Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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Article Citation - WoS: 21Citation - Scopus: 22Role of Ph on Co2 Sequestration in Coal Seams(Elsevier Ltd., 2016) Özdemir, EkremThe effect of acidic or basic pre-treatment on the adsorption capacity of CO2 on coals was investigated. Argonne Premium Pocahontas No. 3, Upper Freeport, Pittsburgh No. 8, Lewiston-Stockton, Blind Canyon, Illinois No. 6, Wyodak, and Beulah-Zap coals were washed in weak solutions of H2SO4 and NaOH to the pH values of 10, 7, and 2, after an initial washing in acidic water. Attempts to treat the Wyodak and Beulah-Zap coals were unsuccessful because the base treatment after the initial acid treatment resulted in a suspension which could be separated neither via filtration through a 45 μm filter nor centrifugation. Equilibration took several days in some cases, although the as-received coal had been ground to 150 μm. Acid washing preferentially removed Ca (calcite) and Mg. Aluminosilicate clays were not notably removed. Iron was removed in significant amounts only after base treatment, possibly after it was converted to hematite. The adsorption capacity of CO2 on the acid treated coals was higher than both the base treated and untreated coals. The difference in adsorption capacities for acid and base treated coals was related to the pore sizes and mineral matter removal from the coals, where the calculated average pore size was higher for acid treated coals than for the base treated coals. It is concluded that the pH decrease due to CO2 dissolution in cleat water is favored in coal seam sequestration, which resulted in an increase in storage capacity of coals.Article Citation - WoS: 37Citation - Scopus: 38Prediction of the Bottom Ash Formed in a Coal-Fired Power Plant Using Artificial Neural Networks(Elsevier Ltd., 2012) Bekat, Tuğçe; Erdoğan, Muharrem; İnal, Fikret; Genç, Aytenhe amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.Article Citation - WoS: 123Citation - Scopus: 131A New Methodology for Removal of Boron From Water by Coal and Fly Ash(Elsevier Ltd., 2004) Polat, Hürriyet; Vengosh, Avner; Pankratov, Irena; Polat, MehmetHigh levels of boron concentrations in water present a serious problem for domestic and agriculture utilizations. The recent EU drinking water directive defines an upper limit of 1 mgB/I. In addition, most crops are sensitive to boron levels >0.75 mg/1 in irrigation water. The boron problem is magnified by the partial (∼60%) removal of boron in reverse osmosis (RO) desalination due to the poor ionization of boric acid and the accumulation of boron in domestic sewage effluents. Moreover, high levels of boron are found in regional groundwater in some Mediterranean countries, which requires special treatment in order to meet the EU drinking water regulations. Previous attempts to remove boron employed boron-specific ion-exchange resin and several cycles of RO desalination under high pH conditions. Here, we present an alternative methodology for boron removal by using coal and fly ash as adsorbents. We conducted various column and batch experiments that explored the efficiency of boron removal from seawater and desalinated seawater using several types of coal and fly ash materials under controlled conditions (pH, liquid/solid ratio, time of reaction, pre-treatment, regeneration). We examined the effect of these factors on the boron removal capacity and the overall chemical composition of the residual seawater. The results show that the selected coal and fly ash materials are very effective in removing boron such that the rejection ratio of boron can reach 95% of the initial boron content under certain optimal conditions (e.g., pH = 9, L/S = 1/10, reaction time > 6 h). Our experiments demonstrated that use of glycerin enables regeneration of boron uptake into coal, but the boron uptake capacity of fly ash reduces after several cycles of treatment-reaction. The boron removal is associated with Mg depletion and Ca enrichment in the residual seawater and conversely with relative Mg enrichment and Ca depletion in the residual fly ash. We propose that the reaction of Ca-rich fly ash with Mg-rich seawater causes co-precipitation of magnesium hydroxide in which boron is co-precipitated. The new methodology might provide an alternative technique for boron removal in areas where coal and fly ash are abundant.