Drying of porous building materials: hydraulic diffusivity and front propagation

One-dimensional drying of a porous building material is modelled as a nonlinear diffusion process. The most difficult case of strong surface drying when an internal drying front is created is treated in particular. Simple analytical formulae for the drying front and moisture profiles during second stage drying are obtained when the hydraulic diffusivity is known. The analysis demonstrates the origin of the constant drying front speed observed elsewhere experimentally. Application of the formulae is illustrated for an exponential diffusivity and applied to the drying of a fired clay brick.

Building business cases for maintenance improvement projects

Many maintenance managers find it difficult to justify investments in maintenance improvement initiatives. In part, this is due to a tendency by mine managers to regard maintenance purely as a cost centre, and not as a process able to influence productive capacity and profit. It is also hindered by a lack of alignment between commonly used maintenance performance measures and key business drivers, and the lack of formal business training amongst maintenance professionals. With this in mind, a model to assist maintenance managers in evaluating the benefits of maintenance improvement projects was recently formulated. The model considers four cost saving dimensions. These are: 1. reduction in the cost of unplanned repairs and maintenance, 2. increased or accelerated production and/or sales, 3. spares inventory reduction, and 4. reduction in over-investment in physical assets and operating costs. This paper discusses the application of this model and a number of numerical examples are given to justify investments in maintenance improvement projects having varying objectives.

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.