Rational design of hybrid sensor arrays combined synergistically with machine learning for rapid response to a hazardous gas leak environment in chemical plants.

Combinations of semiconductor metal oxide (SMO) sensors, electrochemical (EC) sensors, and photoionization detection (PID) sensors were used to discriminate chemical hazards on the basis of machine learning. Sensing data inputs were exploited in the form of either numerical or image data formats, and the classification of chemical hazards with high accuracy was achieved in both cases. Even a small amount of gas sensing or purging data (input for ∼30 s) input can be exploited in machine-learning-based gas discrimination. SMO sensors exhibit high performance even in a single-sensor mode, presumably because of the intrinsic cross-sensitivity of metal oxides, which is otherwise considered a major disadvantage of SMO sensors. EC sensors were enhanced through synergistic integration of sensor combinations with machine learning. For precision detection of multiple target analytes, a minimum number of sensors can be proposed for gas detection/discrimination by combining sensors with dissimilar operating principles. The Type I hybrid sensor combines one SMO sensor, one EC sensor, and one PID sensor and is used to identify NH3 gas mixed with sulfur compounds in simulations of NH3 gas leak accidents in chemical plants. The portable remote sensing module made with a Type I hybrid sensor and LTE module can identify mixed NH3 gas with a detection time of 60 s, demonstrating the potential of the proposed system to quickly respond to hazardous gas leak accidents and prevent additional damage to the environment.

Isolation and characterization of Pseudomonas otitidis WL-13 and its capacity to decolorize triphenylmethane dyes.

Pseudomonas otitidis WL-13, which has a high capacity to decolorize triphenylmethane dyes, was isolated from activated sludge obtained from a wastewater treatment plant of a dyeing industry. This strain exhibited a remarkable color-removal capability when tested against several triphenylmethane dyes under both shaking and static conditions at high concentrations of dyes. More than 95% of Malachite Green and Brilliant Green was removed within 12 h at 500 micromol/L dye concentration under shaking conditions. Crystal Violet lost about 13% of its color under the same conditions tested. The rate of decolorization increased when the M9 medium was supplemented with yeast extract. The optimum pH and temperature for color removal were 7-9 and 35-40 degrees C, respectively. The observed changes in the visible spectra and the inspection of bacterial growth indicated the color-removal by the adsorption of dye to the cells during incubation with strains.

Decolorization of textile plant effluent by Citrobacter sp. strain KCTC 18061P.

Citrobacter sp. strain KCTC 18061P was found to be able to decolorize textile plant effluent containing different types of reactive dyes. Effects of physico-chemical parameters, such as aeration, nitrogen source, glucose and effluent concentrations on the color removal of real dye effluent by this strain were investigated. The observed changes in the visible spectra indicated color removal by the absorption of dye to cells during incubation with the strain. This strain showed higher decolorization ability under aerobic than static culture conditions. With 1% glucose, this strain removed 70% of effluent color within 5 days. Decolorization was not significantly dependent on the nitrogen sources tested. Chemical oxygen demand (COD) and biological oxygen demand (BOD) were decreased in proportion to incubation times, and their removal rates were about 35% and 50%, respectively, at 7 days of culture.