions by chelating starch derivatives

Two chemically modified starch derivatives, crosslinked amino starch (CAS) and dithiocarbamates modified starch (DTCS), were prepared and used for the removal of Cu(II) from aqueous solutions. CAS was found to be effective for the adsorption of Cu(II), which tended to form a stable amine complex. Adsorption of Cu(II) onto DTCS was higher than that onto CAS. Experiments showed that the adsorption processes of Cu(II) on both CAS and DTCS were endothermic, and followed Freundlich isothermal adsorption. For both adsorbents, dynamic modeling of their adsorption showed that the first‐order reversible kinetic model described the adsorption process. The adsorption rate constants of CAS and DTCS were 1.578 and 10.32 h^?1, respectively. From the results of the thermodynamic analysis, free energy ΔG, enthalpy ΔH, and entropy ΔS of the adsorption process were calculated. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 3881–3885, 2004