• Resources Recycling
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• v.24 no.1
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• pp.58-65
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• 2015

Although Korea is a top market sharing and world leading producer and developer of flat panel display devices, relevant recycling technology is not up to her prestigious status. Besides, most of the waste glass arising from flat panel displays is currently land-filled. The present paper mainly reviews on development of recycling systems for waste TFT-LCD glass from end-of-life LCD TVs and monitors and TFT-LCD process waste of crushed glass particles with target end uses of raw material for high strength concrete pile and glass fibers, respectively. Waste LCD glass was recycled to fabricate ingredients for high strength concrete piles with enhanced physical properties and spherical foam products. The waste LCD glass recycling technology is already developed to fabricate long and short fibers at commercial level. In view of these, future R & D on waste LCD glass materials is to be directed toward implementation of commercial materials recycling system therefrom.

• Resources Recycling
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• v.24 no.1
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• pp.66-72
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• 2015

Various waste PCBs arose during disassembly of LCD TVs and monitors in which they originally functioned for transmission of imaging signal, power supply, and imaging control. In those functional PCBs, gold and copper are contained at far more acceptable level, exceeding mining grade ores. Those valuable metals and their contents widely vary with functionality and end use of PCBs. Therefore, compositional analysis of individual waste PCBs from disassembled LCD TVs and monitors were performed in the present study to classify them into three categories: high gold yield and low gold yield PCBs and those without gold contents. Besides, additional chemical analysis was made to reveal gold and copper contents in the waste PCBs arising from actual disassembly/separation of end-of-life LCD TVs and monitors.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Computers and Concrete
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• v.24 no.2
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• pp.151-158
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• 2019

The sustainable development principle of replacing natural resources with renewable material is an important research topic. In this study, waste LCD (liquid crystal display) glass powder was used to replace cement (0%, 10%, 20% and 30%) through a volumetric method using three water-binder ratios (0.47, 0.59, and 0.71) to make cement mortar. The compressive strength was tested at the ages of 7, 28, 56 and 91 days. The test results show that the compressive strength increases with age but decreases as the water-binder ratio increases. The compressive strength slightly decreases with an increase in the replacement of LCD glass powder at a curing age of 7 days. However, at a curing age of 91 days, the compressive strength is slightly greater than that for the control group (glass powder is 0%). When the water-binder ratios are 0.47, 0.59 and 0.71, the compressive strength of the various replacements increases by 1.38-1.61 times, 1.56-1.80 times and 1.45-2.20 times, respectively, during the aging process from day 7 to day 91. Furthermore, a prediction model of the compressive strength of a cement mortar with waste LCD glass powder was deduced in this study. According to the comparison between the prediction analysis values and test results, the MAPE (mean absolute percentage error) values of the compressive strength are between 2.79% and 5.29%, and less than 10%. Thus, the analytical model established in this study has a good forecasting accuracy. Therefore, the proposed model can be used as a reliable tool for assessing the design strength of cement mortar from early age test results.

• Clean Technology
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• v.15 no.4
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• pp.253-257
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• 2009

The waste solution discharged form the LCD(Liquid Crystal Display) manufacturing process contains phosphoric acid, nitric acid, acetic acid and metal ions such Al and other impurities. In this study, vacuum evaporation and diffusion dialysis was developed to commercialize an efficient system for recovering the high-purity phosphoric acid and manufacturing monoammonium phosphate. By vacuum evaporation, almost 99% of nitric and acetic acid was removed. Also, by diffusion dialysis, about 97.5% of Al was removed. Monoammonium phosphate was manufactured from purified phosphoric acid and ammonium hydroxide. In order to get the optimum manufacturing condition, the molar ratio of ammonium hydroxide and phosphoric acid, pH and temperature was controlled. Using this optimum condition, we obtained the recovery rate of monoammonium phosphate of about 90%.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.181-192
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• 2015

The aim of this study is to obtain optimum process condition for using two tower distribution to recycle the waste Propylene Glycol Monomethyl Ether Acetate (PGMEA) that is formed after washing LCD. The optimum process condition for the content of PGMEA, which is dependent variable, at 1st distillation was calculated according to Bottom temperature (BTM temperature), Reflux amount, Feed amount, Feed temperatures, and the optimum process conditions and optimum factors for the content of PGMEA at 2nd distillation according to Bottom temperature (BTM temperature), Reflux amount, Feed amount, Feed temperatures. At 1st distillation, Reflux amount, Feed amount, and Feed temperature are significant variables. However, it is found that the BTM temperature range is not significant in the range of process condition used in this study. The optimum process conditions are based on $5700{\ell}$ of Feed amount, $2500{\ell}$ of Reflux amount, $165^{\circ}C$ of BTM temperature, and $130^{\circ}C$ of Feed temperature. For the this condition, the predicted content of PGMEA was calculated as 92.12~94.62%. Significant factors at 2nd distillation are Reflux amount, Feed amount, and BTM temperature. Multicollinearity is between Reflux amount and BTM temperature. BTM was omitted in the multiple regression equation because there is a strong positive correlation between Reflux amount and BTM temperature. Base on $199^{\circ}C$ of BTM temperature, The optimum process conditions are based on $4275{\ell}$ of Feed amount, $6200{\ell}$ of Reflux amount and $130^{\circ}C$ of Feed temperature. In this condition, the predicted content of PGMEA was calculated as 99.0~99.5%.