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A novel cathode material of (Nd0.9La0.1)1.6Sr0.4Ni0.75Cu0.25O3.8 (NLSNC4), is synthesized by solid state reaction and is further investigated in combination with samarium doped ceria (SDC) as composite cathodes including 0, 10, 30, 50, and 70 of SDC electrolyte phase. The X‐ray diffraction analysis showed that NLSNC4 was produced in pure phase and was chemically compatible with SDC electrolyte. Electrical conductivities of single phase and composite cathode materials along with their electrochemical performances on SDC electrolytes in symmetric cells were measured by DC four‐probe method and electrochemical impedance spectroscopy (EIS), respectively. Results indicated that the maximum electrical conductivity is 135.56 S cm−1 at 350 °C, measured for single phase NLSNC4, sintered at 1,300 °C. Polarization resistance of pure NLSNC4 cathode was 2.71 Ω cm2 at 800 °C, and NLSNC4‐x wt.% SDC composite cathodes displayed Rp value of 0.35, 0.14, 0.12, and 0.32 Ω cm2 at 800 °C, for x = 10, 30, 50, and 70, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective uniform-diversity genetic programming (MUGP) algorithm deployed for robust Pareto modeling and prediction of complex nonlinear processes using some input-output data table. The uncertainties included in measured data are considered to obtain more robust models. The considered benchmarks are an explosive cutting and forming processes, in which the nonlinear behavior between the input and output of processes are detected using MUGP. For both case studies, a multi-objective modeling and prediction procedure firstly performed using deterministic data. Secondly, the same identification procedure carried out using probabilistic uncertainty in the experimental input-output data. The objective functions considered are namely, training error, prediction error and number of tree nodes (complexity of models) in the deterministic approach. Accordingly, the mean and standard deviation of training error and prediction error are considered in robust Pareto modeling and prediction of such processes. In this way, Pareto front of such modeling and prediction is first obtained for both explosive cutting and forming processes with deterministic data. Such Pareto front is then obtained using experimental input-output-data having probabilistic uncertainty in input parameters through a Monte Carlo simulation (MCS) approach. In addition, it has been shown that for both cases, the trade-off models obtained from deterministic data have significant biases when tested on data with probabilistic uncertainty. Finally, the obtained results of such multi-objective robust model identification show promising results in terms of compensating uncertainty in the experimental input-output-data. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-objective genetic programming (GP) with a diversity preserving mechanism and a real number alteration operator is presented and successfully used for Pareto optimal modelling of some complex non-linear systems using some input-output data. In this study, two different input-output data-sets of a non-linear mathematical model and of an explosive cutting process are considered separately in three-objective optimisation processes. The pertinent conflicting objective functions that have been considered for such Pareto optimisations are namely, training error (TE), prediction error (PE), and the length of tree (complexity of the network) (TL) of the GP models. Such three-objective optimisation implementations leads to some non-dominated choices of GP-type models for both cases representing the trade-offs among those objective functions. Therefore, optimal Pareto fronts of such GP models exhibit the trade-off among the corresponding conflicting objectives and, thus, provide different non-dominated optimal choices of GP-type models. Moreover, the results show that no significant optimality in TE and PE may occur when the TL of the corresponding GP model exceeds some values. [ABSTRACT FROM AUTHOR]