Optimisation of acetylation parameters for reduced moisture absorption of bamboo fibre using Taguchi experimental design and genetic algorithm optimisation tools

Natural fibres have good weight-to-strength ratio which has made it a material of interest for scientists and engineers. However, the major drawback for outdoor application of natural fibres is its hydrophilic nature. In this study, attempt was made to render bamboo fibre hydrophobic through acetylation with acetic anhydride at room temperature. The functional water absorption properties were studied and the acetylation parameters, such as chemical dosage and acetylation time, were optimised. Taguchi Orthogonal Array was used for the experimental design. Based on the Taguchi design, a regression equation was generated which served as an objective function for Genetic Algorithm. Acetylation reduced the percentage water absorption of Bambusa Vulgaris fibre from 196.4% in un-acetylated fibre down to 45% in acetylated fibres within the feasible design space. The optimal parameter setting generated with genetic algorithm is 15% acetic acid concentration, 50minutes of time soaked in acetic acid, 5% acetic anhydride concentration and 30 minutes time soaked in acetic anhydride. Under the optimum condition, the percentage water absorption was 44%. A confirmation experiment validated the effectiveness of the Genetic Algorithm result.Keywords: Acetylation, Bamboo Fibre, Taguchi, Optimisation, Genetic Algorith

DETERMINING THE EFFECTIVENESS OF CONCURRENT ENGINEERING THROUGH THE ANALYTICAL HIERARCHY PROCESSING OF PROJECT SUCCESS CRITERIA

The emergence of Concurrent Engineering (CE) as the Project Procurement method of choice for effective integration and coordination into construction has been gaining grounds. However, this is based mainly on empirical data that were derived majorly from the implementation of CE within the manufacturing environment. Thus the theoretical foundations of CE has been more empirical that statistical. Although science is driven by data, strong theoretical foundations must exist in order to explain that data. This work seeks to confirm statistically, the prominence of concurrent engineering as the method which offers the most scope for effective attainment of construction objectives of Cost, Time, Quality and ClientsSatisfaction. Using the Analytical Hierarchy Process (AHP) model, these project success criteria were used as the primary criteria, along with its sub-criteria, to calculate the Eigenvectors, in order to synthesize a pair-wise comparison matrix of the criteria. Thus the priority weight vectors were obtained and used for the ranking of the four principal construction delivery methods: Traditional method, the Design and Build method, the Programme management method and the Concurrent Engineering method. The results of the data computations gave a ranking of the four (4) principal project delivery methods of; Traditional sequential delivery, Programme management, Design and build and CE, with the values 0.0001, 0.1027, 0.2062 and 0.6910 respectively. CE ranked highest in its effectiveness in attaining construction goals. The work thus confirm statistically, the prominence of concurrent engineering as the method which offers the most scope for effective  attainment of construction objectives of Cost, Time, Quality and Clients Satisfaction

Optimisation of transfer function models of multi input single output systems using genetic algorithms

Initial transfer function estimates usually require optimisation. In order to obtain a more efficient transfer function model, a manual estimation method is often employed. This manual estimation method, even though it results to better estimates of the transfer function model, it still does not give an optimum outcome. In order to obtain an optimum transfer function estimate, open source software based on genetic algorithm was developed. The software was developed with Visual Basic programming language. In order to test the software, a transfer function model was developed from data obtained from industry. The forecast obtained from the transfer function model has a MAPE of 99.82%. After optimisation using the developed software the MAPE of the new forecast was 99.84%. This shows a marginal improvement in forecast accuracy