Effect of Microwave Irradiation on Xylanase Production from Wheat Bran and Biobleaching of Eucalyptus Kraft Pulp

Microwave irradiation (MWI) was used as pretreatment of wheat bran and eucalyptus kraft pulp to examine its effect on xylanase production by Bacillus halodurans FNP 135 using solid state fermentation and biobleaching with xylanase, respectively. Irradiation of wheat bran under optimized conditions (600?W, 6?min, and 20?% consistency) resulted in 56.8, and 31.7?% increase in xylanase yield and water absorbance of wheat bran and 17.3?% reduction in reducing sugars content. Optimized MWI of kraft pulp at 850?W, 2?min, and 20?% consistency led to 0.9?% increase in brightness, 10?% decrease in kappa number, 7.7?% increase in water absorbance, 4.6?% decrease in tear factor, 0.9?% increase in burst factor, and 7.5?% increase in viscosity. Also, MWI enhanced xylanase-mediated biobleaching by increasing brightness (1.1?%) and decreasing kappa number (14.3?%) and leading to a total of about 20?% reduction in chlorine consumption. MWI is an economical, efficient, and environment-friendly pretreatment of wheat bran and pulp for enhanced enzyme yield and rapid heating, respectively.     

A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality

Production lot sizing models are often used to decide the best lot size to minimize operation cost, inventory cost, and setup cost. Cellular manufacturing analyses mainly address how machines should be grouped and parts be produced. In this paper, a mathematical programming model is developed following an integrated approach for cell configuration and lot sizing in a dynamic manufacturing environment. The model development also considers the impact of lot sizes on product quality. Solution of the mathematical model is to minimize both production and quality related costs. The proposed model, with nonlinear terms and integer variables, cannot be solved for real size problems efficiently due to its NP-complexity. To solve the model for practical purposes, a linear programming embedded genetic algorithm was developed. The algorithm searches over the integer variables and for each integer solution visited the corresponding values of the continuous variables are determined by solving a linear programming subproblem using the simplex algorithm. Numerical examples showed that the proposed method is efficient and effective in searching for near optimal solutions.