Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Electron transfer mediator PCN secreted by aerobic marine Pseudomonas aeruginosa accelerates microbiologically influenced corrosion of TC4 titanium alloy

Titanium alloys possess excellent corrosion resistance in marine environments,thus the possibility of their corrosion caused by marine microorganisms is neglected.In this work,microbiologically influenced corrosion (MIC) of TC4 titanium alloy caused by marine Pseudomonas aeruginosa was investigated through electrochemical and surface characterizations during a 14-day immersion test.Results revealed that the unstable surface caused by P.aeruginosa resulted in exposure of Ti2O3 and severe pitting corrosion with maximum pit depth of 5.7 μm after 14 days of incubation.Phenazine-1-carboxy[ate (PCN),secreted by P.aeruginosa,promoted extracellular electron transfer (EET) and accelerated corrosion.Deletion of the phzH gene,which codes for the enzyme that catalyzes PCN production,from the P.aeruginosa genome,resulted in significantly decreased rates of corrosion.These results demonstrate that TC4 titanium alloy is not immune to marine MIC,and EET contributes to the corrosion of TC4 titanium alloy caused by P.aeruginosa.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

Significant population genetic structuring in Rhyzopertha dominica across Turkey: Biogeographic and practical implications

Rhyzopertha dominica is a key pest of stored grain. Understanding the movement of this beetle on broad geographic scales is crucial, particularly when developing strategies to prevent the spread of phosphine resistance. We assessed population genetic structuring in this pest across Turkey, using a combination of mitochondrial (cytochrome oxidase I) and microsatellite markers. In addition, we screened samples for Wolbachia, as this endosymbiont has previously been suggested to be associated with low mitochondrial genetic diversity in this beetle. Mitochondrial genetic diversity was low, with only six haplotypes identified. The genetic diversity was, however, substantially higher than that previously found in Australia or India, suggesting that R. dominica may have originated in the Middle East. Wolbachia were detected only at a single site, indicating they are not impacting the mitochondrial genetic diversity of R. dominica across Turkey. Microsatellite markers indicated there is significant geographic genetic structuring across Turkey, even among sites less than 100 km apart, suggesting there is little movement of beetles across regions within the country. This contrasts with the significantly higher levels of gene-flow found in Australia and the United States. We suggest that the limited movement of beetles across Turkey may be due to a combination of the historically localised agricultural practices (which limits anthropogenic movement among regions), and the mountainous landscape (which limits active flight among regions). Our results demonstrate that the movement of stored product pests may differ significantly across studies conducted in different countries. As a consequence, phosphine resistance management strategies must incorporate region specific information on the extent of beetle movement.     

Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions

The minimum cost flow problem (MCFP) is the most generic variation of the network flow problem which aims to transfer a commodity throughout the network to satisfy demands. The problem size (in terms of the number of nodes and arcs) and the shape of the cost function are the most critical factors when considering MCFPs. Existing mathematical programming techniques often assume the cost functions to be linear or convex. Unfortunately, the linearity and convexity assumptions are too restrictive for modelling many real-world scenarios. In addition, many real-world MCFPs are large-scale, with networks having a large number of nodes and arcs. In this paper, we propose a probabilistic tree-based genetic algorithm (PTbGA) for solving large-scale minimum cost integer flow problems with nonlinear non-convex cost functions. We first compare this probabilistic tree-based representation scheme with the priority-based representation scheme, which is the most commonly-used representation for solving MCFPs. We then compare the performance of PTbGA with that of the priority-based genetic algorithm (PrGA), and two state-of-the-art mathematical solvers on a set of MCFP instances. Our experimental results demonstrate the superiority and efficiency of PTbGA in dealing with large-sized MCFPs, as compared to the PrGA method and the mathematical solvers.     

Mechanical,surface morphological and multi-objective optimization of tribological properties of V2O5 doped lead calcium titanate borosilicate glass ceramics

In the 55[(Pb_xCa_1−x).OTiO₂]-44[2SiO₂.B₂O₃]-1V₂O₅ system, various experimental studies were performed to obtain the mechanical and tribological properties of synthesized glass ceramics (GCs). The surface morphological study and elemental analysis were carried out using scanning electron microscope (SEM) followed by EDAX. The tribological measurements were also performed; based on Taguchi's L₂₅ orthogonal array, considering different GC compositions, sliding speeds and loads. The signal to noise (S/N) ratio was used to identify the influencing parameters on minimizing wear coefficient (k) and coefficient of friction (COF). To improve the tribological properties, the modeling of output responses was performed using response surface methodology (RSM) that was being used for multi-objective optimization using a Pareto optimality approach i.e. Genetic Algorithm (GA). The experimentally achieved k and COF were compared with the earlier work that carried out by the authors for germanium (Ge) doped GC samples. It is observed that the various mechanical properties (Vickers hardness, 29 GPa, Young's modulus, 1808 MPa, and compressive strength, 221 MPa) improved significantly in comparison to the Ge doped GC samples. The improved mechanical and tribological properties of the synthesized GC can be used as a coating material for the structural alloys and machine tool slides in view of its effective wear and corrosion resistance.     

The potential of energy flexibility of space heating and cooling in Portugal

This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.     

高速冲压生产线上下料冗余机械臂的运动学分析

在应用于高速冲压生产线冗余机械臂姿态规划和轨迹优化过程中,以兼顾上下料效率与轨迹的平稳性为目的,对冗余机器臂的加加速度进行约束,基于旋量理论建立运动学模型,得到正运动学方程,采用遗传算法对冗余机械臂进行逆运动学分析,结合五次B样条插值方法,对末端执行器运动轨迹的加加速度进行优化。结果表明,该方案可有效提高上下料效率,保证冗余机械臂上下料过程运行平稳。     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.