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In this study, an artificial neural network (ANN) model was applied to predict the thermo-hydraulic performance of the offset-strip fin heat exchanger. The ANN model was used for predicting the thermo-hydraulic performance to improve the prediction accuracy and extend the applicable range compared to the results of using the empirical equations reported in previous studies. The main parameters of these empirical equations were fin height (0.134 < α < 0.997), fin length (0.012 < δ < 0.048), fin width (0.041 < γ < 0.121), and Reynolds number (120 < Re <104). In addition, the Fanning friction factor f and the Colburn factor j were considered as the outputs of the ANN model. The impact of the parameters on the thermo-hydraulic performance of the heat exchanger was quantitatively evaluated, and the prediction accuracy was improved over a wide range for the thermo-hydraulic performance generated by the ANN model. Thus, the results obtained using the ANN model agreed well with the experimental data over a wider range than possible for the previous empirical correlations, showing extremely high accuracy and validity of the ANN model in comparison to the empirical equations. [ABSTRACT FROM AUTHOR]

This study aims to improve the hydrolysis and degradation of primary sludge by using wild-type enzymes (protease and lipase) and establishing the optimal enzymatic cocktail ratio. Primary sludge from three wastewater treatment plants (WWTPs) in Korea (Ulsan, Pohang, and Busan) were subjected to enzymatic hydrolysis. Protease and lipase were isolated from enzyme-producing microorganisms cultured from secondary sludge collected at 8 different digester sites in Korea. Primary sludge degradation through enzymatic hydrolysis was monitored by measuring the reduction in the volatile suspended solids (VSS) content of the sludge and enzyme cocktail mix for 72 h at 40oC and pH 7.0. The enzymatic cocktail of Ulsan primary sludge treated with protease to lipase at a ratio of 1:3 was found to be optimal at 33.3% VSS reduction. Biochemical methane potential (BMP) tests were employed to the optimal enzyme cocktail to measure the potential of the hydrolyzed substrate for further degradation (VSS reduction) and bioconversion to biogas using 125 mL serum bottles as anaerobic reactors for 30 days. BMP tests showed that there was an increase in biogas production by 84.1%, methane production by 89.8%, and methane yield by 9.6%. Methane production rate was also increased. The significant VSS concentration reduction and higher biogas and methane yield of the enzyme-treated primary sludge correlate to the fact that the complex polymeric organic materials were degraded leading to efficient utilization by the microorganisms in the anaerobic digestion process. [ABSTRACT FROM AUTHOR]

The use of precision medicine for chemotherapy requires the individualization of the therapeutic regimen for each patient. This approach improves treatment efficacy and reduces the probability of administering ineffective drugs. To ensure accurate decision-making in a timely manner, anticancer drug efficacy tests must be performed within a short timeframe using a small number of cancer cells. These requirements can be satisfied via microfluidics-based drug screening platforms, which are composed of complex fluidic channels and closed systems. Owing to their complexity, skilled manipulation is required. In this study, we developed a microfluidic platform, to accurately perform multiple drug efficacy tests using a small number of cells, which can be conducted via simple manipulation. As it is a small, open-chamber system, a minimal number of cells could be loaded through simple pipetting. Furthermore, the extracellular matrix gel inside the chamber provides an in vivo-like environment that enables the localized delivery of the drugs to spontaneously diffuse from the channels underneath the chamber without a pump, thereby efficiently and robustly testing the efficacy and resistance of multiple drugs. We demonstrated that this platform enabled the rapid and facile testing of multiple drugs using a small number of cells (~ 10,000) over a short period of time (~ 2 days). These results provide the possibility of using this powerful platform for selecting therapeutic medication, developing new drugs, and delivering personalized medicine to patients. [ABSTRACT FROM AUTHOR]