Heterogeneous electro-Fenton treatment of chemotherapeutic drug busulfan using magnetic nanocomposites as catalyst.

The rapid and efficient mineralization of the chemotherapeutic drug busulfan (BSF) as the target pollutant has been investigated for the first time by three different heterogeneous EF systems that were constructed to ensure the continuous electro-generation of H2O2 and •OH consisting of: i) a multifunctional carbon felt (CF) based cathode composed of reduced graphene oxide (rGO), iron oxide nanoparticles and carbon black (CB) (rGO-Fe3O4/CB@CF), ii) rGO modified cathode (rGO/CB@CF) and rGO supported Fe3O4 (rGO-Fe3O4) catalyst and iii) rGO modified cathode (rGO/CB@CF) and multi walled carbon nanotube supported Fe3O4 (MWCNT-Fe3O4) catalyst. The effects of main variables, including the catalyst amount, applied current and initial pH were investigated. Based on the results, H2O2 was produced by oxygen reduction reaction (ORR) on the liquid-solid interface of both fabricated cathodes. •OH was generated by the reaction of H2O2 with the active site of ≡FeII on the surface of the multifunctional cathode and heterogeneous EF catalysts. Utilizing carbon materials with high conductivity, the redox cycling between ≡FeII and ≡FeIII was effectively facilitated and therefore promoted the performance of the process. The results demonstrated almost complete mineralization of BSF through the heterogeneous systems over a wide applicable pH range. According to the reusability and stability tests, multifunctional cathode exhibited outstanding performance after five consecutive cycles which is promising for the efficient mineralization of refractory organic pollutants. Moreover, intermediates products of BSF oxidation were identified and a plausible oxidation pathway was proposed. Therefore, this study demonstrates efficient and stable cathodes and catalysts for the efficient treatment of an anticancer active substance.

Self-tuning GMV control of glucose concentration in fed-batch baker's yeast production.

A detailed system identification procedure and self-tuning generalized minimum variance (STGMV) control of glucose concentration during the aerobic fed-batch yeast growth were realized. In order to determine the best values of the forgetting factor (λ), initial value of the covariance matrix (α), and order of the Auto-Regressive Moving Average with eXogenous (ARMAX) model (n a, n b), transient response data obtained from the real process wereutilized. Glucose flow rate was adjusted according to the STGMV control algorithm coded in Visual Basic in an online computer connected to the system. Conventional PID algorithm was also implemented for the control of the glucose concentration in aerobic fed-batch yeast cultivation. Controller performances were examined by evaluating the integrals of squared errors (ISEs) at constant and random set point profiles. Also, batch cultivation was performed, and microorganism concentration at the end of the batch run was compared with the fed-batch cultivation case. From the system identification step, the best parameter estimation was accomplished with the values λ = 0.9, α = 1,000 and n a = 3, n b = 2. Theoretical control studies show that the STGMV control system was successful at both constant and random glucose concentration set profiles. In addition, random effects given to the set point, STGMV control algorithm were performed successfully in experimental study.