Response Surface Methodology (RSM) searches for the input combination that optimizes the simulation output. RSM treats the simulation model as a black box. Moreover, this paper assumes that simulation requires much computer time. In the first stages of its search, RSM locally fits first-order polynomials. Next, classic RSM uses steepest descent (SD); unfortunately, SD is scale dependent. Theref...

Modeling and optimization were performed to enhance production of lactase through submerged fermentation by Bacillus subtilis VUVD001 using artificial neural networks (ANN) and response surface methodology (RSM). The effect of process parameters namely temperature (°C), pH, and incubation time (h) and their combinational interactions on production was studied in shake flask culture by Box-Behnk...

Response surface methodology (RSM) was used to enhance the biomass and lipid content in Nannochloropsis salina due to its economic importance. Preliminary screening results revealed that the heterotrophically cultivated N. salina with various carbon and nitrogen sources yielded higher biomass (0.91 ± 0.0035 g/L) and lipid content (37.1 ± 0.49 mg/L) than that of the photoautotrophical cultivatio...

Abstract In our work, the process efficiency of ECMM should be improved by using different combinations nano-particles and added electrolytes. The superior aim this work is to improve predict machining characteristics die hardened steel, namely material removal rate (MRR), Tool wear (TWR) Surface Roughness (Ra). conditions are optimized Response Methodology (RSM) based on Box Behnken Design. be...

Optimum conditions for extracting total phenolic compounds (TPC) and antioxidant activity from fresh dark fig (Ficus carica L.) have been investigated using response surface methodology (RSM). The Box-Behnken design was used to investigate the effects of three independent variables, acetone concentration (40-80%), temperature (25-65 °C), and time (60-120 min), on the response. Regression analys...

Artificial neural networks (ANN) and response surface methodology (RSM) were used to build a model to describe the effects of four independent variables (moisture content, concentrations of glucose, ammonium nitrate and methionine) on the yield of cephalosporin C (CPC) from Acremonium chrysogenum under solid state fermentation. The respective uses of RSM and ANN were found to be effective in lo...