Lime-fly ash stabilization of Calaca, Batangas bottom ash - Antonio, Oscar Victor M.
The coal-fired power plant of the National Power Corporation (NAPOCOR) in Calaca, Batangas emits an estimated 62.62 tons of coal combustion byproducts (CCBs) per hour on a 24-hour daily basis. Like other coal power plants, the continuous disposal of CCBs such as fly ash, bottom ash and boiler slag in landfills or surface impounds creates environmental problems to people living in the neighboring areas. The accumulation of these byproducts poses a significant environmental risk at the rate these byproducts are being generated. This study seeks to find ways of utilizing and maximizing the potential of these materials in a manner that is both environmentally friendly as well as economically viable. A part from mitigating the environmental problems related to its disposal, the use of coal combustion byproducts also saves natural resources and energy by making waste materials useful through recycling. The engineering properties, such as chemical composition, physical and mechanical properties, of Calaca, Batangas bottom ash were determined to assess its suitability as effective material for the construction of engineered fills. Bottom ash is composed principally of silica and alumna with smaller percentages of calcium, iron and other compounds. The low specific gravity (Gs = 1.9) of this materials is advantageous for some structural fill applications by reducing the load on weak layers or zones of soft foundation soils such as poorly consolidated or landslide-prone soils. The study investigated the effects of adding different proportions of lime and fly ash to the compaction behavior and unconfined compressive strength (UCS) of bottom ash. The UCS of stabilized bottom ash as a function of its chemical, physical, and mechanical properties was heuristically established using Artificial Neural Network (ANN) approach. The application of ANN to the prediction of UCS of stabilized bottom ash is usually way to relate UCS with its influence factors. By varying the architecture of the network, 68 ANN models were created, trained and simulated to predict the UCS. The Levenberg-Marquardt feed-forward back propagation algorithm, a variation of Newton's method, was used to train the neural network. The performance of the artificial neural network models were evaluated according to the coefficient of correlation (R), best linear regression relating the ANN predicted and experimental USC values, and the computed errors, mean absolute error (MAE) and root mean squared error (RMSE). The architecture of the best ANN model for predicting the UCS involves two (2) levels of hidden layer, where in the first level contains eight 9*) neurons while the second level contains seven (7) neurons.