• Clean Technology
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• v.3 no.2
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• pp.57-73
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• 1997

Metal cleaning process is a technology which removes oil, dust and soil etc. on the surface of metal utilizing cleaning agents. These contaminants disturb the following processes such as plating and painting etc. if they are not removed. Thus, metal cleaning is typically an environmentally hazardous activity. Until recently, vapor degreasers as utilizing chlorinated solvents have been relatively cheap, extraordinarily versatile and waste disposal costs have been perceived as insignificant. Today, however, it is readily apparent that Industry's reliance upon chlorinated solvents as metal cleaners have resulted in a myriad of environmental, health and safety concerns. Therefore, this paper studies on a parameter and a sort of the alternative cleaning agents for the optimum cleaners. Also, a great deal of effort has been devoted to developing alternative metal cleaning technologies in advanced countries and some processes are being commercialized among them. We are going to consider alternative aqueous cleaning agents replacing organic chlorinated solvents and to pursue a domain application through a successful improvement case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2313-2321
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• 2014

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical input. This study has been prepared for improving efficiency of operation on sludge processes and to establish a basic for factory automation by accuracy improvement and problem solution of sludge concentration meter. The concentration meter's accuracy and stability is improved by applying multi-beam sensors and minimum deviation linear average filtering. Furthermore maintenance without cut-off of water in sludge operation is possible by detachable sensors. The performance of multi-beam concentration meter has been variously verified by the pilot plant experiment.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.3 no.2
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• pp.95-104
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• 2005

Effects of the aluminum melting temperature, melting time and a kind of flux agents on the distribution of surrogate nuclide were investigated in the electric furnace at the aluminum melting including surrogate radionuclides(Co, Cs, Sr) in order to establish the fundamental research of the melting technology for the metallic wastes from the decommissioning of the TRIGA research reactor. It was verified that the fluidity of aluminum melt was increased by adding flux agent but it was slightly varied according to the sort of flux agents. The results of the XRD analysis showed that the surrogate nuclides move into the slag phase and then they were combined with aluminum oxide to form more stable compound. The weight of the slag generated from aluminum melting test increased with increasing melting temperature and melting time and the increase rate of the slag depended on the kind of flux agents added in the aluminum waste. The concentration of the cobalt in the ingot phase decreased with increasing reaction temperature but it increased in the slag phase up to 90$\%$according to the experimental conditions. The volatile nuclides such as Cs and Sr considerably transferred from the ingot phase to the slag and dust phase.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.7
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• pp.694-699
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• 2008

Considering the severe regulation associated with sludge treatment such as direct landfill and ocean dumping, there is no doubt in that an advanced study for the proper treatment of sludge is urgently needed in near feature. As one of viable method for sludge treatment, fry-drying of sludge by waste oil has been investigated in this study. The fundamental mechanism of this drying method lies in the phenomenon of rapid moisture escape in the sludge pore toward oil media. This is caused by the severe pressure gradient formed by the rapid oil heating between sludge and oil. As part of research effort of fry-drying using waste oil, a series of basic study has been made experimentally to obtain typical drying curves as function of important parameters such as drying temperature, drying time, oil type and geometrical shape of sludge formed. Based on this study, a number of useful conclusion can be drawn as following. The fry-drying method by oil immersion was found quite effective in the removal efficiency of sludge moisture, in general, the moisture content decreases significantly after 10 minutes and the whole moisture content was less than 5% after 14 minutes regardless of the drying temperature. The increase of oil temperature up to 140$^{\circ}C$ favors significantly for the removal of moisture but there was no visible difference above 140$^{\circ}C$. As expected, the decrease of diameter in sludge was efficient in drying due to the increased surface area per unit volume. Further, the effect of oil property by the change of oil type was noted. To be specific, for the case of engine oil the efficiency was found to be remarkably delayed in moisture evaporation compared with that of vegetable oil due to the increased viscosity of engine oil. It produced a result of increasing the evaporation of moisture largely relatively high in the drying temperature over 140$^{\circ}C$ compared with the drying temperature 120$^{\circ}C$ drying temperature as the drying time passed. Accordingly, the drying temperature is considered desirable as keeping over 140$^{\circ}C$ regardless of a sort of used oil.

• Journal of Animal Environmental Science
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• v.12 no.1
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• pp.7-12
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• 2006

Ammonia emission from swine production process originates from three major sources: manure storage facility, swine housing, and land application of manure. Most of the ammonia gas that are emitted from swine production operations is the by-product of aerobic or anaerobic decomposition of swine waste by microorganism. Knowing the ammonia emission rate is necessary to understand how management practices or alternative manure handling process could reduce impacts of this emission on the environment and neighbors. Ammonia gas emission from pig slurry is very difficult to predict because it is affected by many factors including wind speed of slurry surface, temperature or pH of the swine slurry, sort breed differences and classes, and diets. This study was carried out to effects of pH and temperature on ammonia gas emission from growing-finishing pig slurry. Treated far slurry in this study were pH and temperature. Results showed that pH of slurry variable changes 5, 6, 7, 8 upon an addition of NaOH and $HNO_3$, respectively. The temperature of the slurry which was contained in a water bath maintained at increasing levels ranging from 10 to $35^{\circ}C$. Ammonia emission rate of influenced pH and temperature such that the increase in pH or temperature resulted to an increase in ammonia emission. The ammonia gas was not detected at pH 5 and 6. Moreover, at a slurry of pH 8, the ammonia ranged from 28 to 60ppm and 8-29 ppm at slurry pH of 7 while temperature was 13 to $33^{\circ}C$. When slurry pH was>6, the ammonia emission was significantly increased according to rise in temperature in contrast to acid treatment of the pH. There was also a significantly increase in ammonia emission relative to slurry pH of 7 to 8. The above findings showed that to effectively reduce ammonia emission from slurry of growing-finishing pigs, the pH and temperature should be maintained a low levels.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.119-125
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• 2015

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical dosage. When the strange substance is contained in the sludge, however, the attenuation of ultrasonic wave could be increased or not be transmitted to the receiver. At that case, the value of concentration meter is higher than the actual density value or vibrated up and down. It has also been difficult to automate the residuals treatment process according to the problems as sludge attachment or damage of a sensor. Multi-beam ultrasonic concentration meter has been developed to solve these problems, but the failure of the ultrasonic beam of a specific concentration measurement value degrade the performance of the entire system. This paper proposes the method to improve the accuracy of sludge concentration rate by choosing reliable sensor values and learning them by proposed algorithm. The prediction algorithm is chosen as neuro-fuzzy model, which is tested by the various experiments.