Optimization design on polybrominated biphenyls (PBBs) extraction from plastics for RoHS directive

Extracting polybrominated biphenyls (PBB) from electronic and electrical equipment is of high concern due to RoHS directive. PBBs were so toxic for human and environment, they are applied largely in electronic and electrical equipment as flame retardant. In this thesis, a new method was developed to predict the optimal conditions of semivolatile organic polybrominated biphenyls (PBB) extraction from plastic for Enviromental protection. A feed forward type of artificial neural network (ANN) model design was used to investigate the effects of four independent variables, namely, the ratio of solvent, stirring speed (rpm), extraction temperature (omicronC), extraction time (h) on the response, the acquired ratio of PBB. The independent variables were coded at four levels and their actual values selected on the basis of results of single-factor experiment. The model was initially trained by the analytical data with function approximation principle in MATLAB environment to reveal the real engineering world of extract process. Then dimensions of the trained result were reduced from n-D to 2D, In which, a visual contour plot and simulated curve were displayed, an optimal extract processing is achieved with 9.654% maximal acquired ratio of PBB. In order to validate the method, at the optimal point, the simulated result generated by proposed model was checked with the real experiment, it founded they kept a good agreement with each other. Thus proves that the mathematical model developed for resolving the PBB extraction from plastics is very effective and accurate. It is also a useful tool to reveal the real parameters effect on productivity.