A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.     

Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions

This paper proposes a novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center, and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions shows its ability in dealing with different types of optimization problems. The proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO) and harmony search algorithm (HSA). The results show that the HSS algorithm has faster convergence and results in better solutions than GA, PSO and HSA.     

Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques

Robotic belt grinding operations are performed by mounting a workpiece to the end effector and commanding it to move along a trajectory while maintaining contact with the belt grinding wheel. A constant contact force throughout the grinding process is necessary to provide a smooth finish on the workpiece, but it is difficult to maintain this force due to a multitude of installation, manipulation, and calibration errors. The following describes a novel methodology for robotic belt grinding, which primarily focuses on system calibration and force control to improve grinding performance. The overall theory is described and experimental results of turbine blade grinding for each step of the methodology are shown.     

OCRSpell: an interactive spelling correction system for OCR errors in text

In this paper, we describe a spelling correction system designed specifically for OCR-generated text that selects candidate words through the use of information gathered from multiple knowledge sources. This system for text correction is based on static and dynamic device mappings, approximate string matching, and n-gram analysis. Our statistically based, Bayesian system incorporates a learning feature that collects confusion information at the collection and document levels. An evaluation of the new system is presented as well. Received August 16, 2000 / Revised October 6, 2000     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

POLAROGRAPHIC CHARACTERIZATION OF AROMATIC STRUCTURES IN COAL LIQUID PRODUCTS

Reducible aromatic species in coal liquid products from five major processes (Synthoil, FMC COED, HRI H-Coal, PAMCO SRC, and Catalytic Inc. SRC) together with solvent elution chromatography fractions of a selected asphaltene has been evaluated. The half-wave potentials measurments are found to be consistant with other methods. The total polarographic reduction of measurement per average molecule for Synthoil solvent fractions, as measured by diffusion constant (i_d/C), increases in the following manner: gas oil → resin → asphaltene → carbene → carboid. The same trend was observed when color indices (I) and refractive indices (n₂₅) were individually studied.     

Biochemical and molecular characterization of novel PAH-degrading bacteria isolated from polluted soil and sludge

Polycyclic aromatic hydrocarbons (PAHs) containing more than three rings are mainly less biodegradable. Therefore, the isolation of PAHs-degrading bacteria is of great importance to be augmented for bioremediation of polluted sites with PAHs. PAHs-degrading bacteria were isolated from contaminated sites of an oil refinery. The strains were confirmed by biochemical tests and 16S rRNA, which were Stenotrophomonas maltophilia (two strains), Thermomonas koreensis (three strains), Achromobacter (two strains), Pseudomonas stutzeri (one strain), Azospirillum brasilense (one strain) and Brevibacillus brevis (one strain). The isolate strains can be applied as a bacterial consortium for purification of polluted soil with high levels of PAHs.     

Effect of preventive maintenance intervals on reliability and maintenance costs of wind turbine gearboxes

As many of the installed wind turbines (WTs) get older or approach their design life, there will be a drive to keep extending the lives of the main components especially the gearbox. The challenge of operations and maintenance will potentially be even more as there will be a need to keep the cost to a minimum. Similarly, as years of experience of operating WTs accumulate, knowledge about the behaviour and failure of subsystems is gained as well. Also with good documentation and repository of historical operational, performance and failure data, future decisions of operations and maintenance can be taken on the basis of insights from past experience. This paper presents an approach for implementing preventive maintenance (PM) by using historical failure data to determine the optimal PM interval required to maintain desired reliability of a typical module or subassembly. This paper builds upon previous research in the area of WT gearbox reliability analysis and prediction, taking it further by examining the relationships between the frequency of a PM task and the reliability, availability and maintenance costs. The approach presented demonstrates how historical in‐service failure data can be used in PM task selection based on the minimum maintenance cost and maximum availability. Available historical field failure data of the high speed module of a Vestas 2MW WT gearbox is used to validate the approach and show its practicality. The results of this study are then presented—indicating that choosing the right PM interval based on the minimum unit maintenance cost and maximum availability also improves WT gearbox reliability. Copyright © 2014 John Wiley & Sons, Ltd.     

Mathematical and neural network models for predicting the electrical performance of a PV/T system

There are many photovoltaic/thermal (PV/T) systems' designs that are used mainly to reduce the temperature of the PV cell by using a thermal medium to cool the photovoltaic module. In this study, a PV/T system uses nano‐phase change material (PCM) and nanofluid cooling system was adopted. Three cooling models were compared using nanofluid (SiC‐water) and nano‐PCM to improve the performance and productivity of the PV/T system. Three mathematical models were developed for linear prediction, and their results were compared with the predicted artificial neural network results, results were verified, and experimental results were appropriate. Three common evaluation criteria were adopted to compare that the results of proposed forecasting models with other models developed in many research studies are done, including the R², mean square error (MSE), and root‐mean‐square error (RMSE). Besides, different experiments were implemented using varying number of hidden layers to ensure that the proposed neural network models achieved the best results. The best neural prediction models deployed in this study resulted in good R² score of 0.81 and MSE of 0.0361 and RMSE and RMSE rate is 0.371. Mathematical models have proven their high potential to easily determine the future outcomes with the preferable circumstances for any PV/T system in a precise way to reduce the error rate to the lowest level.     

Photocatalytic degradation of diclofenac using hybrid MIL-53(Al)@TiO2 and MIL-53(Al)@ZnO catalysts

In this work, TiO₂ and ZnO were incorporated successfully into a MIL-53(Al) metal–organic framework (MOF) to form nanocomposites via a facile post-modification technique. The hybrid MIL-53(Al)@TiO₂ and MIL-53(Al)@ZnO were characterized by several characterization tests. The X-ray diffraction (XRD), Fourier-transform infrared (FTIR), and field-emission scanning electron microscopy (FE-SEM) analyses showed evidence of the successful incorporation of TiO₂ and ZnO within the MIL-53(Al) framework. The thermal gravimetric analysis (TGA) analysis demonstrated the excellent thermal stability of MIL-53(Al)@TiO₂ and MIL-53(Al)@ZnO, while diffuse reflectance spectroscopy (DRS) determined the direct optical band gaps of MIL-53(Al)@ZnO and MIL-53(Al)@TiO₂ to be 3.24 and 3.34 eV, respectively. The composites were also tested for the photocatalytic degradation of diclofenac (DCF) as a micropollutant. The DCF degradation efficiency of the photocatalysts was ranked in the following order: MIL-53(Al)@TiO₂ > MIL-53(Al) > TiO₂ > ZnO > MIL-53(Al)@ZnO. The incorporation of TiO₂ enhanced the optical properties of MIL-53 (Al), which was confirmed with the superior photodegradation efficiency of MIL-53(Al)@TiO₂ (>85% in 2 h) as compared to the pristine MIL-53(Al) (around 80% in 2 h). The improvement in the photodegradation of the hybrid-MOF is mostly associated with the possible dual function of the adsorption and photodegradation mechanisms. The reusability of MIL-53(Al) and its composites was inspected over 3 cycles of photodegradation experiments with DCF. The photocatalytic activity of MIL-53(Al)@TiO₂ remained unchanged (>90%), while for MIL-53(Al) and MIL-53(Al)@ZnO a slight drop was observed over three cyclic degradation experiments. Fluorescence measurements revealed that the hydroxyl radical is an important reactive oxygen species produced by all the photocatalysts that aid in the photodegradation of DCF. Furthermore, the kinetic modelling of the photoreaction identified a second-order kinetics for all catalysts. Experiments with scavengers showed that hydroxyl radicals played a major role in the photocatalytic process, and it was found that only 2 h of treatment was sufficient to obtain a considerable chemical oxygen demand (COD) reduction of 58%.