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The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
As an optimization that starts from a randomly selected structure generally does not guarantee reasonable optimality, the use of a systemic approach, named the ground structure, is widely accepted in steel-made truss and frame structural design. However, in the case of reinforced concrete (RC) structural optimization, because of the orthogonal orientation of structural members, randomly chosen or architect-sketched framing is used. Such a one-time fixed layout trend, in addition to its lack of a systemic approach, does not necessarily guarantee optimality. In this study, an approach for generating a candidate ground structure to be used for cost or weight minimization of 3D RC building structures with included slabs is developed. A multiobjective function at the floor optimization stage and a single objective function at the frame optimization stage are considered. A particle swarm optimization (PSO) method is employed for selecting the optimal ground structure. This method enables generating a simple, yet potential, real-world representation of topologically preoptimized ground structure while both structural and main architectural requirements are considered. This is supported by a case study for different floor domain sizes.