Alastair S. Wood

Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system

Abstract This paper presents research into analysis and data fusion for sensors measuring hydraulic parameters (flow and pressure) of the pipeline water flow in treated water distribution systems. An artificial neural network (ANN) based system is used on time series data produced by sensors to construct an empirical model for the prediction and classification of leaks. A rules based system performs a fusion on the ANNs’ outputs to produce an overall state classification for a set of zones. Results are presented using data from an experimental site in a distribution system of a UK water company in which bursts were simulated by hydrant flushing. The ANN system successfully detected events and a study of the pressure gradient across the zone provided a more precise location within the zone.

A new look at the heat balance integral method

Abstract The heat balance integral method is a familiar technique for treating transport problems, particularly phase-change scenarios. Here a number of differences arising in the methods implementation are investigated that result in quantitatively distinct solutions. As a consequence some guidance is provided for selecting the appropriate implementation of the method.

Species based evolutionary algorithms for multimodal optimization: A brief review

The species conservation technique is a relatively new approach to finding multiple solutions of a multimodal optimization problem. When adopting such a technique, a species is defined as a group of individuals in a population that have similar characteristics and are dominated by the best individual, called the species seed. Species conservation techniques are used to identify species within a population and to conserve the identified species in the current generation. A ‘species-based evolutionary algorithm’ (SEA) is the combination of a species conservation technique with an evolutionary algorithm, such as genetic algorithms, particle swarm optimization, or differential evolution. These SEAs have been demonstrated to be effective in searching multiple solutions of a multimodal optimization problem. This paper will briefly review its principles and its variants developed to date. These methods had been used to solve engineering optimization problems and found some new solutions.

Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization

Abstract Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.

Numerical solutions of the thermistor problem with a ramp electrical conductivity

This paper presents approximate steady-state solutions of a positive temperature coefficient thermistor problem, having a ramp electrical conductivity that is a highly non-linear function of the temperature, using a standard explicit finite difference method. It is shown that numerical solutions exhibit the correct physical characteristics of the problem and, they are in good agreement with the exact solution.

Parameter identification of a cage induction motor using particle swarm optimization

Abstract The current paper presents an adaptive system identification/parameter estimation algorithm for a three-phase cage induction motor based on particle swarm optimization (PSO). The performance of the proposed algorithm is emphasized by comparing its results with those of the well-known stochastic optimization techniques of genetic algorithm (GA) and simulated annealing (SA) for the benchmark application with six unknown parameters to identify. The dynamic inertia-weighted PSO algorithm significantly outperformed the GA and SA techniques. The achievement of the presented methodology in confronting a rather complicated non-linear dynamic engineering application underlines the ability of the algorithm to be used for a range of real-world problems, and moreover justifies and motivates the development of more advanced techniques.

Enhancement of Productivity of Distillate Fractions by Crude Oil Hydrotreatment: Development of Kinetic Model for the Hydrotreating Process

Abstract Crude oil hydrotreatment enhances the productivity of distillate fractions due to chemical reactions. A trickle bed reactor (TBR) is used in this work for hydrotreating (HDT) crude oil. In order to obtain a useful model for the reactor which can be confidently applied to design, operation and control, accurate estimation of kinetic parameters of the relevant reactions are required. A kinetic model for those chemical reactions is proposed here. An optimization technique is used to obtain the best values of the kinetic parameters based on pilot plant experiment. The predicted hydrotreated product composition shows very well agreement with the experimental data for a wide range of operating conditions with absolute average errors less than 5% and clearly shows enhancement of productivity of distillate fractions.

PermGA algorithm for a sequential optimal space filling DoE framework

This paper presents the development and implementation of a customised Permutation Genetic Algorithm (PermGA) for a sequential Design of Experiment (DoE) framework based on space filling Optimal Latin Hypercube (OLH) designs. The work is motivated by multivariate engineering problems such as engine mapping experiments, which require efficient DoE strategies to minimise expensive testing. The DoE strategy is based on a flexible Model Building - Model Validation (MB-MV) sequence based on space filling OLH DoEs, which preserves the space filling and projection properties of the DoEs through the iterations. A PermGA algorithm was developed to generate MB OLHs, subsequently adapted for generation of infill MV test points as OLH DoEs, preserving good space filling and projection properties for the merged MB + MV test plan. The algorithm was further modified to address issues with non-orthogonal design spaces. A case study addressing the steady state engine mapping of a Gasoline Direct Injection was used to illustrate and validate the practical application of MB-MV sequence based on the developed PermGA algorithm.

Random search with species conservation for multimodal functions

This paper is to investigate the influence of a minimum population size on the performance of the species conservation technique in searching multiple solutions. The species conservation technique is combined a random search technique, which is a special genetic algorithm with one individual, to present an algorithm, called species conservation random search (SCRS), for solving multimodal problems. Each species is built around a dominating point, called the species seed, with a given species radius, and the species are saved in the species set. The random search is used to explore a new point in the neighborhood area of an initial point randomly selected from the species set. A modified species conservation technique has been developed to update species seeds according to these new exploration points. Numerical experiments demonstrate that the proposed SCRS is very effective in dealing with multimodal problems and can also find all the global solutions of test functions.

The Butt-fusion welding of polymers

A predictive model, based on the definition of an enthalpy function, is constructed for the evolutionary heat transfer behaviour of the butt-fusion welding process for polymer pipes. The model is implemented using standard finite difference techniques and is shown to be robust for a wide range of operating conditions. Its usefulness to relevant industrial and commercial areas is demonstrated.