Dynamics of the wet-end section in paper mills

A model representing the wet-end section of a paper mill has been developed to characterize its dynamic behavior during the grade change. The model is based on the mass balance relationships written for the simplified wetend white water network. From the linearization of the dynamic model, higher-order Laplace transfer functions were obtained followed by the reduction procedure to give simple lower-order models in the form of 1^st-order or 2^nd-order plus dead times. The dynamic response of the wet-end is influenced both by the white water volume and by the level of wire retention. Effects of key manipulated variables such as the thick stock flow rate, the ash flow rate and the retention aid flow rate on the major controlled variables were analyzed by numerical simulations. The simple dynamic model developed in the present study can be effectively used in the operation and control. This paper is dedicated to Professor Se Ki Moon on the occasion of his retirement from Hanyang University.     

Updating the trusted connection of re-organized computing resource under the automated system management platform

The High-Performance Computing (HPC) is an infrastructure to support various research fields. The research using HPC requires big storage for large-scale of raw data and the huge computing resources to analyze. The consolidated operations of the computing center for supporting various researches using HPC are a very effective organization. The consolidated operations are able to increase the utilization. The computing center has to be re-organized continuously in order to support the various fields of research effectively and flexibly. Re-organization to reuse the limited resources enforces the update of the information for verification of the computing resources after the re-allocation of computing resources and the re-distribution of trusted connection between computing resources. Consequently, to maintain effective research support we need automated resource re-organization environment. In this paper, we build an automated update of the trusted connection for re-organized computing resource using puppet which is automated system management platform. Finally, we provide a unified verification point and efficient environment for the re-allocation and re-organization of the integrated computing center.     

Development of modified stokes expression to model the behavior of expanded beds containing polydisperse resins for protein adsorption

Expanded bed behavior was modeled by using the Richardson-Zaki correlation between the superficial velocity of the feed stream and the void fraction of the bed. A polydisperse material, Chelating excellose® (70-210 Μm in diameter, 1.21 g/cm³ in density), which has Ni²⁺ ions for the selective binding of histidine-tagged proteins, was used as the resin. A method to modify the Stokes expression to express the terminal settling velocity of the resins by introducing two empirical parameters, the effective diameter of the resins and an exponent for(ρ _p -ρ)/Μ term, was developed. Combined use of the Richardson-Zaki correlation and the modified Stokes expression was successful in modeling the bed expansion by incorporating physical properties of feed streams and the resins.     

On-line identification of interacting two-tank system

In order to demonstrate the effectiveness of the process identification algorithm, on-line parameter estimator is evaluated experimentally by using two-tank system with interaction. On-line parameter estimator used in this paper is based on a recursive parameter estimation algorithm. MIMO linear, bilinear and quadratic models based on ARMA model are used to identify two-tank system. A quadratic model for two-tank system with interaction is developed to confirm the propriety of MIMO quadratic model used in identification of two-tank system. The results of on-line identification experiments on the two-tank system show that the estimated parameters of each model converge and the output tracking errors are bounded by disturbance bound. But, the quadratic model showed the best convergence.     

Model algorithmic control of grade change operations in paper mills

In this work, the Model Algorithmic Control (MAC) method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first and the impulse model is extracted from the neural network model. Results of simulations for MAC control of grade change operations are compared with plant operation data. The major contribution of the present work is the application of MAC in the industrial plants based on the identification of neural network models. We can confirm that the proposed MAC method exhibits faster responses and less oscillatory behavior compared to the plant operation data in the grade change operations.     

Kinetics of volume expansion during infusion of Ringer’s solution based on two volume model

In this work mathematical models were developed to represent the kinetics of volume changes of fluid spaces associated with infusion of Ringer’s solution. During infusion of Ringer’s solution, the human body is assumed to be characterized by the two-fluid space model which has second volume space in addition to the first volume so that fluid exchanges between these two spaces are possible. Various infusion types were tested to accommodate different medical situations. Volunteers were given Ringer’s solution and the changes in blood hemoglobin were detected. From the comparison with experimental data, the two-fluid space model was found to represent adequately the kinetics of human volume expansion during infusion of Ringer’s solution.     

A kinetic study on medium temperature desulfurization using a natural manganese ore

Natural manganese ores were selected as raw materials for the desulfurization sorbent because of economical efficiency and high reactivity on hydrogen sulfide. Initial reaction rates between H₂S and desulfurization sorbent of natural manganese ores were determined in a temperature range of 400-800°C using a thermobalance reactor. All reactions were first order with respect to H₂S and were expressed by the Arrhenius relation. When the sulfidation reaction was controlled by diffusion, the temperature dependence of the effective diffusivity was given by the Arrhenius equation. Activation energies and frequency factors were obtained from the product layer diffusion coefficient of various sorbents by plotting as an Arrhenius equation form. Several additives were mixed to improve the sulfidation capacity, and NiO was the best additive.     

Changes in ether-linked phospholipids in rat kidney by dietary α-linolenic acidin vivo

We investigated the effects of perilla oil containing a high level of α-linolenic acid onin vivo phospholipid metabolism, particularly three subclasses of choline glycerophospholipids (CGP) and ethanolamine glycerophospholipids (EGP), in rat kidney. After three weeks of feeding, a significantly lower proportion (by 35%) of the alkylacyl subclass of CGP was found in the perilla oil, as compared to corn oil-fed animals. The alkylacyl species of EGP was also higher in the perilla oil than in the corn oil-fed animals. These alterations were accompanied by a remarkably lower proportion of arachidonic acid and a higher level of eicosapentaenoic acid (EPA) in all six subclasses of CGP and EGP in the perilla oil-fed animals. The levels of linoleic acid were even higher in the diacyl subclasses of CGP and EGP in the perilla oil group, suggesting that desaturase and elongase enzymes prefer n−3 to n−6 fatty acids as substrates for diacyl species. These data are useful in defining the effects of α-linolenic acid on the biosynthesis of renal phospholipids and on the replacement of n−6 with n−3 fatty acids in the six CGP and EGP subclasses.     

Development of a Data‐Driven Framework for Real‐Time Travel Time Prediction

Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long‐term prediction in a real‐time manner have been lacking. Existing methods do not fully utilize the advantages of the state‐of‐the‐art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real‐time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long‐term (at least 6‐hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k‐nearest neighbor (Mk‐NN) method which is compared with the conventional k‐NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long‐term travel time with shorter computation time.