화학공학소재연구정보센터
검색결과 : 1,147건
No. Article
91 Neural Computing Strategy for Predicting Deactivation of Fischer-Tropsch Synthesis With Different Nickel Loadings
Pakdel MG, Zohdi-Fasaei H, Mirzaei AA, Atashi H
Catalysis Letters, 149(9), 2444, 2019
92 Adaptive neuro-fuzzy inference system (ANIFS) and artificial neural network (ANN) applied for indium (III) adsorption on carbonaceous materials
Franco DSP, Duarte FA, Salau NPG, Dotto GL
Chemical Engineering Communications, 206(11), 1463, 2019
93 Artificial neural network modeling on the prediction of mass transfer coefficient for ozone absorption in RPB
Liu TR, Liu YR, Wang D, Li YW, Shao L
Chemical Engineering Research & Design, 152, 38, 2019
94 Evaluation of carrier size and surface morphology in carrier-based dry powder inhalation by surrogate modeling
Farizhandi AAK, Paclawski A, Szlek J, Mendyk A, Shao YH, Lau R
Chemical Engineering Science, 193, 144, 2019
95 Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks
Kimaev G, Ricardez-Sandoval LA
Chemical Engineering Science, 207, 1230, 2019
96 Assessment of Cu (II) removal from an aqueous solution by raw Gundelia tournefortii as a new low-cost biosorbent: Experiments and modelling
Shandi SG, Ardejani FD, Sharifi F
Chinese Journal of Chemical Engineering, 27(8), 1945, 2019
97 Prediction of the colorimetric parameters and mass loss of heat-treated bamboo: Comparison of multiple linear regression and artificial neural network method
Gurgen A, Topaloglu E, Ustaomer D, Yildiz S, Ay N
Color Research and Application, 44(5), 824, 2019
98 Simulation and analysis of vacuum pressure swing adsorption using the differential quadrature method
Makarem MA, Mofarahi M, Jafarian B, Lee CH
Computers & Chemical Engineering, 121, 483, 2019
99 Multi-objective optimization of sulfur recovery units using a detailed reaction mechanism to reduce energy consumption and destruct feed contaminants
Rahman RK, Ibrahim S, Raj A
Computers & Chemical Engineering, 128, 21, 2019
100 Predictive models and detection methods applicable in water detection framework for industrial electric arc furnaces
Alshawarghi H, Elkamel A, Moshiri B, Hourfar F
Computers & Chemical Engineering, 128, 285, 2019