The construction and maintenance of UK roads consume large amounts of quarried aggregates. The use of secondary (recycled), instead of primary (virgin), materials helps easing landfill pressures and reducing demand of extraction. However, concerns over inferior road performance and additional costs have hindered the widespread use of secondary aggregates in such applications. This is especially the case in surface layers of asphalt pavements that may represent a value application for recycled solid waste materials (SWM). Waste glass, steel slag, tyres and plastics are selected for this study, which reviews standards and literature for technical requirements, as well as the performance of asphalt pavements constructed using such recycled materials. Waste arising and management indicates that although there is a large potential for supplying secondary materials, a few factors have effectively depressed such recycling activities. Such barriers are described here and may also apply to the secondary use of other SWM. After identifying and quantifying such barriers a brief discussion suggests ways of their removal.

A review of the use of recycled solid waste materials in asphalt pavements

Success in experiments and/or technology mainly depends on a properly designed process or product. The traditional method of process optimization involves the study of one variable at a time, which requires a number of combinations of experiments that are time, cost and labor intensive. The Taguchi method of design of experiments is a simple statistical tool involving a system of tabulated designs (arrays) that allows a maximum number of main effects to be estimated in an unbiased (orthogonal) fashion with a minimum number of experimental runs. It has been applied to predict the significant contribution of the design variable(s) and the optimum combination of each variable by conducting experiments on a real-time basis. The modeling that is performed essentially relates signal-to-noise ratio to the control variables in a 'main effect only' approach. This approach enables both multiple response and dynamic problems to be studied by handling noise factors. Taguchi principles and concepts have made extensive contributions to industry by bringing focused awareness to robustness, noise and quality. This methodology has been widely applied in many industrial sectors; however, its application in biological sciences has been limited. In the present review, the application and comparison of the Taguchi methodology has been emphasized with specific case studies in the field of biotechnology, particularly in diverse areas like fermentation, food processing, molecular biology, wastewater treatment and bioremediation.

The Taguchi methodology as a statistical tool for biotechnological applications: A critical appraisal

Towards advanced aqueous dye removal processes: a short review on the versatile role of activated carbon.

Computational fluid dynamic and thermal analysis of Lithium-ion battery pack with air cooling

Concrete made with recycled tire rubber: Effect of alkaline activation and silica fume addition

Experimental investigation of thermal resistance of a heat sink with hexagonal fins

Optimum design-parameters of a heat exchanger having hexagonal fins

Determination of the optimum conditions in the removal of Bomaplex Red CR-L dye from the textile wastewater using O3, H2O2, HCO3- and PAC.

Optimisation of the chlorination of gold in decopperized anode slime in aqueous medium

Artificial neural networks (ANNs) have been successfully used for solving variety of problems. One major disadvantage of ANNs is that there is no formal systematic model building approach. This paper presents the application of the Taguchi method in the optimization of the design parameters of the ANNs. The performances of the ANNs were determined by the Taguchi method considering factors relevant for ANNs’ performance. The properties affecting the performance of the ANNs and their levels on the peak analytical function were determined by performing computational experiments. After training the network, the values of the statistical data criteria were determined and the optimum parameter levels were obtained in terms of the performance statistics. The performance of ANNs is shown to be better in the case of the application of the Taguchi method rather than in the case of random choice of factor values.

The investigation of model selection criteria in artificial neural networks by the Taguchi method

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