Ethanol production from organic fraction of municipal solid waste with inclusion of construction/demolition waste can be an effective waste management strategy to overcome the growing problems with landfill space and dependency on conventional fuels. The main challenge in ethanol conversion is the high cost of processing in which pre-treatment, enzymatic hydrolysis and fermentation are the major steps. This study investigates impact of several key parameters, namely: pH, temperature, adsorption capacity, cellulose hydrolysis rate, cell mass, enzyme and substrate loading doses on ethanol yield. The pre-treatment incorporates pre-processing and enzymatic hydrolysis steps through the use of a thermal screw press (TSP) and cellulose-organic-solvent based lignocellulosic fractionation (COSLIF) on the source-separated organic (SSO) waste to liberate fermentable sugars. Enzymatic hydrolysis experiments were featured with the addition of a commercially available enzyme complex, Accellerase 1500, to mediate the process and increase sugar yields. A kinetic model that uses a semi-mechanistic rate equation for cellulose hydrolysis was adapted and modified to accommodate batch simultaneous saccharification and co-fermentation (SSCF) process on pre-treated SSO waste by yeast, Saccharomyces cerevisiae DA2416. New experimentally defined SSO parameters have been fitted into a kinetic model to evaluate the sugar and ethanol yields. It was found that the model was capable of predicting ethanol productions with diminutive variance from experiments with substrate concentrations between 10 g/L and 50 g/L. Model predictions from experimental data deviated significantly with substrate loading rate from 60 g/L and higher. Fermentation results demonstrated that S. cerevisiae DA2416 produced ethanol in the range of 35 -50 g/L, with ethanol yield of 0.48 -0.50 g of ethanol/g sugar, in 5 days with 96% cellulose conversion. This study provides important insights for investigation on the use of SSO waste for ethanol production by S. cerevisiae DA2416. Furthermore, the model was proven to be a useful tool to facilitate future process optimization for up-scale bioreactors.