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An integrated theory is developed to describe the steadystate operation of a suspended-growth bioscrubber for the control of biodegradable, volatile organic gases. The bioscrubber consists of an N-stage absorber and an oxidation reactor. A biomass slurry is circulated between the absorber and the oxidation reactor, the pollutant is absorbed and partially oxidized in the absorber. Oxidation is completed in the oxidation reactor. Predictions of the theory show that the removal efficiency is a function of Henry's Law constant for the pollutant, the ratio of the liquid flow rate to the gas flow rate, and the number of stages. Since high efficiencies can be achieved for soluble, biodegradable, volatile organic compounds, such systems have the potential to be a low-cost control method.Wet scrubbers can be used to remove water soluble gases including some volatile organic compounds. If the gas is not very soluble in water, its rate of absorption can be enhanced by either neutralization or oxidation. Neutralization is used for acid and alkaline gases. Chemical oxidation with sodium hypochlorite, hydrogen peroxide, or other oxidizing agents has been used for various organic compounds, particularly odorant gases. Biochemical oxidation by microorganisms is a potential alternative to chemical oxidation for biodegradable pollutants. It is attractive for toxic compounds because biochemical oxidation usually results in mineralization, destroying the toxic nature of the pollutant. This paper presents a theory for a biologically enhanced scrubber to remove biodegradable volatile organic compounds from the gas phase. In the scrubber, suspendedgrowth biomass metabolizes the pollutant. This lowers the liquid-phase concentration, allowing continuous operation with minimal requirements for both makeup water and water discharged from the system. ImplicationsTheoretical predictions show that suspended-growth bioscrubbers have potential for high removal efficiency of soluble, biodegradable, volatile organic compounds. In addition, biological oxidation usually mineralizes the compound and destroys the toxic nature of the pollutant without producing toxic byproducts. Such a system has the potential to be a lower cost method than conventional air pollution control technologies.

Typhoon intensity forecast is an important issue. The objective of this study is to construct a 5-day 12-hourly typhoon intensity forecast model based on the adaptive neuro-fuzzy inference systems (ANFIS) to improve the typhoon intensity forecast in the Northwest Pacific. It analyzed the improvement of the ANFIS typhoon intensity forecast model by comparing it with the MLR model when only the atmospheric factor or both atmospheric and oceanic factors are considered. This study collected the SHIPS (Statistical Hurricane Intensity Prediction Scheme) developmental data of typhoons in the Northwest Pacific before landing from 2000 to 2012. The input factors of the ANFIS model were simplified by the stepwise regression procedure (SRP). Subtractive clustering (SC) was used to determine the number of ANFIS rules and to reduce model complexity. Model Index (MI) was taken as the clustering standard of SC to determine the network architecture of the ANFIS typhoon intensity forecast model. The simulated results show that the MI could effectively determine the radius of influence of SC. The typhoon intensity forecast was significantly improved after oceanic environmental factors were added. The improvement of RMSE of ANFIS was the highest at 84 h; the improvement of ANFIS on the underestimated ratio was primarily positive. The Typhoon Songda case study shows that the maximum bias of ANFIS is greatly improved, at 60 h of the lead time, and the improvement percentage of maximum bias is the highest (39%). Overall, the ANFIS model could effectively improve the MLR model in typhoon intensity forecast.

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