One of the major steps toward the industrialization of the microbial product(s) is to optimize the cultivation conditions at the large-scale bioreactor and successfully control the microbial behavior within a large scale production environment. Statistical Design of Experiment was proven to optimize a vast number of microbial processes to achieve robustness and explore possible interactions among the variables. In this research, Taguchi Orthogonal Array was applied to optimize the cultivation condition of a newly isolated Prodigiosin-producing marine bacterial strain, Serratia AM8887, at the bioreactor level. The two-steps fermentation process was applied; as the productivity was scaled up from shake flask level to a benchtop bioreactor (5L) and subsequently to an in-situ sterilization bioreactor system (20L); yielding a 7g/L of the pigment; compared to 100mg/L before optimization confirming that; applying Taguchi experimental design is a reliable and good positive option for the optimization of biotechnological processes. The produced pigment was purified and the chemical structure was revealed using Spectrophotometric, Mass Spectrum (MS), Fourier Transform InfraRed (FT-IR), and proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy analysis. The biological activity including antibacterial (against multidrug resistance), antioxidants (against 1, 1-diphenyl-2-picrilhydrazyl) and cytotoxicity to cancer cell lines (against breast cancer MCF-7 and liver cancer HepG-2) of the pigment were explored showing very characteristic features that could be helpful in food, pharmaceuticals, and/or textile industries.

The utilization of modified magnetite nanoparticles (Fe3O4 NPs) with a cationic surfactant (cetyltrimethylammonium bromide (CTAB)) as an efficient adsorbent was successfully carried out to remove reactive black 5 (RBBA), reactive red 198 (RRR) and reactive blue 21 (RTB) dyes from aqueous solutions. First, a reactor was designed to be simple, repeatable and efficient in its synthesis of Fe3O4 NPs via co-precipitation method. Then, an orthogonal array design (OAD), four factor-four level (44) matrix was applied to assign affecting factors on removing of the dyes from aqueous solutions. The obtained results from ANOVA showed that the amount of CTAB and NaCl% significantly affect the adsorption of RBBA, RRR and RTB dyes. The sorption kinetics of the dyes were best described by a second-order kinetic model, suggesting chemisorptions mechanism. Also, dye adsorption equilibrium state data were fitted well to the Langmuir isotherm rather than Freundlich isotherm. Also, the maximum monolayer capacity, qmax, obtained from the Langmuir was 312.5, 163.9 and 556.2 mg g-1 for RBBA, RRR and RTB, respectively. The obtained results in the present study indicated that the CTAB-coated Fe3O4 NPs can be an efficient adsorbent material for removal of reactive dyes form aqueous solutions.

Face milling is an important and common machining operation because of its versatility and capability to produce various surfaces. Face milling is a machining process of removing material by the relative motion between a work piece and rotating cutter with multiple cutting edges. It is an interrupted cutting operation in which the teeth of the milling cutter enter and exit the work piece during each revolution. This paper is concerned with the experimental and numerical study of face milling of AISI1045. The proposed approach is based on statistical analysis on the experimental data gathered using Taguchi design matrix. Surface roughness is the most important performance characteristics of the face milling process. In this study the effect of input face milling process parameters on surface roughness of AISI1045 steel milled parts have been studied. The input parameters are cutting speed (v), feed rate (fz) and depth of cut (ap). The experimental data are gathered using Taguchi L9 design matrix. In order to establish the relations between the input and the output parameters, various regression functions have been fitted on the data based on output characteristics. The significance of the process parameters on the quality characteristics of the process was also evaluated quantitatively using the analysis of variance method. Then, statistical analysis and validation experiments have been carried out to compare and select the best and most fitted models. In the last section of this research, mathematical model has been developed for surface roughness prediction using particle swarm optimization (PSO) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. It has been found that the predicted roughness using PSO is in good agreement with the actual surface roughness.

Energy management system in a microgrid with uncertainties in load, renewable generation and market price is critical for stable operation of the microgrid. Scenario-based robust energy management exploiting upper and lower bounds is used to deal with the uncertainties. Taguchi’ s orthogonal array method is used to reduce the large number of scenarios, considering all the possible combinations of max and min values of loads and renewable generations. In this study, uncertainty of market price is handled by robust optimization method, and worst case scenario with the maximum total cost is defined as the output result. Furthermore, demand response program is also considered for the flexible loads, which help the microgrid to operate robustly with a lower cost, in the presence of the uncertainties. In this study, two cases with typical microgrids are considered to evaluate the effectiveness of the proposed method, and GAMS tool is used for implementation of simulations. Additionally, Monte Carlo simulation is applied for verifying the effectiveness of the method.