• Journal of Biosystems Engineering
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• v.20 no.3
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• pp.250-261
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• 1995

This study has been performed to develop an agricultural waste incinerator to combust the rice hull originated from RPC with the typical disposal treatment capacity of 30kg/h and to test performance of the developed incinerator. Experimental results are summarized as following. 1. The optimum feed rate of rice hull of the incinerator is 30kg/h with air ratio of 1.5. 2. The contents of $SO_2$ in flue gas is maximum 18ppm(when rice hull feed rate was 20kg/h and air ratio of 1.5), minimum 7ppm(when rice hull feed rate was 30kg/h and air ratio of 1.5) and average 11ppm. So there is no environmental pollution problem for the incinerating treatment of rice hull of the RPC. 3. The temperature of water of heat exchanger are $53^{circ} C$ and $62^{circ} C$ with water flow rate $3{ell}/min$ and $1.5{ell}/min$ at the optimum combustion condition, respectively. 4. According to theoretical energy calculation, the energy from rice hull combustion may be amounted as much as 80%~190% of energy supplied by kerosene required by RPC.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Fisheries and Aquatic Sciences
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• v.18 no.4
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• pp.373-378
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• 2015

Postprandial ammonia excretion and oxygen consumption in olive flounder Paralichthys olivaceus fed two different feed types, moist pellet (MP) and expanded pellet (EP) diets, to satiation were determined at $12^{\circ}C$, $15^{\circ}C$, $20^{\circ}C$, and $25^{\circ}C$ for 48 h. The ammonia excretion and oxygen consumption rates increased with increasing water temperature. However, the postprandial times for the maximum rates of ammonia excretion and oxygen consumption were shortened from 12 h to 6 h after feeding with increasing water temperature. The ammonia excretion and oxygen consumption rates of the fish fed EP were significantly higher (P < 0.05) than those fed MP at 12 h post-feeding both for $12^{\circ}C$ and $15^{\circ}C$. The highest (P < 0.05) weight-specific ammonia excretion rates at $12^{\circ}C$ were observed in the fish fed EP and MP at $12.1mg\;NH_3-N\;kg^{-1}h^{-1}$ and $8.7mg\;NH_3-N\;kg^{-1}h^{-1}$, respectively, for 12 h and 9 h after feeding. The highest (P < 0.05) weight-specific oxygen consumption rates at $12^{\circ}C$ were observed in fish fed EP and MP at $116.4mg\;kg^{-1}h^{-1}$ and $101.0mg\;kg^{-1}h^{-1}$, respectively, for 12 h after feeding. The highest ammonia excretion rates at $25^{\circ}C$ in the fish fed EP and MP increased to $16.9mg\;NH_3-N\;kg^{-1}h^{-1}$ and $18.3mg\;NH_3-N\;kg^{-1}h^{-1}$, respectively, for 6 h after feeding. The highest (P < 0.05) weight-specific oxygen consumption rates at $25^{\circ}C$ were observed in fish fed EP and MP at $184.3mg\;O_2kg^{-1}h^{-1}$ and $197.3mg\;O_2kg^{-1}h^{-1}$, respectively. These data are valuable for the design of biofilters and development of effluent treatment technologies for the land-based flounder farms.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.3
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• pp.350-357
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• 2021

This study aimed to evaluate how fish meal (FM) replacement in diets with a mixture of animal and plant protein sources affect growth performance, feed utilization, hematological parameters and innate immunity of red seabream Pagrus major. A control FM diet was formulated to contain 65% FM (Con). Two other diets were prepared replacing FM in the control diet with a mixture of protein sources (wheat gluten, soy-protein concentrate, tankage meal, and poultry by-product meal) by 30 and 40% (FM30 and FM40, respectively). Total 300 red seabream (body weight, 77.6±0.3g) were distributed to 12 tanks (300 L) in 4 replicates per diet. The fish were fed the diets to apparent satiation for 19 weeks. After the feeding trial, no significant differences could be observed in growth performance, feed utilization, hematological parameters, innate immunity, and survivals among all the dietary treatments. This long-term feeding trial at low water temperature (13.8-17.5℃) indicates that a proper mixture ratio of wheat gluten, soy protein concentrate, tankage meal, and poultry by-product meal can replace FM up to 40% in red seabream diets.

• Journal of Korean Society on Water Environment
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• v.16 no.4
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• pp.523-532
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• 2000

The purpose of this study was to investigate the effects of the hydraulic retention time (HRT) and organic loading rate (OLR) on microbial characteristics and treatment efficiency in sewage treatment using aerated submerged biological filter (ASBF) reactor. This reactor combines biodegradation of organic substrates by fixed biomass with a physical separation of biomass by filtration in a single reactor. Both simulated wastewater and domestic wastewater were used as feed solutions. The experimental conditions were a temperature of 17 to $27^{\circ}C$, a hydraulic retention time of 1 to 9hr, an organic loading rate of 0.47 to $3.84kg\;BOD/m^3{\cdot}day$ in ASBF reactor. This equipment could obtain a stable effluent quality in spite of high variation of influent loading rate. Total biomass concentration. biofilm thickness and biofilm mass increased an exponential function according to the increasing OLR. The relationships between water content and biofilm density were in inverse proportion. The percentage of backwash water to influent flow was almost 9%. The separation efficiency of biomass was the percentage of 91 to 92 in ASBF reactor. The sludge production rates in feed solutions of simulated wastewater and domestic wastewater were 0.14~0.26 kg VSS/kg BODrem, 0.43~0.48 kg VSS/kg BODrem, respectively.

• 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.

• Textile Coloration and Finishing
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• v.18 no.3 s.88
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• pp.42-48
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• 2006

The aim of this work is to develop Thick-Thin polyester yarn(T-T yarn) with finer than 1 denier mono filament. The manufacture of T-T yarns were carried out in a draw-winder using 85d/72f PET filament with various spinning speed of 2700, 2900, and 3100 m/min, respectively. The structure and physical properties of T-T yarns with spinning speed and over feed ratio were examined by draw-winder processing the sample in $100^{\circ}C$ water for 20 min and drying in 120, 140, 160, and $180^{\circ}C$ of dry air for 20 min. The crystallinity, the birefringence and the initial elasticity modulus of T-T yarns increased with increased spinning speed of filament and the heat treatment temperature but at the temperatures higher than $140^{\circ}C$ the increased rates show a tendency to decrease. Moreover, the initial modulus and the tenacity of T-T yarns increased with decreasing the over feed ratio of filament and the those of T-T yarns decreases with increasing the heat treatment temperature. The shrinkage of T-T yarns decreased with decreasing spinning speed and increased over feed ratio of filaments. Consequently, the results indicate that the best T-T yarn under 1 denier was optimized from PET filament with spinning speed of 2700 m/min and over feed ratio of 0.67

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.3
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• pp.427-433
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• 2013

This study was designed to examine changes in the physicochemical properties of extruded corn fibers with different amounts of feed moisture (30, 40, and 50%). The screw speed and die temperature were fixed to 200 rpm and $140^{\circ}C$, respectively. The crude ash, fat, and protein in corn fiber decreased from the extrusion process. The insoluble dietary fiber in corn fibers decreased, while soluble dietary fiber increased at a feed moisture of 30%. The specific length of the extruded corn fiber increased while the specific mechanical energy input, density, breaking strength, and elastic modulus decreased. The water absorption index (WAI) and reducing sugar content of the corn fibers did not significantly change, but the water soluble index (WSI) decreased as the feed moisture content of the corn fiber increased. On the other hand, the WAI of de-starched corn fiber decreased while WSI and reducing sugars increased as the feed moisture content of the corn fiber increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Journal of Animal Science and Technology
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• v.61 no.6
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• pp.305-312
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• 2019

This study was carried out to investigate the effects of dietary energy levels on growth performance, blood parameter, and intestinal morphology of Pekin ducks in low temperature. A total of 500, 21-d-old Pekin ducks (initial BW = 1,089 ± 5.21 g) were evenly assigned to five dietary treatments (2,950, 3,000, 3,050, 3,100, or 3,150 kcal AME/kg, calculated on an as-is basis) with four replicates (pens) for each treatment (25 ducks per pen). During the experiment, hens were provided with feed and water ad libitum. Overall, increasing dietary energy levels corresponded to an increase of final body weight and body weight gain (linear, p < 0.01). Feed intake decreased (linear, p < 0.01) and feed conversion ratio increased (linear, p < 0.01) with increasing levels of energy. There were no significant differences (p < 0.05) in the level of leukocytes between groups. However, heterophils decreased (quadratic, p < 0.05) and lymphocytes increased (linear, p < 0.01) as inclusion of dietary energy levels increased. The H/L ratio increased (linear, p < 0.01) with increasing dietary energy levels while serum corticosterone levels decreased at overall experimental periods. Triglycerides increased (linear and quadratic, p < 0.05) with increasing dietary energy levels. There were no significant changes in villus height or crypt depth of the jejunum at overall experimental. In conclusion, increasing concentrations of dietary energy levels up to 2,950-3,150 kcal/kg in diet. Additionally, 3,150 kcal/kg dietary energy had been revealed more beneficial and could be practiced as protective management for the Pekin ducks reared under low ambient temperature (8℃ to 10℃).