International Journal of Experimental Design and Process Optimisation
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International Journal of Experimental Design and Process Optimisation (3 papers in press)
USING CRITERION-BASED MODEL AVERAGING IN TWO-INPUT MRSM PROBLEMS:
Investigating Cloning of an Under-Fitted Response Model
by Domingo Pavolo, Delson Chikobvu Abstract: Cloning of an under-fitted parent ordinary least squares response model using model averaging to combine its genetic ordinary least squares models is presented and investigated as a solution to the models problem of parametric bias and variability with the intention of improving prediction accuracy. The permutation of genetic models that is produced from the set of the under-fitted ordinary least squares models independent variables is first determined. Sets of genetic models that combine to give the same functional form as the parent ordinary least squares model are then obtained and combined using criterion-based model averaging. The mean squared error of the clones are very close to that of the parent ordinary least squares model but always larger. The mean squared forecasted error suggest that most of the clones have better prediction accuracy than the ordinary least squares model. Combining the clones using criterion-based frequentist model averaging and arithmetic model averaging shows that the higher the number of clones combined using criterion-based frequentist model averaging the better the fitness to data than arithmetic model averaging while the higher the number of clones combined using arithmetic averaging the better the prediction accuracy. Keywords: response surface cloning; multi-response surface methodology; ordinary least squares model; response models; all regressions modeling.
Deep-Learning Assisted Iterative Multi-Objective Optimization of Yarn Production Process by Tamal Ghosh, Kristian Martinsen Abstract: In textile industries, ring and rotor spinning activities are most crucial to the yarn production process, which consist of many parameters and responses. To optimize the said process, the optimal settings for the process parameters must be obtained. Multi-objective optimization models for yarn production exist but these are product sensitive and expensive in terms of the computation and production cost. In this article, an iterative multi-objective deep-learning assisted optimizer is developed and a non-dominated search technique is employed to obtain the Pareto optimal sets of the process parameters, which could improve the yarn quality. Further a Kohonens self-organizing map (KSOM) based model is introduced to investigate the correlations among the yarn production variables. The proposed method is successfully validated with case studies and shown to outperform the existing results. Keywords: Yarn production process optimization; deep-learning model; Artificial Neural Network; Multi-objective optimization; Self Organising Map.
An Optimization of Critical Quality Attributes for Acyclovir Dispersible Tablets: A Quality by Design Approach by ASHWIN MALI Abstract: The present work deals with the development of dispersible tablets (DTs) of an Acyclovir (ACV) by quality by design (QbD) approach. The ACV is an antiviral drug that is widely used in the treatment of Herpes simplex virus infections, chickenpox and shingles. The objective of this study is to formulate DTs of ACV by using maize starch (X1) as a binder, sodium starch glycolate (SSG) (X2) as superdisintegrant and magnesium stearate (X3) as a lubricating agent by wet granulation method. A design of experiment (DoE) comprising two levels, three factors (23) is applied to optimize the combined effect of X1, X2 and X3 as a critical quality attributes (CQA's) on the dependent variables such as disintegration time (Y1), hardness (Y2) and drug release (Y3). The QbD software Minitab is used to represent the data. Moreover, tablets pre-compression and post-compression parameters are evaluated for each formulation. The center batches have depicted the excellent pre-compression and post-compression properties. Moreover, batch comprising 1.20 % of X1, 4.89 % of X2 and 1 % of X3 reflected better disintegration time (48 Keywords: Acyclovir; Quality by design; Design of experiment; Superdisintegrants; 23 factorial design.