• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.464-465
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• 2006

Monodispersed and nano-sized Cu powders were synthesized from copper sulfate pentahydrate $(CuSO_4{\cdot}5H_2O)$ inside a nonionic polymer matrix by using wet chemical reduction process. The sucrose was used as a nonionic polymer network source. The influences of a nonionic polymer matrix on the particle size of the prepared Cu powders were characterized by means of X-ray diffraction), scanning electron microscopy), and particle size analysis). The smallen Cu powders with size of approximately 100 nm was obtained with adding of 0.04M sucrose at reaction temperature of $60\;^{\circ}C$. The particle size of the Cu powders prepared by the reduction inside polymer network was strongly dependent of the sucrose content and reaction temperature.

• Polymer(Korea)
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• v.29 no.1
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• pp.1-7
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• 2005

Interpenetrating polymer network (PN) is a mixture of network polymers. The characteristics of IPN material is the control of morphology during the IPN synthesis. By controlling the relative kinetics of chemical reaction (as well as gellation) and phase separation, the morphology of IPN can be controlled to obtain materials with nano-scale domain and also the co-continuous phase. Other important advantage is the fact that the morphology is permanent due to the presence of the physical interlocking between the networks. The combination of hydrophilic polyurethane and hydrophobic polystyrene in IPN form provides enhanced blood compatibility due to the co-existence of the hydrophilic and hydrophobic domains in nano-scale on the surface. The reaction temperature, reaction pressure and the degree of crosslinking were varied during the IPN synthesis and the morphology and blood compatibility of the resulting IPN materials were studied.

• Proceedings of the Membrane Society of Korea Conference
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• 1996.10a
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• pp.86-87
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• 1996

IPN (Interpenetrating Polymer Network) is a mixture of two or more crosslinked polymers with physically interlocked network structures between the component polymers. IPN can be classified as an alloy of thermosets and has the characteristics of thermosets such as the thermal resistance and chemical resistance and also has the characteristics of polymer alloys with enhanced impact resistance and amphoteric properties. The physical interlocking during the synthesis restricts the phase separation of the component polymer with chemical pinning process, thus the control of morphology is possible through variations of the reaction temperature and pressure, catalyst concentration and crosslinking agent concentration. Finely dispersed domain structure can be obtained through IPN synthesis of polymer components with gross immiscibility. In membrane applications, particularly for the separation of liquid mixtures, crosslinked polymer component with specific affinity to the permeate is needed. With the presence of the permeant-inert polymer component, the mechanical strength and the selectivity of the membranes are enhanced by restricting the swelling of the transporting polymer component networks.

• Bulletin of the Korean Chemical Society
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• v.34 no.12
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• pp.3591-3596
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• 2013

The enantioselective aza-Morita Baylis Hillman reaction of nitroalkene and N-tosylimine catalyzed by thiourea-tertiary amine has been investigated using density functional theory. Enantioselectivity is dominated by the cooperative effect of non-covalent and weak covalent interactions imposed by different units of catalyst. As Lewis base, the tertiary amine unit activates nitroalkene via weak covalent bond. The weak covalent interaction orients the reaction in a major path with smaller variations of this bond. The aromatic ring unit activates N-tosylimine via ${\pi}-{\pi}$ stacking. The non-covalent interaction selects the major path with smaller changes of the efficient packing areas. Thiourea unit donates more compact H-bonded network for species of the major path. The calculated ee value in xylene solution phase (97.6%) is much higher than that in N,N-Dimethylformamide (27.2%). Our conclusion is also supported by NBO analysis.

• Macromolecular Research
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• v.16 no.5
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• pp.424-428
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• 2008

We prepared an ionic gel polymer electrolyte for dye-sensitized solar cells (DSSCs) without leakage problem. Triiodide compound (BTDI) was synthesized by the reaction of benzene tricarbonyl trichloride with diethylene glycol monotosylate and subsequent substitution of tosylate by iodide using NaI. Bisimidazole was prepared by the reaction of imidazole with the triethylene glycol ditosylate under strongly basic condition provided by NaH. BTDI and bisimidazole dissolved in an ionic liquid were injected into the cells and permeated into the $TiO_2$ nanopores. In situ crosslinking was then carried out by heating to form a network structure of poly(imidazolium iodide), thereby converting the ionic liquid electrolytes to a gel or a quasi-solid state. A monomer (BTDI and bisimidazole) concentration in the electrolytes of as low as 30 wt% was sufficient to form a stable gel type electrolyte. The DSSCs based on the gel polymer electrolytes showed a power conversion efficiency of as high as 1.15% with a short circuit current density of $5.69\;mAcm^{-2}$, an open circuit voltage of 0.525 V, and a fill factor of 0.43.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.282-285
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• 1996

In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.328-339
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• 2023

Methanol steam reforming (MSR) is a promising method for hydrogen supplying as a critical step in hydrogen fuel cell commercialization in mobile applications. Modelling and understanding of the reactor behavior is an attractive research field to develop an efficient reformer. Three-layer feed-forward artificial neural network (ANN) and Box-Behnken design (BBD) were used to modelling of MSR process using the Cu-SiO₂ aerogel catalyst. Furthermore, impacts of the basic operational variables and their mutual interactions were studied. The results showed that the most affecting parameters were the reaction temperature (56%) and its quadratic term (20.5%). In addition, it was also found that the interaction between temperature and Steam/Methanol ratio is important on the MSR performance. These models precisely predict MSR performance and have great agreement with experimental results. However, on the basis of statistical criteria the ANN technique showed the greater modelling ability as compared with statistical BBD approach.

• Tribology and Lubricants
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• v.38 no.4
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• pp.152-161
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• 2022

Chemical mechanical planarization (CMP) is a technology that planarizes the surfaces of semiconductor devices using chemical reaction and mechanical material removal, and it is an essential process in manufacturing highly integrated semiconductors. In the CMP process, a conditioning process using a diamond conditioner is applied to remove by-products generated during processing and ensure the surface roughness of the CMP pad. In previous studies, prediction of pad wear by CMP conditioning has depended on numerical analysis studies based on mathematical simulation. In this study, using an artificial neural network, the ratio of conditioner coverage to the distance between centers in the conditioning system is input, and the average conditioning density, standard deviation, nonuniformity (NU), and conditioning density distribution are trained as targets. The result of training seems to predict the target data well, although the average conditioning density, standard deviation, and NU in the contact area of wafer and pad and all areas of the pad have some errors. In addition, in the case of NU, the prediction calculated from the training results of the average conditioning density and standard deviation can reduce the error of training compared with the results predicted through training. The results of training on the conditioning density profile generally follow the target data well, confirming that the shape of the conditioning density profile can be predicted.

• Bulletin of the Korean Chemical Society
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• v.29 no.10
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• pp.1993-1997
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• 2008

In the present work, Diels-Alder reaction of cyclopentadiene with ethylacrylate has been carried out by using two types of mesoporous solid acid catalysts (Al-MCM-41, Al-MCM-48) with different pore structures. The specific topology of Al-MCM-48 (cubic Ia3d structure composed of two independent 3-D channel systems) exhibit higher activity and stereo-control than those of Al-MCM-41 (hexagonal packing of 1-D channels). The physical properties of Al-MCM-48 catalyst, such as high accessibility of reactants to the acid sites, spatial confinement in the nanoscopic reactors, and 3-D channel network structure that are effective adsorption and diffusion of reactants, play a crucial role in the present study.