TY - JOUR A1 - Faizollahzadeh Ardabili, Sina A1 - Najafi, Bahman A1 - Alizamir, Meysam A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Rabczuk, Timon T1 - Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters JF - Energies N2 - 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. KW - Biodiesel KW - Optimierung KW - extreme learning machine KW - machine learning KW - response surface methodology KW - support vector machine KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181025-38170 UR - https://www.mdpi.com/1996-1073/11/11/2889 IS - 11, 2889 SP - 1 EP - 20 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kaps, Christian A1 - Schuch, Kai A1 - Stäblein, Stefan T1 - Silicate coatings for concrete components with waterglass systems by means of neutral salt initiation N2 - The objective of the investigations was the proof of the use of the neutral salt initiation as a construction material in the protecting silicate coating of concrete components, e.g. factory finished parts or reinforced concrete construction parts, by means of waterglass fused silica suspensions KW - Silicate KW - Coating KW - Wasserglas KW - Aggregation KW - Bindemittel KW - Waterglass KW - Alkalisilicate KW - Coating KW - Wasserglas KW - Sol-Gel Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160601-25888 SP - 1 EP - 14 ER - TY - JOUR A1 - Sadeghzadeh, Milad A1 - Maddah, Heydar A1 - Ahmadi, Mohammad Hossein A1 - Khadang, Amirhosein A1 - Ghazvini, Mahyar A1 - Mosavi, Amir Hosein A1 - Nabipour, Narjes T1 - Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network JF - Nanomaterials N2 - In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text KW - Wärmeleitfähigkeit KW - Fluid KW - Neuronales Netz KW - Thermal conductivity KW - Nanofluid KW - Artificial neural network Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200421-41308 UR - https://www.mdpi.com/2079-4991/10/4/697 VL - 2020 IS - Volume 10, Issue 4, 697 PB - MDPI CY - Basel ER - TY - JOUR A1 - Schuch, Kai T1 - Theoretische Grundlagen zum Aggregationsprozess von Wassergläsern im Hinblick auf silikatische Beschichtungen JF - Steuerung des Aggregationsprozesses in wässrigen Alkalisilikatsolen durch spezielle Gelinitiatoren und moderate Wärmebehandlung zum Aufbau einer stabilen Silikatbeschichtung N2 - Der Artikel beinhaltet den theoretischen Teil und die Ergebnisse der Dissertation von Kai Schuch, Bauhaus Universität Weimar, Nov. 2014 KW - Alkalisilikatsol KW - Wasserglas KW - Silicate KW - Gel KW - Aggregationsprozess KW - Wasserglas KW - Gelbildung KW - Alkalisilikat KW - Silikat Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160113-24971 ER - TY - JOUR A1 - Schuch, Kai A1 - Kaps, Christian T1 - Reifungs- und Strukturbildungsprozesse bei Bindern mit wässrigen Alkalisilikat-Lösungen N2 - Durch Reifungs- und Strukturbildungsprozesse kann es bei silikatischen und alumosilikatischen Bindern zu Rissbildung bei behinderter Verformung, Festigkeitsverlust und somit Verlust der Dauerhaftigkeit kommen. Die Bewertung dieser Prozesse erfolgt an silikatischen Materialien mit einem Ausblick auf die alumosilikatischen Binder. KW - Alkalisilikat KW - Wasserglas KW - Bindemittel KW - Silikat KW - Wasserglas KW - Reifungsprozess KW - Strukturbildungsprozess KW - Alumosilikat KW - Silikat Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170728-32682 UR - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/3267 SP - 1 EP - 17 ER - TY - JOUR A1 - Schuch, Kai A1 - Kaps, Christian T1 - Reifungs- und Strukturbildungsprozesse bei Bindern mit wässrigen Alkalisilikat-Lösungen N2 - Durch Reifungs- und Strukturbildungsprozesse kann es bei silikatischen und alumosilikatischen Bindern zu Rissbildung bei behinderter Verformung, Festigkeitsverlust und somit Verlust der Dauerhaftigkeit kommen. Die Bewertung dieser Prozesse erfolgt an silikatischen Materialien mit einem Ausblick auf die alumosilikatischen Binder KW - Alkalisilikat KW - Wasserglas KW - Bindemittel KW - Silikat KW - Wasserglas KW - Reifungsprozess KW - Strukturbildungsprozess KW - Alumosilikat KW - Silikat Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170718-32675 UR - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/3268 SP - 1 EP - 17 ER - TY - JOUR A1 - Schuch, Kai A1 - Kaps, Christian T1 - Maturation and Structure Formation Processes in Binders with Aqueous Alkali-Silicate Solutions N2 - Maturation and structure formation processes can lead to crack formation in silicate and aluminosilicate binders (e.g. for coating materials...) through restricted deformation, loss of strength and thus to loss of durability. These processes are evaluated with silicate materials with an outlook on aluminosilicate binders. KW - Waterglass KW - Silicate KW - Aluminosilicate KW - Geopolymer KW - Alkalisilicate KW - Aggregation KW - Geopolymer KW - Aluminosilicate KW - Coating Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170907-35979 N1 - First published in german language 28.07.2017 SP - 1 EP - 16 ER -