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Optimal design of a composite propellant formulation using response surface methodology

Hamed J. O., Ogunleye O. O., Osheku C. A.

Advances in Materials Science

2017

Vol. 17, nr 1(51)

44--57

There is a continuous demand for high performance composite propellant formulations to meet mission requirements. The performance of composite propellant formulations can be enhanced by optimizing propellant formulation. However, the main objective of this study is to formulate a composition for composite propellant by optimizing the specific impulse which is the measure of propellant performance. A central composite design (ccd) consisting five ingredients (ammonium nitrate, powdered aluminum, polyester resin, ammonium dichromate and powdered charcoal) at five levels was used to formulate optimum propellant formulation from composite materials of ammonium nitrate based propellant verified for propellant characteristics using propellant performance evaluation programme (propep 3). The responses evaluated are specific impulse, characteristic velocity, density, temperature and molecular weight. Response surface methodology was used to analyze the results of the ccd of the composite formulations. The optimum values for specific impulse, characteristic velocity, density, temperature and molecular weight of the mixture from the surface plot are 212.178 s, 1335.81 m/s, 1640.6 k g/m3, 1968.73 k and 21.7722 g/mol respectively. The optimum predicted specific impulse was 212.178 s at composite composition of 73.61% ammonium nitrate, 4.36% powdered aluminum, 14.39% polyester resin, 5.10% ammonium dichromate and 2.54% powdered charcoal. The propellant optimum composition validated with propep 3 are in good agreement with each other in their accompany propellant characteristics. Therefore, the optimal propellant formulation enhanced the performance of solid propellants.

Neuro-fuzzy modelling of blending process in cement plant

Araromi D O, Odewale S A, Hamed J O

Advances in Science and Technology. Research Journal

2015

Vol. 9, nr 28

27--33

The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX) model was developed for comparison purpose. ARX model gave high root mean square error (RMSE) of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS) model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE) and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.