Corrosion Inhibition of Brass by Biochemical Process in Circulating Cooling Water System

Controlling the corrosion rate of metal materials is one of the key issues in circulating cooling water treatment. In recent years, the treatment of circulating cooling water by microorganisms has become a research hotspot. Compared with the traditional chemical treatment, microbial treatment is an environmentally friendly technology. In this paper, the effects of ammonia nitrogen concentration, microbial dosage and aeration intensity on copper corrosion rate were studied. In order to analyze the experimental data more comprehensively, a full factor experimental design was used to investigate the effects of ammonia nitrogen concentration, microbial dosage and aeration intensity on copper corrosion. The corrosion rate of copper was less than the national standard (< 0.005 mm / a), in which ammonia nitrogen concentration and aeration intensity were significant factors (P < 0.05), and the interaction between ammonia nitrogen concentration and aeration intensity was also significant (P < 0.05), After optimization, the regression rate of the model increased from 85.02% to 92.41%.

Detection Method of Environmentally Friendly Non-POP PBDEs by Derivatization-Enhanced Raman Spectroscopy Using the Pharmacophore Model

Background: Polybrominated diphenyl ethers (PBDEs) are dangerous for the environment and human health because of their persistent organic pollutant (POP) characteristics, which have attracted extensive research attention. Raman spectroscopy is a simple highly sensitive detection operation. This study was performed to obtain environmentally friendly non-POP PBDE derivatives with simple detection-based molecular design and provide theoretical support for establishing enhanced Raman spectroscopic detection techniques. Methods: A three-dimensional quantitative structure-activity relationship (3DQSAR) pharmacophore model of characteristic PBDE Raman spectral was established using 20 and 10 PBDEs as training and test sets, respectively. Full-factor experimental design was used to modify representative commercial PBDEs, and their flame retardancy and POP characteristics were evaluated. Results: The pharmacophore model (Hypo1) exhibited good predictive ability with the largest correlation coefficient (R2) of 0.88, the smallest root mean square (RMS) value of 0.231, and total cost of 81.488 with a configuration value of 12.56 (˂17).74 monosubstituted and disubstituted PBDE derivatives were obtained based on the Hypo 1 pharmacophore model and full-factor experimental design auxiliary. Twenty PBDE derivatives were screened, and their flame-retardant capabilities were enhanced and their migration and bio-concentration were reduced (log(KOW) <5), with unchanged toxicity and high biodegradability. The Raman spectral intensities increased up to 380%. In addition, interference analysis of the Raman peaks by group frequency indicated that the 20 PBDE derivatives were easily detected with no interference in gaseous environments. Conclusion: Nine pharmacophore models were constructed in this study; Hypo 1 was the most accurate. Twenty PBDE derivatives showed Raman spectral intensities increased up to 380%; these were classified as new non-POP environmentally friendly flame retardants with low toxicity, low migration, good biodegradability, and low bio-concentrations. 2D QSAR analysis showed that the most positive Milliken charge and lowest occupied orbital energy were the main contributors to the PBDE Raman spectral intensities. Raman peak analysis revealed no interference between the derivatives in gaseous environments.

The Study of the Optimal Parameter Settings in a Hospital Supply Chain System in Taiwan

This study proposed the optimal parameter settings for the hospital supply chain system (HSCS) when either the total system cost (TSC) or patient safety level (PSL) (or both simultaneously) was considered as the measure of the HSCS’s performance. Four parameters were considered in the HSCS: safety stock, maximum inventory level, transportation capacity, and the reliability of the HSCS. A full-factor experimental design was used to simulate an HSCS for the purpose of collecting data. The response surface method (RSM) was used to construct the regression model, and a genetic algorithm (GA) was applied to obtain the optimal parameter settings for the HSCS. The results show that the best method of obtaining the optimal parameter settings for the HSCS is the simultaneous consideration of both the TSC and the PSL to measure performance. Also, the results of sensitivity analysis based on the optimal parameter settings were used to derive adjustable strategies for the decision-makers.