The dissolved air at the bottom layer of the deep aeration tank transforms into fine gas bubbles within the MLSS (Mixed Liquor Suspended Solid) floc when exposed to the atmosphere. MLSS floc flotation occurs when MLSS from the deep aeration tank enters the secondary clarifier for solid-liquid separation, as dissolved air becomes fine air within the MLSS floc. The floated MLSS floc causes a high SS (Suspended Solid) concentration in the secondary effluent. The fine air bubbles within the MLSS floc must be removed to achieve stable sedimentation in the secondary clarifier. Fine bubbles within the MLSS floc can be removed by air sparging. The settleability of MLSS was measured by sludge volume indexes (SVIs) after air sparging MLSS taken at the end of the deep aeration tank. MLSS settling tests were performed at MLSS heights of 200, 300, 400, and 500 mm, and compressed air was fed at the bottom of the settling column with air flow rates of 100, 300, and 500 ml/min at each MLSS height, respectively. Also, at each height and air flow rate, air was sparged for 3, 5, and 7 minutes, respectively. SVI was determined for each height, air flow rate, and sparging time, respectively. Experimental results showed that a 300 mm MLSS height, 300 ml/min air flow rate, and 3 minutes of sparging time were the least conditions to achieve less than 120 ml/g of SVI, which was the criterion for good MLSS settling in the secondary clarifier.

In order to determine the location of average concentration and distribution status of dissolved oxygen in the rectangular aeration tank of the sewage treatment plant was analyzed and the difference of dissolved oxygen concentration was remarkable at each location. Compared with the computational fluid dynamics analysis, it was found that the results were consistent with the measurement results by showing the difference of dissolved oxygen concentration between the locations. Based on the measured data, the representative location of dissolved oxygen in aeration tank was selected by using statistical analysis method and the representative location was expressed in three-dimensional coordinates(LWH : 25%, 50%, 33%) from flow direction and left wall. Also the difference between the dissolved oxygen concentration at the actual measurement location and the average concentration value of the entire aeration tank was founded, and the equations for calibrating the automatic measurement data considering the actual measurement location were calculated.

Activated sludge sewage treatment processes are difficult to be controlled because of their complex and nonlinear behaviour, however, the control of the dissolved oxygen level in the reactors plays an important role in the operation of the facility. For this reason, this study is designed to present a system which accurately measures DO, MLSS, pH and ORP in the aeration tank to alleviate situations above and provide the automatization of a sewage treatment plant (STP) using new DO control system. The automatic control systems must be guaranteed the accuracy. Therefore, the proposed automatic DO control system in this study could be commercial applications in the aeration tanks by means of operating cost analysis and user-friendly for operation and maintenance. We could get accurate data from the lab tank which has water quality checker because there was no vortex and air bubble during the measurement process. Improvement of confidence in the lab tank enabled effective and automatic operation of sewage treatment plants so that operation costs and manpower could be saved. If this result is put in place in every sewage treatment plant nationwide for practical purposes, it is estimated to cost 18.5 million dollars in installing the lab tank and to save 9.8 million dollars in management cost a year, except for cost saved by automation.

Fuzzy algorithm of automatic control for dissolved oxygen(DO) concentration in the aeration tank of an activated sludge process is proposed. Among variables repirometry and air flowrate are selected as significant input factors and the relationship with DO is estimated using a multiple regression model. The DO concentration and the amount of repirometry are fuzzified and the fuzzy rule base are determined. Using the fuzzy algorithm, the change of amount of air flowrate are determined and the change of amount of DO is derived.